---
title: Machine Learning Development
description: Home  /  Services  /  Machine Learning App Development  Available Now · 30+ Production Models  Machine Learning App  Development  Company  Predictive Models · M
url: https://miracuves.com/service/ml-development
date_modified: 2026-07-14
author: miracuves
language: en_US
---

@import url('https://fonts.googleapis.com/css2?family=Montserrat:ital,wght@0,300;0,400;0,500;0,600;0,700;0,800;0,900&family=JetBrains+Mono:wght@400;500;600&display=swap');

  /* Redesign variables - Corporate brand dark & red glass accents */
  .ta {
    --red: #a70d2a;
    /* Corporate primary red */
    --red-d: #990000;
    /* Corporate alternate red */
    --red-l: #a70d2a;
    /* Light brand accent */
    --ra: rgba(167, 13, 42, 0.06);
    /* Transparent brand primary */
    --rb: rgba(167, 13, 42, 0.16);
    --rc-glow: rgba(167, 13, 42, 0.12);
    --rg: linear-gradient(135deg, #a70d2a 0%, #990000 100%);
    /* Strict primary-alternate red gradient */
    --green: #00E676;
    /* Vibrant Emerald Green */
    --gl: #26A69A;
    /* Beautiful Clean Teal */
    --teal: #00F3FF;
    /* High-fidelity Neon Cyan */

    /* High-end obsidian dark scales */
    --dk0: #030008;
    /* Obsidian deep background */
    --dk1: #07040f;
    /* Sleek card background */
    --dk2: #0c081a;
    /* Elevated container dark */
    --dk3: #120e26;
    /* Saturated card base */
    --dk4: #191433;
    /* Modern highlight dark */

    /* Elevated glassmorphism */
    --card: rgba(8, 4, 15, 0.76);
    /* Frosted premium glass */
    --cardh: rgba(14, 8, 25, 0.88);
    /* Hover glass brightness */
    --l0: #ffffff;
    --l1: #fafafd;
    --l2: #f2f1f6;
    --l3: #e8e7ee;

    /* Text and borders */
    --dx1: #ffffff;
    --dx2: rgba(255, 255, 255, 0.95);
    --dx3: rgba(255, 255, 255, 0.65);
    --dx4: rgba(255, 255, 255, 0.35);
    --lx1: #0c0716;
    --lx2: #322d3e;
    --lx3: #5f5a70;
    --lx4: #928ea3;

    /* High fidelity grid borders */
    --bd: rgba(255, 255, 255, 0.06);
    --bd2: rgba(255, 255, 255, 0.12);
    --bd3: rgba(255, 255, 255, 0.20);
    --bl: rgba(12, 7, 22, 0.04);
    --bl2: rgba(12, 7, 22, 0.08);

    /* Animations */
    --f: 'Montserrat', -apple-system, sans-serif;
    --fm: 'JetBrains Mono', monospace;
    --r20: 20px;
    --r14: 14px;
    --r10: 10px;
    --r6: 6px;
    --r100: 100px;
    --spring: cubic-bezier(0.175, 0.885, 0.32, 1.275);
    /* Fluid elastic bounce */
    --ease: cubic-bezier(0.16, 1, 0.3, 1);
  }

  .ta,
  .ta * {
    box-sizing: border-box !important;
  }

  .ta {
    font-family: var(--f) !important;
    -webkit-font-smoothing: antialiased;
    font-size: 15px;
    line-height: 1.7;
    color: var(--lx2);
    width: 100%;
    overflow-x: hidden;
    background: var(--l0);
  }

  .ta h1,
  .ta h2,
  .ta h3,
  .ta h4,
  .ta p,
  .ta ul,
  .ta ol {
    margin: 0;
    padding: 0;
  }

  .ta ul,
  .ta ol {
    list-style: none;
  }

  .ta a {
    text-decoration: none !important;
    color: inherit;
  }

  .ta img {
    max-width: 100%;
    display: block;
  }

  /* Layout & Grid systems */
  .ta .W {
    max-width: 1260px;
    margin: 0 auto;
    padding: 0 40px;
  }

  .ta .S {
    padding: 120px 0;
    position: relative;
  }

  .ta .SM {
    padding: 75px 0;
    position: relative;
  }

  .ta .g2 {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 32px;
  }

  .ta .g3 {
    display: grid;
    grid-template-columns: repeat(3, 1fr);
    gap: 32px;
  }

  .ta .g4 {
    display: grid;
    grid-template-columns: repeat(4, 1fr);
    gap: 24px;
  }

  .ta .g5 {
    display: grid;
    grid-template-columns: repeat(5, 1fr);
    gap: 0;
  }

  .ta .sp5050 {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 88px;
    align-items: start;
  }

  .ta .sp5050a {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 96px;
    align-items: start;
  }

  .ta .hi>*,
  .ta .sp5050>*,
  .ta .sp5050a>*,
  .ta .two-col>* {
    min-width: 0;
  }

  /* Skins and Ambient backgrounds */
  .ta .sD {
    background: var(--dk1);
  }

  .ta .sD2 {
    background: var(--dk2);
  }

  .ta .sW {
    background: var(--l0);
    position: relative;
  }

  .ta .sG {
    background: var(--l1);
    position: relative;
  }

  .ta .sR {
    background: var(--red-d);
    position: relative;
  }

  .ta .sD h2,
  .ta .sD h3,
  .ta .sD h4,
  .ta .sD2 h2,
  .ta .sD2 h3,
  .ta .sD2 h4 {
    color: var(--dx1);
  }

  .ta .sD p,
  .ta .sD2 p {
    color: var(--dx2);
  }

  .ta .sD .mu,
  .ta .sD2 .mu {
    color: var(--dx3);
  }

  .ta .sW h2,
  .ta .sW h3,
  .ta .sW h4,
  .ta .sG h2,
  .ta .sG h3,
  .ta .sG h4 {
    color: var(--lx1);
  }

  .ta .sW p,
  .ta .sG p {
    color: var(--lx2);
  }

  .ta .sW .mu,
  .ta .sG .mu {
    color: var(--lx3);
  }

  .ta .sR h2,
  .ta .sR h3,
  .ta .sR h4 {
    color: #fff;
  }

  .ta .sR p {
    color: rgba(255, 255, 255, 0.9);
  }

  /* Typography system */
  .ta h1 {
    font-size: clamp(52px, 7vw, 92px);
    font-weight: 800;
    line-height: 0.98;
    letter-spacing: -3.8px;
  }

  .ta h2 {
    font-size: clamp(34px, 4.2vw, 54px);
    font-weight: 800;
    line-height: 1.06;
    letter-spacing: -2px;
  }

  .ta h2 em {
    font-style: normal;
    color: var(--red);
  }

  .ta .sR h2 em {
    color: #a70d2a;
  }

  .ta h3 {
    font-size: 21px;
    font-weight: 800;
    line-height: 1.25;
    letter-spacing: -0.6px;
  }

  .ta .lead {
    font-size: 18px;
    line-height: 1.76;
    font-weight: 400;
    max-width: 640px;
  }

  .ta .body {
    font-size: 15px;
    line-height: 1.76;
    font-weight: 400;
  }

  /* Micro-animations */
  @keyframes ta-fade-up {
    from {
      opacity: 0;
      transform: translateY(32px);
    }

    to {
      opacity: 1;
      transform: translateY(0);
    }
  }

  @keyframes ta-pulse {
    0% {
      transform: scale(0.6);
      opacity: 0.7;
    }

    100% {
      transform: scale(2.2);
      opacity: 0;
    }
  }

  @keyframes ta-float {

    0%,
    100% {
      transform: translateY(0) rotate(-2deg);
    }

    50% {
      transform: translateY(-8px) rotate(-1deg);
    }
  }

  @keyframes ta-float2 {

    0%,
    100% {
      transform: translateY(0) rotate(2deg);
    }

    50% {
      transform: translateY(-7px) rotate(1deg);
    }
  }

  @keyframes ta-draw {
    0% {
      stroke-dashoffset: 240;
    }

    100% {
      stroke-dashoffset: 0;
    }
  }

  @keyframes ta-orbit1 {
    from {
      transform: rotate(0deg) translateX(36px) rotate(0deg);
    }

    to {
      transform: rotate(360deg) translateX(36px) rotate(-360deg);
    }
  }

  @keyframes ta-orbit2 {
    from {
      transform: rotate(120deg) translateX(36px) rotate(-120deg);
    }

    to {
      transform: rotate(480deg) translateX(36px) rotate(-480deg);
    }
  }

  @keyframes ta-orbit3 {
    from {
      transform: rotate(240deg) translateX(36px) rotate(-240deg);
    }

    to {
      transform: rotate(600deg) translateX(36px) rotate(-600deg);
    }
  }

  @keyframes ta-core {

    0%,
    100% {
      transform: scale(1);
      box-shadow: 0 0 20px rgba(167, 13, 42, 0.4);
    }

    50% {
      transform: scale(1.08);
      box-shadow: 0 0 35px rgba(167, 13, 42, 0.85);
    }
  }

  @keyframes ta-dotglow {

    0%,
    100% {
      box-shadow: 0 0 0 0 rgba(74, 222, 128, 0.6);
    }

    50% {
      box-shadow: 0 0 0 8px rgba(74, 222, 128, 0);
    }
  }

  @keyframes ta-glow-pulse {

    0%,
    100% {
      opacity: 1;
      transform: scale(1);
    }

    50% {
      opacity: 0.8;
      transform: scale(1.05);
    }
  }

  @keyframes ta-shimmer {
    0% {
      transform: translateX(-100%);
    }

    100% {
      transform: translateX(100%);
    }
  }

  @keyframes drift {
    0% {
      transform: translate(0, 0) scale(1);
    }

    50% {
      transform: translate(4%, 5%) scale(1.08);
    }

    100% {
      transform: translate(-2%, -3%) scale(0.95);
    }
  }

  /* Ambient background light blobs */
  .ta .glow-blob {
    position: absolute;
    border-radius: 50%;
    filter: blur(130px);
    opacity: 0.15;
    pointer-events: none;
    z-index: 0;
  }

  .ta .glow-1 {
    top: -10%;
    right: -5%;
    width: 550px;
    height: 550px;
    background: radial-gradient(circle, var(--red) 0%, transparent 70%);
  }

  .ta .glow-2 {
    bottom: 10%;
    left: -10%;
    width: 450px;
    height: 450px;
    background: radial-gradient(circle, var(--red-d) 0%, transparent 70%);
  }

  .ta .glow-3 {
    top: 35%;
    right: 25%;
    width: 400px;
    height: 400px;
    background: radial-gradient(circle, var(--red-l) 0%, transparent 70%);
  }

  /* Scroll reveals */
  .ta .rev {
    opacity: 0;
    transform: translateY(32px);
    transition: opacity 0.8s var(--ease), transform 0.8s var(--ease);
  }

  .ta .rev.in {
    opacity: 1;
    transform: translateY(0);
  }

  .ta .revl {
    opacity: 0;
    transform: translateX(-32px);
    transition: opacity 0.8s var(--ease), transform 0.8s var(--ease);
  }

  .ta .revl.in {
    opacity: 1;
    transform: translateX(0);
  }

  .ta .revr {
    opacity: 0;
    transform: translateX(32px);
    transition: opacity 0.8s var(--ease), transform 0.8s var(--ease);
  }

  .ta .revr.in {
    opacity: 1;
    transform: translateX(0);
  }

  .ta .stag>* {
    opacity: 0;
    transform: translateY(24px);
    transition: opacity 0.65s var(--ease), transform 0.65s var(--ease);
  }

  .ta .stag.in>* {
    opacity: 1;
    transform: none;
    transition-delay: 0.35s;
  }

  .ta .stag.in>*:nth-child(1) {
    transition-delay: 0.04s;
  }

  .ta .stag.in>*:nth-child(2) {
    transition-delay: 0.08s;
  }

  .ta .stag.in>*:nth-child(3) {
    transition-delay: 0.12s;
  }

  .ta .stag.in>*:nth-child(4) {
    transition-delay: 0.16s;
  }

  .ta .stag.in>*:nth-child(5) {
    transition-delay: 0.20s;
  }

  .ta .stag.in>*:nth-child(6) {
    transition-delay: 0.24s;
  }

  .ta .stag.in>*:nth-child(7) {
    transition-delay: 0.28s;
  }

  .ta .stag.in>*:nth-child(8) {
    transition-delay: 0.32s;
  }

  .ta .stag.in>*:nth-child(9) {
    transition-delay: 0.35s;
  }

  .ta .stag.in>*:nth-child(10) {
    transition-delay: 0.38s;
  }

  .ta .stag.in>*:nth-child(11) {
    transition-delay: 0.41s;
  }

  .ta .stag.in>*:nth-child(12) {
    transition-delay: 0.44s;
  }

  .ta .stag.in>*:nth-child(13) {
    transition-delay: 0.47s;
  }

  .ta .stag.in>*:nth-child(14) {
    transition-delay: 0.50s;
  }

  .ta .stag.in>*:nth-child(15) {
    transition-delay: 0.53s;
  }

  .ta .stag.in>*:nth-child(16) {
    transition-delay: 0.56s;
  }

  /* Eyebrow Pill Badge */
  .ta .pill {
    display: flex;
    margin-bottom: 30px;
  }

  .ta .pill.c {
    justify-content: center;
  }

  .ta .eyp {
    display: inline-flex;
    align-items: center;
    gap: 10px;
    background: rgba(12, 7, 23, 0.85);
    backdrop-filter: blur(20px);
    -webkit-backdrop-filter: blur(20px);
    border: 1px solid var(--bd2);
    border-radius: var(--r100);
    padding: 8px 18px 8px 12px;
    font-size: 12.5px;
    font-weight: 600;
    color: rgba(255, 255, 255, 0.9);
    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.4);
  }

  .ta .sW .eyp,
  .ta .sG .eyp {
    background: rgba(255, 255, 255, 0.85);
    border-color: var(--bl2);
    color: var(--lx2);
    box-shadow: 0 4px 16px rgba(12, 7, 22, 0.04);
  }

  .ta .sR .eyp {
    background: rgba(255, 255, 255, 0.12);
    border-color: rgba(255, 255, 255, 0.22);
    color: #fff;
  }

  .ta .eyp strong {
    color: #fff !important;
    font-weight: 700;
  }

  .ta .sW .eyp strong,
  .ta .sG .eyp strong {
    color: var(--red) !important;
  }

  .ta .eyp .el {
    width: 18px;
    height: 2px;
    background: var(--red);
    border-radius: 2px;
  }

  .ta .sR .eyp .el {
    background: rgba(255, 255, 255, 0.75);
  }

  /* Live Pulsing Dot */
  .ta .ldot {
    position: relative;
    width: 8px;
    height: 8px;
    background: var(--gl);
    border-radius: 50%;
    box-shadow: 0 0 12px rgba(74, 222, 128, 0.8);
    flex-shrink: 0;
    animation: ta-dotglow 2s ease-in-out infinite;
  }

  .ta .ldot::after {
    content: '';
    position: absolute;
    inset: -5px;
    border-radius: 50%;
    border: 1.5px solid var(--gl);
    opacity: 0.55;
    animation: ta-pulse 2.2s ease-out infinite;
  }

  /* Buttons */
  .ta .btn {
    position: relative;
    display: inline-flex;
    align-items: center;
    gap: 10px;
    font-family: var(--f);
    font-size: 14.5px;
    font-weight: 700;
    letter-spacing: 0.015em;
    padding: 16px 30px;
    border-radius: var(--r14);
    cursor: pointer;
    transition: transform 0.3s var(--spring), box-shadow 0.3s var(--ease), background 0.3s var(--ease);
    line-height: 1;
    white-space: nowrap;
    overflow: hidden;
    border: none;
    text-decoration: none !important;
  }

  .ta .btn svg {
    width: 18px;
    height: 18px;
    stroke-width: 2.4;
    position: relative;
    z-index: 1;
    flex-shrink: 0;
  }

  .ta .btn span {
    position: relative;
    z-index: 1;
  }

  .ta .btn-p {
    background: var(--rg);
    color: #fff !important;
    box-shadow: 0 12px 30px rgba(167, 13, 42, 0.35), inset 0 1px 0 rgba(255, 255, 255, 0.20);
  }

  .ta .btn-p::before {
    content: '';
    position: absolute;
    inset: 0;
    background: linear-gradient(110deg, transparent 25%, rgba(255, 255, 255, 0.25) 50%, transparent 75%);
    transform: translateX(-100%);
    transition: transform 0.65s var(--ease);
  }

  .ta .btn-p:hover {
    transform: translateY(-3px) scale(1.02);
    box-shadow: 0 18px 40px rgba(167, 13, 42, 0.5);
  }

  .ta .btn-p:hover::before {
    transform: translateX(100%);
  }

  .ta .btn-g {
    background: rgba(12, 7, 23, 0.6);
    color: #fff !important;
    border: 1px solid var(--bd2);
    backdrop-filter: blur(20px);
    -webkit-backdrop-filter: blur(20px);
  }

  .ta .btn-g:hover {
    background: rgba(12, 7, 23, 0.85);
    transform: translateY(-3px);
    border-color: var(--bd3);
    box-shadow: 0 12px 32px rgba(0, 0, 0, 0.45);
  }

  .ta .sW .btn-g,
  .ta .sG .btn-g {
    background: rgba(255, 255, 255, 0.85);
    color: var(--lx1) !important;
    border-color: var(--bl2);
    backdrop-filter: none;
    box-shadow: 0 2px 10px rgba(12, 7, 22, 0.04);
  }

  .ta .sW .btn-g:hover,
  .ta .sG .btn-g:hover {
    background: #fff;
    transform: translateY(-3px);
    box-shadow: 0 10px 24px rgba(12, 7, 22, 0.08);
    border-color: var(--bl2);
  }

  .ta .sR .btn-g {
    background: rgba(255, 255, 255, 0.16);
    color: #fff !important;
    border-color: rgba(255, 255, 255, 0.26);
  }

  .ta .sR .btn-g:hover {
    background: rgba(255, 255, 255, 0.26);
  }

  .ta .btn-wa {
    background: #25D366;
    color: #fff !important;
    box-shadow: 0 10px 24px rgba(37, 211, 102, 0.25);
    border: none;
  }

  .ta .btn-wa:hover {
    background: #20ba5a;
    transform: translateY(-3px);
    box-shadow: 0 12px 30px rgba(37, 211, 102, 0.4);
  }

  .ta .btn-wa svg {
    color: #fff;
  }

  /* Checklist Elements */
  .ta .ck {
    display: flex;
    gap: 14px;
    font-size: 15px;
    font-weight: 500;
    line-height: 1.62;
    padding: 14px 0;
    align-items: flex-start;
  }

  .ta .ck::before {
    content: '✓';
    font-weight: 900;
    font-size: 12px;
    margin-top: 3px;
    flex-shrink: 0;
    display: inline-flex;
    align-items: center;
    justify-content: center;
    width: 20px;
    height: 20px;
    border-radius: 50%;
  }

  .ta .sD .ck::before,
  .ta .sD2 .ck::before {
    color: var(--gl);
    background: rgba(74, 222, 128, 0.08);
  }

  .ta .sD .ck,
  .ta .sD2 .ck {
    color: var(--dx2);
  }

  .ta .sW .ck::before,
  .ta .sG .ck::before {
    color: var(--green);
    background: rgba(34, 197, 94, 0.08);
  }

  .ta .sW .ck,
  .ta .sG .ck {
    color: var(--lx2);
  }

  .ta .sR .ck::before {
    color: #ffd700;
    background: rgba(255, 215, 0, 0.12);
  }

  .ta .sR .ck {
    color: rgba(255, 255, 255, 0.88);
  }

  /* Structural Badges & Icons */
  .ta .nb {
    width: 38px;
    height: 38px;
    border-radius: 50%;
    display: flex;
    align-items: center;
    justify-content: center;
    font-size: 13.5px;
    font-weight: 800;
    flex-shrink: 0;
  }

  .ta .sD .nb,
  .ta .sD2 .nb {
    background: var(--ra);
    color: var(--red-l);
    border: 1px solid var(--rb);
  }

  .ta .sW .nb,
  .ta .sG .nb {
    background: var(--red);
    color: #fff;
  }

  .ta .sR .nb {
    background: rgba(255, 255, 255, 0.18);
    color: #fff;
    border: 1px solid rgba(255, 255, 255, 0.1);
  }

  .ta .ico {
    width: 44px;
    height: 44px;
    border-radius: var(--r10);
    display: flex;
    align-items: center;
    justify-content: center;
    border: 1px solid;
    background: var(--ra);
    border-color: var(--rb);
    flex-shrink: 0;
    transition: transform 0.3s var(--spring);
  }

  .ta .ico svg {
    width: 20px;
    height: 20px;
    color: var(--red-l);
    transition: color 0.3s;
  }

  .ta .titem:hover .ico {
    transform: scale(1.08) rotate(3deg);
    background: rgba(167, 13, 42, 0.12);
    border-color: rgba(167, 13, 42, 0.4);
  }

  .ta .sR .ico {
    background: rgba(255, 255, 255, 0.14);
    border-color: rgba(255, 255, 255, 0.24);
  }

  .ta .sR .ico svg {
    color: #fff;
  }

  /* Dynamic Header alignment */
  .ta .sh {
    display: flex;
    justify-content: space-between;
    align-items: flex-end;
    gap: 28px;
    flex-wrap: wrap;
    margin-bottom: 56px;
  }

  .ta .ta-bc {
    display: flex;
    align-items: center;
    gap: 8px;
    font-size: 12.5px;
    font-weight: 600;
    margin-bottom: 30px;
    flex-wrap: wrap;
    text-transform: uppercase;
    letter-spacing: 0.05em;
  }

  .ta .ta-bc a {
    color: var(--dx3);
    transition: color 0.2s;
    text-decoration: none;
  }

  .ta .ta-bc a:hover {
    color: var(--red-l);
  }

  .ta .ta-bc .bs {
    color: var(--dx4);
    opacity: 0.6;
    font-size: 10px;
    margin: 0 4px;
  }

  .ta .ta-bc .ta-bc-curr {
    color: var(--dx1);
    font-weight: 700;
  }

  /* ══ S01 HERO SECTION REDESIGN ══ */
  .ta .hero {
    background:
      radial-gradient(circle at 15% 15%, rgba(167, 13, 42, 0.25) 0%, transparent 55%),
      radial-gradient(circle at 85% 75%, rgba(255, 42, 95, 0.18) 0%, transparent 60%),
      radial-gradient(circle at 50% -10%, rgba(0, 243, 255, 0.08) 0%, transparent 45%),
      #04010a;
    position: relative;
    padding: 120px 0 20px;
    overflow: hidden;
  }

  .ta .hero::before {
    content: '';
    position: absolute;
    inset: 0;
    z-index: 0;
    background-image: linear-gradient(rgba(167, 13, 42, 0.03) 1px, transparent 1px), linear-gradient(90deg, rgba(167, 13, 42, 0.03) 1px, transparent 1px);
    background-size: 56px 56px;
    pointer-events: none;
  }

  /* Animated & Vibrant Hero Ambient Glows */
  .ta .hero .glow-blob {
    opacity: 0.38;
    filter: blur(140px);
    transition: opacity 0.5s ease;
  }

  .ta .hero .glow-1 {
    background: radial-gradient(circle, #ff2a5f 0%, transparent 70%);
    animation: drift 16s ease-in-out infinite alternate;
  }

  .ta .hero .glow-2 {
    background: radial-gradient(circle, var(--red) 0%, transparent 70%);
    animation: drift 20s ease-in-out infinite alternate-reverse 2s;
  }

  .ta .hero .glow-3 {
    background: radial-gradient(circle, var(--teal) 0%, transparent 70%);
    opacity: 0.22;
    animation: drift 24s ease-in-out infinite alternate 1s;
  }

  .ta .hi {
    display: grid;
    grid-template-columns: 1.05fr 0.95fr;
    gap: 72px;
    align-items: start;
    position: relative;
    z-index: 1;
  }

  .ta .hl {
    padding-bottom: 80px;
  }

  .ta .hew {
    display: flex;
    margin-bottom: 28px;
    animation: ta-fade-up 0.6s var(--ease) both;
  }

  .ta .he {
    display: inline-flex;
    align-items: center;
    gap: 10px;
    background: rgba(12, 7, 23, 0.85);
    backdrop-filter: blur(24px);
    border: 1px solid var(--bd2);
    border-radius: var(--r100);
    padding: 8px 18px 8px 12px;
    font-size: 12.5px;
    font-weight: 600;
    color: rgba(255, 255, 255, 0.9);
    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.4);
  }

  /* Gradient H1 Hero typography */
  .ta .hh1 {
    font-size: clamp(54px, 7.5vw, 94px);
    font-weight: 800;
    line-height: 0.96;
    letter-spacing: -0.045em;
    color: var(--dx1);
    animation: ta-fade-up 0.7s var(--ease) 0.1s both;
  }

  .ta .hh1 em {
    font-style: normal;
    color: #ff3355 !important;
    -webkit-text-fill-color: #ff3355 !important;
    background: none !important;
    -webkit-background-clip: initial !important;
    background-clip: initial !important;
    padding: 0 !important;
    border-radius: 0 !important;
    box-shadow: none !important;
    display: inline !important;
    letter-spacing: -0.045em;
    text-shadow: 0 0 20px rgba(255, 51, 85, 0.25);
  }

  .ta .hh1 .h1s {
    display: block;
  }

  .ta .hh1 .dim {
    color: rgba(255, 255, 255, 0.85);
    font-weight: 700;
    font-size: 0.68em;
    display: block;
    margin-top: 14px;
    letter-spacing: -0.035em;
    -webkit-text-fill-color: rgba(255, 255, 255, 0.85);
  }

  .ta .hdim {
    color: #ffffff;
    font-weight: 700;
    font-size: 26px;
    display: block;
    margin-top: 14px;
    letter-spacing: -0.01em;
    -webkit-text-fill-color: #ffffff;
  }

  .ta .hlead {
    font-size: 18.5px;
    line-height: 1.76;
    color: var(--dx2);
    margin-top: 24px;
    max-width: 540px;
    animation: ta-fade-up 0.7s var(--ease) 0.18s both;
    font-weight: 400;
  }

  .ta .htrust {
    display: flex;
    flex-wrap: wrap;
    gap: 10px;
    margin: 30px 0;
    animation: ta-fade-up 0.7s var(--ease) 0.24s both;
  }

  .ta .htr {
    display: inline-flex;
    align-items: center;
    gap: 8px;
    font-size: 12.5px;
    font-weight: 600;
    background: rgba(255, 255, 255, 0.08);
    border: 1px solid rgba(255, 255, 255, 0.16);
    color: #ffffff;
    padding: 8px 16px;
    border-radius: var(--r100);
    transition: all 0.3s var(--ease);
  }

  .ta .htr:hover {
    background: rgba(255, 255, 255, 0.14);
    border-color: rgba(255, 255, 255, 0.26);
    transform: translateY(-2px);
    box-shadow: 0 4px 12px rgba(167, 13, 42, 0.15);
  }

  .ta .htr svg {
    width: 13px;
    height: 13px;
    color: var(--green);
    flex-shrink: 0;
  }

  .ta .hctas {
    display: flex;
    gap: 16px;
    flex-wrap: wrap;
    animation: ta-fade-up 0.7s var(--ease) 0.3s both;
  }

  .ta .hproof {
    display: flex;
    gap: 16px;
    align-items: center;
    margin-top: 28px;
    flex-wrap: wrap;
    font-size: 12.5px;
    font-weight: 600;
    animation: ta-fade-up 0.7s var(--ease) 0.36s both;
    color: rgba(255, 255, 255, 0.85);
  }

  .ta .hproof a {
    color: #ffffff;
    text-decoration: underline !important;
    display: inline-flex;
    align-items: center;
    gap: 6px;
    transition: opacity 0.2s;
    font-weight: 700;
  }

  .ta .hproof a:hover {
    opacity: 0.8;
  }

  .ta .hproof .dot {
    color: rgba(255, 255, 255, 0.35);
  }

  .ta .hr {
    padding-top: 8px;
    animation: ta-fade-up 0.7s var(--ease) 0.2s both;
  }

  /* SScard with double borders and inset highlights */
  .ta .hsc {
    position: relative;
    border-radius: var(--r20);
    overflow: hidden;
    box-shadow: 0 30px 70px rgba(0, 0, 0, 0.65);
    transition: transform 0.4s var(--spring), box-shadow 0.4s var(--ease);
  }

  .ta .hsc::before {
    content: '';
    position: absolute;
    inset: 0;
    padding: 1.5px;
    background: linear-gradient(135deg, rgba(167, 13, 42, 0.6), rgba(255, 255, 255, 0.05), rgba(153, 0, 0, 0.5));
    border-radius: var(--r20);
    -webkit-mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
    mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
    -webkit-mask-composite: xor;
    mask-composite: exclude;
    pointer-events: none;
    z-index: 2;
  }

  .ta .hsc:hover {
    transform: translateY(-6px) scale(1.01);
    box-shadow: 0 35px 80px rgba(167, 13, 42, 0.15), 0 30px 70px rgba(0, 0, 0, 0.7);
  }

  .ta .hsc-inner {
    background: rgba(6, 3, 12, 0.88);
    backdrop-filter: blur(40px) saturate(180%);
    -webkit-backdrop-filter: blur(40px) saturate(180%);
    border-radius: calc(var(--r20) - 1px);
    position: relative;
    z-index: 1;
  }

  .ta .hsch {
    padding: 24px 28px 18px;
    border-bottom: 1px solid var(--bd);
    display: flex;
    align-items: center;
    justify-content: space-between;
  }

  .ta .hscl {
    font-size: 11px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.15em;
    color: var(--red-l);
  }

  .ta .hscv {
    font-size: 11.5px;
    color: var(--dx3);
    font-weight: 650;
  }

  .ta .hscg {
    display: grid;
    grid-template-columns: 1fr 1fr;
  }

  .ta .hsci {
    padding: 24px 28px;
    border-right: 1px solid var(--bd);
    border-bottom: 1px solid var(--bd);
    transition: background 0.3s var(--ease);
  }

  .ta .hsci:hover {
    background: rgba(167, 13, 42, 0.03);
  }

  .ta .hsci:nth-child(2n) {
    border-right: none;
  }

  .ta .hsci:nth-child(3),
  .ta .hsci:nth-child(4) {
    border-bottom: none;
  }

  .ta .hscn {
    font-size: 34px;
    font-weight: 800;
    color: #ffffff !important;
    -webkit-text-fill-color: #ffffff !important;
    line-height: 1.1;
    letter-spacing: -0.04em;
    text-shadow: 0 4px 12px rgba(167, 13, 42, 0.4);
  }

  .ta .hscll {
    font-size: 11px;
    font-weight: 600;
    text-transform: uppercase;
    letter-spacing: 0.07em;
    color: var(--dx3);
    margin-top: 6px;
  }

  .ta .hscav {
    padding: 16px 28px;
    display: flex;
    align-items: center;
    gap: 12px;
    border-top: 1px solid var(--bd);
    font-size: 13.5px;
    font-weight: 600;
    color: var(--green);
    background: rgba(0, 230, 118, 0.05);
  }

  /* Clone card slider grid */
  .ta .hshow {
    margin-top: 18px;
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 12px;
  }

  .ta .hsc-card {
    display: block;
    text-decoration: none !important;
    background: rgba(255, 255, 255, 0.02);
    border: 1px solid var(--bd);
    border-radius: var(--r14);
    padding: 16px 20px;
    transition: all 0.35s var(--ease);
    position: relative;
    overflow: hidden;
    backdrop-filter: blur(8px);
    -webkit-backdrop-filter: blur(8px);
  }

  .ta .hsc-card::before {
    content: '';
    position: absolute;
    left: 0;
    top: 0;
    bottom: 0;
    width: 4px;
    background: var(--rg);
    opacity: 0;
    transition: opacity 0.25s, transform 0.25s;
  }

  .ta .hsc-card:hover {
    border-color: rgba(167, 13, 42, 0.4);
    background: rgba(255, 255, 255, 0.05);
    transform: translateX(6px);
    box-shadow: 0 12px 30px rgba(167, 13, 42, 0.08), 0 8px 24px rgba(0, 0, 0, 0.35);
  }

  .ta .hsc-card:hover::before {
    opacity: 1;
  }

  .ta .hsc-cat {
    font-size: 9.5px;
    font-weight: 900;
    text-transform: uppercase;
    letter-spacing: 0.12em;
    color: #ffffff !important;
    margin-bottom: 5px;
  }

  .ta .hsc-name {
    font-size: 14px;
    font-weight: 800;
    color: var(--dx1);
    line-height: 1.35;
    letter-spacing: -0.01em;
  }

  .ta .hsc-meta {
    font-size: 11px;
    color: rgba(255, 255, 255, 0.8) !important;
    margin-top: 4px;
    font-weight: 600;
  }

  /* Interactive Machine Learning Engine Status Console */
  .ta .hstatus-console {
    background: rgba(255, 255, 255, 0.02);
    border: 1px solid var(--bd);
    border-radius: var(--r14);
    margin-top: 14px;
    padding: 18px 22px;
    backdrop-filter: blur(8px);
    -webkit-backdrop-filter: blur(8px);
    transition: all 0.35s var(--ease);
  }

  .ta .hstatus-console:hover {
    border-color: rgba(167, 13, 42, 0.3);
    background: rgba(255, 255, 255, 0.04);
    transform: translateY(-3px);
    box-shadow: 0 16px 36px rgba(0, 0, 0, 0.4);
  }

  .ta .hstatus-header {
    display: flex;
    align-items: center;
    gap: 8px;
    font-size: 11px;
    text-transform: uppercase;
    letter-spacing: 0.1em;
    color: var(--dx3);
    margin-bottom: 14px;
    font-weight: 800;
  }

  .ta .hstatus-dot {
    width: 7px;
    height: 7px;
    background: var(--green);
    border-radius: 50%;
    box-shadow: 0 0 8px var(--green);
    animation: ta-dotglow 2s ease-in-out infinite;
  }

  .ta .hstatus-tag {
    margin-left: auto;
    color: var(--red-l);
    font-weight: 850;
    font-size: 10px;
    border: 1px solid rgba(167, 13, 42, 0.2);
    padding: 2px 8px;
    border-radius: 4px;
    background: rgba(167, 13, 42, 0.05);
  }

  .ta .hstatus-grid {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 10px;
  }

  .ta .hstatus-item {
    background: rgba(255, 255, 255, 0.01);
    border: 1px solid rgba(255, 255, 255, 0.03);
    padding: 10px 14px;
    border-radius: var(--r10);
    transition: border-color 0.25s;
  }

  .ta .hstatus-item:hover {
    border-color: rgba(255, 255, 255, 0.08);
  }

  .ta .hstatus-lbl {
    display: block;
    font-size: 9px;
    font-weight: 850;
    color: var(--dx4);
    letter-spacing: 0.08em;
    margin-bottom: 2px;
  }

  .ta .hstatus-val {
    display: block;
    font-size: 12.5px;
    font-weight: 750;
    color: var(--dx1);
  }

  /* ══ S01 STRIP REDESIGN ══ */
  .ta .hstrip {
    background: var(--l0);
    border-top: none;
    padding: 34px 0;
    position: relative;
    z-index: 1;
    box-shadow: 0 -1px 0 rgba(167, 13, 42, 0.08), 0 12px 40px rgba(12, 7, 22, 0.06);
  }

  .ta .hsr {
    display: flex;
    align-items: center;
    justify-content: space-between;
    flex-wrap: nowrap;
    gap: 16px;
  }

  .ta .hsi {
    flex-shrink: 0;
  }

  .ta .hsi strong {
    display: block;
    font-size: 20px;
    font-weight: 800;
    color: var(--red-d);
    line-height: 1.1;
    letter-spacing: -0.03em;
    background: linear-gradient(135deg, var(--red-d) 0%, var(--red) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .hsi span {
    display: block;
    font-size: 10.2px;
    font-weight: 700;
    color: var(--lx3);
    text-transform: uppercase;
    letter-spacing: 0.08em;
    margin-top: 4px;
  }

  .ta .hsep {
    width: 1px;
    height: 36px;
    background: var(--bl2);
    flex-shrink: 0;
  }

  /* ══ S02 TRUST BAR REDESIGN ══ */
  .ta .tbar {
    padding: 48px 0;
    border-bottom: 1px solid var(--bd);
    position: relative;
    z-index: 1;
  }

  .ta .tbrow {
    display: grid;
    grid-template-columns: repeat(3, 1fr);
    gap: 32px 48px;
    justify-items: center;
  }

  .ta .titem {
    display: flex;
    align-items: center;
    gap: 14px;
  }

  .ta .titem h4 {
    font-size: 14.5px;
    font-weight: 800;
    color: var(--dx1);
    letter-spacing: -0.01em;
  }

  .ta .titem p {
    font-size: 12.5px;
    color: var(--dx2);
    margin-top: 4px;
    font-weight: 500;
  }

  .ta .tsep {
    display: none !important;
  }

  /* ══ S03 OUR APPROACH REDESIGN ══ */
  .ta .approach-right {
    display: flex;
    flex-direction: column;
    gap: 24px;
  }

  .ta .approach-cards {
    display: grid;
    grid-template-columns: 1fr 1fr 1fr;
    gap: 16px;
    margin-top: 4px;
  }

  .ta .ac {
    border: 1px solid transparent;
    border-radius: var(--r14);
    padding: 22px 16px;
    background-image: linear-gradient(var(--l0), var(--l0)), linear-gradient(135deg, rgba(12, 7, 22, 0.06), rgba(167, 13, 42, 0.15));
    background-origin: border-box;
    background-clip: padding-box, border-box;
    transition: all 0.35s var(--spring);
    text-align: center;
    box-shadow: 0 4px 16px rgba(12, 7, 22, 0.02);
  }

  .ta .ac:hover {
    transform: translateY(-5px);
    box-shadow: 0 16px 36px rgba(167, 13, 42, 0.1);
    background-image: linear-gradient(var(--l0), var(--l0)), linear-gradient(135deg, rgba(167, 13, 42, 0.4), rgba(167, 13, 42, 0.08));
  }

  .ta .ac-icon {
    width: 42px;
    height: 42px;
    border-radius: var(--r10);
    background: var(--ra);
    border: 1px solid var(--rb);
    display: flex;
    align-items: center;
    justify-content: center;
    margin: 0 auto 12px;
    transition: transform 0.3s var(--spring);
  }

  .ta .ac:hover .ac-icon {
    transform: scale(1.1) rotate(3deg);
    background: rgba(167, 13, 42, 0.1);
  }

  .ta .ac-icon svg {
    width: 20px;
    height: 20px;
    color: var(--red);
  }

  .ta .ac-title {
    font-size: 14px;
    font-weight: 800;
    color: var(--lx1);
    margin-bottom: 6px;
    letter-spacing: -0.01em;
  }

  .ta .ac-desc {
    font-size: 11.5px;
    color: var(--lx3);
    line-height: 1.5;
    font-weight: 500;
  }

  /* Symmetrical stat card elements */
  .ta .sgrid {
    display: grid;
    grid-template-columns: 1fr 1fr;
    gap: 20px;
  }

  .ta .scard {
    border: 1px solid transparent;
    border-radius: var(--r14);
    padding: 28px 24px;
    transition: all 0.35s var(--spring);
    position: relative;
    overflow: hidden;
    background-image: linear-gradient(var(--l0), var(--l0)), linear-gradient(135deg, rgba(12, 7, 22, 0.04), rgba(167, 13, 42, 0.08));
    background-origin: border-box;
    background-clip: padding-box, border-box;
    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.02);
  }

  .ta .scard::before {
    content: '';
    position: absolute;
    inset: 0;
    background: linear-gradient(135deg, rgba(167, 13, 42, 0.04), transparent);
    opacity: 0;
    transition: opacity 0.3s;
  }

  .ta .scard:hover {
    transform: translateY(-6px);
    box-shadow: 0 20px 45px rgba(167, 13, 42, 0.08), 0 8px 24px rgba(12, 7, 22, 0.04);
    background-image: linear-gradient(var(--l0), var(--l0)), linear-gradient(135deg, rgba(167, 13, 42, 0.35), rgba(167, 13, 42, 0.05));
  }

  .ta .scard:hover::before {
    opacity: 1;
  }

  .ta .sn {
    font-size: 38px;
    font-weight: 800;
    line-height: 1.1;
    margin-bottom: 6px;
    letter-spacing: -0.04em;
    background: var(--rg);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .sl {
    font-size: 13px;
    font-weight: 600;
    line-height: 1.5;
    color: var(--lx3);
  }

  .ta .anote {
    background: rgba(167, 13, 42, 0.02);
    border: 1px solid rgba(167, 13, 42, 0.12);
    border-left: 5px solid var(--red);
    border-radius: 0 var(--r14) var(--r14) 0;
    padding: 20px 24px;
    margin-top: 30px;
    box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.7);
  }

  .ta .anh {
    font-size: 11px;
    font-weight: 850;
    color: var(--red);
    text-transform: uppercase;
    letter-spacing: 0.12em;
    margin-bottom: 8px;
  }

  .ta .mrow {
    display: flex;
    align-items: center;
    gap: 12px;
    padding: 18px 0;
    border-top: 1px solid var(--bl2);
    margin-top: 30px;
    font-size: 13.5px;
    color: var(--lx3);
    font-weight: 650;
  }

  .ta .mrow svg {
    width: 16px;
    height: 16px;
    color: var(--red);
    flex-shrink: 0;
  }

  .ta .mrow a {
    color: var(--red-d);
    font-weight: 700;
    transition: color 0.2s;
  }

  .ta .mrow a:hover {
    color: var(--red);
  }

  .ta .tech-adv {
    background: linear-gradient(135deg, rgba(167, 13, 42, 0.04) 0%, rgba(167, 13, 42, 0.01) 100%);
    border: 1px solid rgba(167, 13, 42, 0.12);
    border-radius: var(--r14);
    padding: 26px 28px;
    margin-top: 4px;
    box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.7);
  }

  .ta .tech-adv h4 {
    font-size: 14px;
    font-weight: 850;
    color: var(--lx1);
    margin-bottom: 16px;
    text-transform: uppercase;
    letter-spacing: 0.08em;
  }

  .ta .fadv-row {
    display: flex;
    align-items: center;
    justify-content: space-between;
    padding: 10px 0;
    border-bottom: 1px solid var(--bl2);
  }

  .ta .fadv-row:last-child {
    border-bottom: none;
    padding-bottom: 0;
  }

  .ta .fadv-label {
    font-size: 13.5px;
    color: var(--lx2);
    font-weight: 550;
  }

  .ta .fadv-val {
    font-size: 13.5px;
    font-weight: 800;
    color: var(--red);
  }

  /* ══ S04 CLONE SOLUTIONS REDESIGN ══ */
  .ta .scrd {
    display: block;
    text-decoration: none !important;
    background: var(--l0);
    border: 1px solid var(--bl2);
    border-radius: var(--r20);
    overflow: hidden;
    transition: all 0.4s var(--spring);
    box-shadow: 0 4px 16px rgba(12, 7, 22, 0.02);
  }

  .ta .scrd:hover {
    transform: translateY(-8px);
    border-color: rgba(167, 13, 42, 0.35);
    box-shadow: 0 24px 50px rgba(167, 13, 42, 0.1), 0 8px 24px rgba(12, 7, 22, 0.06);
  }

  .ta .sv {
    height: 130px;
    position: relative;
    overflow: hidden;
    border-bottom: 1px solid var(--bl2);
    display: flex;
    align-items: center;
    justify-content: center;
  }

  .ta .sv1 {
    background: linear-gradient(135deg, rgba(167, 13, 42, 0.12), rgba(153, 0, 0, 0.03));
  }

  .ta .sv2 {
    background: linear-gradient(135deg, rgba(0, 243, 255, 0.12), rgba(0, 243, 255, 0.03));
  }

  .ta .sv3 {
    background: linear-gradient(135deg, rgba(153, 0, 0, 0.12), rgba(153, 0, 0, 0.03));
  }

  .ta .sv4 {
    background: linear-gradient(135deg, rgba(0, 230, 118, 0.12), rgba(0, 230, 118, 0.03));
  }

  .ta .sv5 {
    background: linear-gradient(135deg, rgba(157, 0, 255, 0.12), rgba(157, 0, 255, 0.03));
  }

  .ta .sv6 {
    background: linear-gradient(135deg, rgba(167, 13, 42, 0.06), rgba(153, 0, 0, 0.06));
  }

  .ta .spill {
    position: absolute;
    top: 16px;
    right: 16px;
    background: rgba(12, 7, 23, 0.76);
    backdrop-filter: blur(8px);
    border: 1px solid rgba(255, 255, 255, 0.15);
    border-radius: var(--r100);
    padding: 5px 14px;
    font-size: 10px;
    font-weight: 850;
    color: #fff;
    text-transform: uppercase;
    letter-spacing: 0.1em;
  }

  .ta .snum {
    font-size: 60px;
    font-weight: 900;
    letter-spacing: -0.04em;
    line-height: 1;
    opacity: 0.07;
    position: absolute;
    bottom: -12px;
    right: 18px;
    color: var(--lx1);
  }

  .ta .siwrap {
    width: 56px;
    height: 56px;
    border-radius: var(--r14);
    display: flex;
    align-items: center;
    justify-content: center;
    background: rgba(255, 255, 255, 0.75);
    backdrop-filter: blur(10px);
    -webkit-backdrop-filter: blur(10px);
    border: 1px solid rgba(255, 255, 255, 0.65);
    box-shadow: 0 6px 20px rgba(12, 7, 22, 0.06);
    transition: transform 0.35s var(--spring);
  }

  .ta .scrd:hover .siwrap {
    transform: scale(1.1) rotate(4deg);
  }

  .ta .siwrap svg {
    width: 26px;
    height: 26px;
  }

  .ta .sb {
    padding: 26px;
  }

  .ta .scat {
    font-size: 10px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.12em;
    color: var(--red);
    margin-bottom: 12px;
  }

  .ta .scrd h3 {
    font-size: 19px;
    font-weight: 800;
    line-height: 1.3;
    color: var(--lx1);
    letter-spacing: -0.02em;
  }

  .ta .scrd p {
    font-size: 13.5px;
    line-height: 1.68;
    color: var(--lx2);
    margin-top: 10px;
  }

  .ta .schips {
    display: flex;
    gap: 8px;
    margin-top: 16px;
    flex-wrap: wrap;
  }

  .ta .schip {
    font-size: 11px;
    font-weight: 700;
    padding: 5px 12px;
    border-radius: var(--r6);
    background: rgba(167, 13, 42, 0.05);
    color: var(--red);
    border: 1px solid rgba(167, 13, 42, 0.12);
  }

  .ta .hnote {
    margin-top: 26px;
    padding: 18px 24px;
    font-size: 13.8px;
    color: var(--lx2);
    background: rgba(167, 13, 42, 0.03);
    border: 1px solid rgba(167, 13, 42, 0.12);
    border-left: 5px solid var(--red);
    border-radius: 0 var(--r14) var(--r14) 0;
    box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.7);
  }

  /* ══ S05 COMPARISON REDESIGN ══ */
  .ta .ctw {
    overflow-x: auto;
    border-radius: var(--r20);
    border: 1px solid var(--bd);
    margin-top: 40px;
    box-shadow: 0 30px 70px rgba(0, 0, 0, 0.5);
  }

  .ta .ct {
    width: 100%;
    border-collapse: collapse;
    font-size: 13.8px;
  }

  .ta .ct th,
  .ta .ct td {
    padding: 20px 22px;
    text-align: left;
    border: 1px solid;
    vertical-align: middle;
  }

  .ta .ct th {
    font-weight: 850;
    font-size: 11.5px;
    text-transform: uppercase;
    letter-spacing: 0.1em;
    line-height: 1.4;
  }

  .ta .sD .ct th {
    background: rgba(255, 255, 255, 0.03);
    color: var(--dx2);
    border-color: var(--bd);
  }

  .ta .sD .ct td {
    border-color: var(--bd);
    color: var(--dx2);
  }

  .ta .ct .ch {
    border-left: 2px solid rgba(167, 13, 42, 0.4) !important;
    border-right: 2px solid rgba(167, 13, 42, 0.4) !important;
    background: rgba(167, 13, 42, 0.05) !important;
  }

  .ta .ct tr:first-child .ch {
    border-top: 3px solid var(--red) !important;
  }

  .ta .ct tr:last-child .ch {
    border-bottom: 3px solid var(--red) !important;
  }

  .ta .ct .yes {
    color: var(--green) !important;
    font-weight: 800;
    text-shadow: 0 0 10px rgba(0, 230, 118, 0.2);
  }

  .ta .ct .no {
    opacity: 0.5;
  }

  .ta .ct .win {
    color: var(--red-l) !important;
    font-weight: 850;
    text-shadow: 0 0 10px rgba(167, 13, 42, 0.2);
  }

  .ta .dbx {
    padding: 24px 26px;
    border-radius: var(--r14);
    border: 1px solid;
  }

  .ta .sD .dbx {
    background: rgba(255, 255, 255, 0.02);
    border-color: var(--bd);
  }

  .ta .dlbl {
    font-size: 11.5px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.12em;
    margin-bottom: 12px;
  }

  /* ══ S06 ARCHITECTURE REDESIGN ══ */
  .ta .ait {
    padding: 24px 0;
    border-bottom: 1px solid var(--bl2);
  }

  .ta .ait:last-child {
    border-bottom: none;
  }

  .ta .ait h3 {
    font-size: 17px;
    font-weight: 800;
    color: var(--lx1);
    margin-bottom: 18px;
    letter-spacing: -0.02em;
  }
  .ta .ait p.body {
    margin-bottom: 14px;
  }

  .ta .wcall {
    background: rgba(167, 13, 42, 0.02);
    border: 1px solid rgba(167, 13, 42, 0.12);
    border-left: 5px solid var(--red);
    border-radius: 0 var(--r14) var(--r14) 0;
    padding: 20px;
    margin-top: 4px;
    box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.7);
  }

  .ta .wch {
    font-size: 11.5px;
    font-weight: 850;
    color: var(--red);
    text-transform: uppercase;
    letter-spacing: 0.12em;
    margin-bottom: 8px;
  }

  /* High fidelity macOS Window structure */
  .ta .cw2 {
    background: var(--dk0);
    border: 1px solid var(--bd2);
    border-radius: var(--r20);
    overflow: hidden;
    box-shadow: 0 35px 80px rgba(0, 0, 0, 0.7);
    position: relative;
    transition: transform 0.4s var(--spring);
  }

  .ta .cw2:hover {
    transform: translateY(-4px);
    box-shadow: 0 40px 90px rgba(157, 0, 255, 0.12), 0 30px 75px rgba(0, 0, 0, 0.75);
  }

  .ta .cw2::before {
    content: '';
    position: absolute;
    inset: 0;
    padding: 1px;
    background: linear-gradient(135deg, rgba(255, 255, 255, 0.12), transparent, rgba(255, 255, 255, 0.02));
    border-radius: var(--r20);
    -webkit-mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
    mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
    -webkit-mask-composite: xor;
    mask-composite: exclude;
    pointer-events: none;
  }

  .ta .chdr {
    padding: 16px 22px;
    border-bottom: 1px solid var(--bd);
    display: flex;
    align-items: center;
    gap: 8px;
    background: rgba(255, 255, 255, 0.03);
  }

  .ta .cd {
    width: 12px;
    height: 12px;
    border-radius: 50%;
  }

  .ta .cdr {
    background: #ff5f56;
  }

  .ta .cdy {
    background: #ffbd2e;
  }

  .ta .cdg {
    background: #27c93f;
  }

  .ta .ctit {
    font-size: 11.5px;
    font-weight: 600;
    color: var(--dx3);
    margin-left: 10px;
    font-family: var(--fm);
    letter-spacing: 0.04em;
  }

  .ta .cbody {
    padding: 24px 28px;
    font-family: var(--fm);
    font-size: 12.8px;
    line-height: 1.9;
    white-space: pre;
    overflow-x: auto;
    color: rgba(255, 255, 255, 0.95);
    background: #050308;
  }

  .ta .ckw {
    color: #c678dd;
    font-weight: 700;
  }

  .ta .cty {
    color: #e5c07b;
  }

  .ta .cst {
    color: #98c379;
  }

  .ta .ccm {
    color: #5c6370;
    font-style: italic;
  }

  .ta .cnot {
    padding: 18px 24px;
    background: var(--l1);
    border-top: 1px solid var(--bl2);
    font-size: 13.8px;
    color: var(--lx2);
    line-height: 1.7;
    border-radius: 0 0 var(--r20) var(--r20);
    font-weight: 500;
  }

  /* ══ S07 SERVICE MODELS REDESIGN ══ */
  .ta .vcrd {
    background: var(--l0);
    border: 1px solid var(--bl2);
    border-radius: var(--r20);
    overflow: hidden;
    transition: all 0.4s var(--spring);
    position: relative;
    box-shadow: 0 4px 16px rgba(12, 7, 22, 0.02);
  }

  .ta .vcrd:hover {
    transform: translateY(-8px);
    box-shadow: 0 24px 50px rgba(167, 13, 42, 0.08), 0 8px 24px rgba(12, 7, 22, 0.06);
    border-color: rgba(167, 13, 42, 0.3);
  }

  .ta .vbadge {
    position: absolute;
    top: -1px;
    left: 24px;
    background: var(--red);
    color: #fff;
    font-size: 10px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.12em;
    padding: 5px 16px;
    border-radius: 0 0 8px 8px;
    z-index: 5;
    box-shadow: 0 2px 10px rgba(167, 13, 42, 0.25);
  }

  .ta .vvis {
    height: 180px;
    position: relative;
    overflow: hidden;
    border-bottom: 1px solid var(--bl2);
    display: flex;
    align-items: center;
    justify-content: center;
  }

  /* Phone Mockups redesign */
  .ta .vvis-phones {
    position: absolute;
    inset: 0;
    display: flex;
    align-items: center;
    justify-content: center;
    background: linear-gradient(180deg, rgba(167, 13, 42, 0.08), rgba(153, 0, 0, 0.02));
  }

  .ta .phone-mock {
    position: absolute;
    width: 68px;
    height: 110px;
    border-radius: 14px;
    border: 2.2px solid;
    display: flex;
    flex-direction: column;
    overflow: hidden;
    box-shadow: 0 12px 28px rgba(0, 0, 0, 0.45);
  }

  .ta .phone-mock .pm-bar {
    height: 12px;
    flex-shrink: 0;
    border-bottom: 0.5px solid rgba(255, 255, 255, 0.05);
  }

  .ta .phone-mock .pm-body {
    flex: 1;
    display: flex;
    flex-direction: column;
    gap: 4px;
    padding: 8px 6px;
  }

  .ta .phone-mock .pm-line {
    height: 3px;
    border-radius: 2px;
    width: 85%;
  }

  .ta .phone-mock .pm-line:nth-child(2) {
    width: 55%;
  }

  .ta .phone-mock .pm-line:nth-child(3) {
    width: 75%;
  }

  .ta .phone-mock .pm-btn {
    margin-top: auto;
    height: 11px;
    border-radius: 4px;
  }

  .ta .phone-customer {
    transform: rotate(-8deg) translate(-40px, 8px);
    z-index: 1;
    opacity: 0.7;
    border-color: rgba(255, 255, 255, 0.16);
    background: rgba(8, 4, 12, 0.94);
  }

  .ta .phone-customer .pm-bar {
    background: rgba(167, 13, 42, 0.35);
  }

  .ta .phone-customer .pm-line {
    background: rgba(255, 255, 255, 0.12);
  }

  .ta .phone-customer .pm-btn {
    background: rgba(167, 13, 42, 0.3);
  }

  .ta .phone-driver {
    z-index: 3;
    border-color: rgba(167, 13, 42, 0.6);
    background: linear-gradient(160deg, rgba(167, 13, 42, 0.15), rgba(8, 3, 12, 0.94));
    box-shadow: 0 16px 36px rgba(167, 13, 42, 0.25);
    animation: ta-float 4.5s ease-in-out infinite;
  }

  .ta .phone-driver .pm-bar {
    background: rgba(167, 13, 42, 0.65);
  }

  .ta .phone-driver .pm-line {
    background: rgba(255, 255, 255, 0.25);
  }

  .ta .phone-driver .pm-btn {
    background: var(--rg);
  }

  .ta .phone-admin {
    transform: rotate(8deg) translate(40px, -4px);
    z-index: 2;
    opacity: 0.8;
    border-color: rgba(255, 255, 255, 0.2);
    background: rgba(12, 6, 20, 0.94);
    animation: ta-float2 4.5s ease-in-out infinite;
  }

  .ta .phone-admin .pm-bar {
    background: rgba(255, 255, 255, 0.16);
  }

  .ta .phone-admin .pm-line {
    background: rgba(255, 255, 255, 0.12);
  }

  .ta .phone-admin .pm-btn {
    background: rgba(255, 255, 255, 0.16);
  }

  .ta .phone-lbl {
    position: absolute;
    bottom: 8px;
    font-size: 7.5px;
    font-weight: 800;
    text-transform: uppercase;
    letter-spacing: 0.08em;
    color: rgba(255, 255, 255, 0.4);
    text-align: center;
    width: 100%;
  }

  .ta .phone-lbl.driver {
    color: var(--red-l);
    z-index: 4;
    bottom: 4px;
    left: 50%;
    transform: translateX(-50%);
    width: 70px;
    font-weight: 900;
  }

  /* Widget Tree Layout */
  .ta .vvis-tree {
    position: absolute;
    inset: 0;
    display: flex;
    align-items: center;
    justify-content: center;
    background: linear-gradient(180deg, rgba(0, 243, 255, 0.08), rgba(0, 243, 255, 0.02));
  }

  .ta .tree-svg {
    width: 100%;
    height: 100%;
    max-width: 220px;
    max-height: 130px;
  }

  .ta .tree-node {
    fill: rgba(8, 3, 15, 0.92);
    stroke: rgba(0, 243, 255, 0.5);
    stroke-width: 1.2;
    rx: 5;
  }

  .ta .tree-node-root {
    fill: rgba(167, 13, 42, 0.16);
    stroke: rgba(167, 13, 42, 0.65);
    stroke-width: 1.5;
  }

  .ta .tree-line {
    stroke: rgba(0, 243, 255, 0.25);
    stroke-width: 1;
    stroke-dasharray: 4 3;
  }

  .ta .tree-line-root {
    stroke: rgba(167, 13, 42, 0.4);
    stroke-width: 1.2;
    stroke-dasharray: none;
    animation: ta-draw 3s ease-in-out infinite alternate;
  }

  .ta .tree-text {
    fill: rgba(255, 255, 255, 0.75);
    font-size: 6.5px;
    font-family: var(--fm);
    font-weight: 600;
  }

  .ta .tree-text-root {
    fill: var(--red-l);
  }

  /* Sprint Timeline */
  .ta .vvis-sprint {
    position: absolute;
    inset: 0;
    display: flex;
    align-items: center;
    justify-content: center;
    padding: 0 24px;
    background: linear-gradient(180deg, rgba(157, 0, 255, 0.08), rgba(157, 0, 255, 0.02));
  }

  .ta .sprint-track {
    width: 100%;
    display: flex;
    flex-direction: column;
    gap: 10px;
  }

  .ta .sprint-row {
    display: flex;
    align-items: center;
    gap: 10px;
  }

  .ta .sprint-label {
    font-size: 8px;
    font-weight: 850;
    color: rgba(255, 255, 255, 0.4);
    text-transform: uppercase;
    letter-spacing: 0.08em;
    width: 30px;
    flex-shrink: 0;
    text-align: right;
  }

  .ta .sprint-bar-wrap {
    flex: 1;
    height: 10px;
    background: rgba(255, 255, 255, 0.06);
    border-radius: 6px;
    overflow: hidden;
    position: relative;
    border: 0.5px solid rgba(255, 255, 255, 0.02);
  }

  .ta .sprint-fill {
    height: 100%;
    border-radius: 6px;
    position: relative;
  }

  .ta .sprint-fill.done {
    background: linear-gradient(90deg, var(--red), rgba(167, 13, 42, 0.5));
  }

  .ta .sprint-fill.active {
    background: linear-gradient(90deg, rgba(157, 0, 255, 0.8), rgba(157, 0, 255, 0.45));
    width: 75%;
    position: relative;
  }

  .ta .sprint-fill.active::after {
    content: '';
    position: absolute;
    inset: 0;
    background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.3), transparent);
    transform: translateX(-100%);
    animation: ta-shimmer 1.8s infinite;
  }

  .ta .sprint-fill.next {
    background: rgba(255, 255, 255, 0.06);
  }

  .ta .sprint-dot {
    width: 9px;
    height: 9px;
    border-radius: 50%;
    flex-shrink: 0;
  }

  .ta .sprint-dot.done {
    background: var(--green);
  }

  .ta .sprint-dot.active {
    background: var(--red-l);
    animation: ta-dotglow 1.5s ease-in-out infinite;
  }

  .ta .sprint-dot.next {
    background: rgba(255, 255, 255, 0.2);
  }

  .ta .vbody {
    padding: 28px 30px;
  }

  .ta .vtag {
    font-size: 10.5px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.12em;
    color: var(--red);
    margin-bottom: 12px;
  }

  .ta .vtit {
    font-size: 20px;
    font-weight: 800;
    line-height: 1.25;
    color: var(--lx1);
    margin-bottom: 12px;
    letter-spacing: -0.02em;
  }

  .ta .vdesc {
    font-size: 13.8px;
    line-height: 1.68;
    color: var(--lx2);
    margin-bottom: 20px;
  }

  /* ══ S08 QUALITY STANDARDS REDESIGN ══ */
  .ta .two-col {
    display: grid;
    grid-template-columns: 1fr 1fr;
  }

  .ta .tc-panel {
    padding: 130px 90px;
    display: flex;
    align-items: flex-start;
  }

  .ta .tc-inner {
    width: 100%;
    max-width: 560px;
  }

  .ta .two-col .tc-panel:first-child .tc-inner {
    margin-left: auto;
    margin-right: 0;
  }

  .ta .two-col .tc-panel:last-child .tc-inner {
    margin-left: 0;
    margin-right: auto;
  }

  .ta .qs-dark {
    background: var(--dk1);
    position: relative;
  }

  .ta .qs-dark::before {
    content: '';
    position: absolute;
    inset: 0;
    background: radial-gradient(circle at 0% 100%, rgba(167, 13, 42, 0.08) 0%, transparent 68%);
    pointer-events: none;
  }

  .ta .qs-red {
    background: var(--red-d);
    position: relative;
    overflow: hidden;
  }

  .ta .qs-red::before {
    content: '';
    position: absolute;
    inset: 0;
    background: radial-gradient(circle at 100% 0%, rgba(167, 13, 42, 0.35) 0%, transparent 68%);
    pointer-events: none;
  }

  .ta .qs-red .tc-inner {
    position: relative;
    z-index: 1;
  }

  .ta .qs-red h2 em {
    color: #ffffff;
  }

  .ta .qs-dark .qrow {
    display: flex;
    justify-content: space-between;
    align-items: center;
    gap: 12px;
    background: rgba(255, 255, 255, 0.02);
    border: 1px solid rgba(255, 255, 255, 0.06);
    border-radius: var(--r10);
    padding: 16px 22px;
    transition: all 0.3s var(--ease);
  }

  .ta .qs-dark .qrow:hover {
    background: rgba(255, 255, 255, 0.05);
    border-color: rgba(167, 13, 42, 0.35);
    transform: translateX(6px);
    box-shadow: 0 10px 24px rgba(0, 0, 0, 0.25);
  }

  .ta .qs-dark .qn {
    font-size: 13.8px;
    font-weight: 650;
    color: var(--dx1);
  }

  .ta .qs-dark .qb {
    font-size: 10px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.1em;
    background: rgba(255, 255, 255, 0.06);
    border-color: var(--bd2);
    color: var(--dx3);
    flex-shrink: 0;
    white-space: nowrap;
  }

  .ta .qs-dark h2 {
    color: var(--dx1);
  }

  .ta .qs-dark h2 em {
    color: var(--red-l);
  }

  .ta .qs-dark .lead {
    color: var(--dx2);
  }

  .ta .qs-dark .eyp {
    background: rgba(8, 4, 12, 0.75);
    border-color: var(--bd2);
  }

  .ta .qs-dark .eyp strong {
    color: #fff;
  }

  .ta .qs-dark .el {
    background: var(--red);
  }

  .ta .qs-red .tc-inner {
    max-width: 760px;
  }

  .ta .qsteps {
    display: grid;
    grid-template-columns: repeat(2, 1fr);
    gap: 20px;
    width: 100%;
  }

  .ta .vstep {
    background: rgba(255, 255, 255, 0.03);
    border: 1px solid rgba(255, 255, 255, 0.08);
    border-radius: var(--r14);
    padding: 24px;
    display: flex;
    flex-direction: column;
    align-items: flex-start;
    gap: 12px;
    transition: transform 0.35s var(--spring), border-color 0.3s, background 0.3s, box-shadow 0.3s;
  }

  .ta .vstep:hover {
    transform: translateY(-4px);
    border-color: rgba(255, 255, 255, 0.22);
    background: rgba(255, 255, 255, 0.07);
    box-shadow: 0 16px 36px rgba(0, 0, 0, 0.3);
  }

  .ta .vstep-nb {
    width: 30px;
    height: 30px;
    border-radius: 50%;
    background: rgba(255, 255, 255, 0.14);
    border: 1px solid rgba(255, 255, 255, 0.1);
    color: #fff;
    display: flex;
    align-items: center;
    justify-content: center;
    font-size: 11.5px;
    font-weight: 850;
    flex-shrink: 0;
  }

  .ta .vstep h4 {
    font-size: 15px;
    font-weight: 800;
    color: #fff;
    margin: 0;
    line-height: 1.35;
    letter-spacing: -0.01em;
  }

  .ta .vstep p {
    font-size: 12.5px;
    line-height: 1.62;
    color: rgba(255, 255, 255, 0.82);
    margin: 0;
  }

  /* ══ S09 TECH STACK REDESIGN ══ */
  .ta #stack {
    background: linear-gradient(135deg, #cccccc 0%, #b2b2b2 100%) !important;
    color: var(--lx2) !important;
  }

  .ta #stack h2 {
    color: var(--lx1) !important;
  }

  .ta #stack .mu {
    color: var(--lx3) !important;
  }

  .ta #stack .eyp {
    background: rgba(255, 255, 255, 0.75) !important;
    border-color: rgba(0, 0, 0, 0.08) !important;
    color: var(--lx2) !important;
    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.03) !important;
  }

  .ta #stack .eyp strong {
    color: var(--red) !important;
  }

  .ta #stack .el {
    background: var(--red) !important;
  }

  .ta .stack16 {
    display: grid;
    grid-template-columns: repeat(4, 1fr);
    gap: 20px;
  }

  .ta .stki {
    background: rgba(255, 255, 255, 0.6) !important;
    border: 1px solid rgba(0, 0, 0, 0.08) !important;
    border-radius: var(--r14);
    padding: 28px 22px;
    text-align: center;
    transition: all 0.35s var(--ease);
    backdrop-filter: blur(20px) !important;
    -webkit-backdrop-filter: blur(20px) !important;
    position: relative;
    box-shadow: 0 8px 30px rgba(0, 0, 0, 0.03) !important;
  }

  .ta .stki::before {
    content: '';
    position: absolute;
    inset: 0;
    padding: 1px;
    background: linear-gradient(135deg, rgba(255, 255, 255, 0.4), transparent, rgba(0, 0, 0, 0.03));
    border-radius: var(--r14);
    -webkit-mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
    mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
    -webkit-mask-composite: xor;
    mask-composite: exclude;
    pointer-events: none;
  }

  .ta .stki:hover {
    transform: translateY(-6px);
    border-color: var(--red) !important;
    background: rgba(255, 255, 255, 0.9) !important;
    box-shadow: 0 20px 40px rgba(167, 13, 42, 0.12) !important;
  }

  .ta .stlogo {
    width: 50px;
    height: 50px;
    border-radius: var(--r10);
    border: 1px solid rgba(0, 0, 0, 0.1) !important;
    display: flex;
    align-items: center;
    justify-content: center;
    margin: 0 auto 16px;
    font-size: 14px;
    font-weight: 850;
    background: rgba(0, 0, 0, 0.04) !important;
    color: var(--red) !important;
    transition: transform 0.3s var(--spring);
  }

  .ta .stki:hover .stlogo {
    transform: scale(1.1) rotate(4deg);
    background: rgba(0, 0, 0, 0.08) !important;
    border-color: var(--red) !important;
  }

  .ta .stname {
    font-size: 14px;
    font-weight: 800;
    color: var(--lx1) !important;
    margin-bottom: 6px;
    letter-spacing: -0.01em;
  }

  .ta .stdesc {
    font-size: 11px;
    line-height: 1.5;
    color: var(--lx3) !important;
    font-weight: 500;
  }

  /* ══ S10 PROCESS REDESIGN ══ */
  .ta #process {
    background: #000000 !important;
    color: #fff !important;
    position: relative;
    overflow: hidden;
    padding: 120px 0;
  }

  .ta #process h2 {
    color: #fff !important;
  }

  .ta #process h2 em {
    color: var(--red) !important;
    font-style: normal;
  }

  .ta #process p,
  .ta #process .body,
  .ta #process .lead {
    color: rgba(255, 255, 255, 0.82) !important;
  }

  .ta #process .pill .eyp {
    background: rgba(255, 255, 255, 0.08);
    border-color: rgba(255, 255, 255, 0.14);
  }

  .ta #process .pill .eyp strong {
    color: var(--red-l);
  }

  .ta-process-wrapper {
    margin-top: 80px;
    position: relative;
  }

  .ta-process-grid {
    display: grid;
    grid-template-columns: repeat(5, 1fr);
    gap: 20px;
    position: relative;
  }

  .ta-process-step {
    position: relative;
    height: 380px;
    display: flex;
    flex-direction: column;
    justify-content: center;
    align-items: center;
  }

  .ta-process-card {
    width: 200px;
    height: 220px;
    background: rgba(255, 255, 255, 0.02);
    border: 1px solid rgba(255, 255, 255, 0.06);
    border-radius: 18px;
    padding: 24px 18px;
    text-align: center;
    display: flex;
    flex-direction: column;
    align-items: center;
    justify-content: center;
    backdrop-filter: blur(12px);
    -webkit-backdrop-filter: blur(12px);
    box-shadow: 0 15px 35px rgba(0, 0, 0, 0.3);
    transition: all 0.4s var(--ease);
    position: relative;
    z-index: 5;
  }

  .ta-process-card:hover {
    transform: translateY(-8px);
    background: rgba(255, 255, 255, 0.06);
    border-color: rgba(255, 255, 255, 0.18);
    box-shadow: 0 25px 50px rgba(0, 0, 0, 0.45);
  }

  .ta-process-icon-wrap {
    width: 48px;
    height: 48px;
    border-radius: 50%;
    background: rgba(255, 255, 255, 0.08) !important;
    border: 1.5px solid rgba(255, 255, 255, 0.2) !important;
    display: flex;
    align-items: center;
    justify-content: center;
    margin-bottom: 14px;
    transition: all 0.3s var(--ease);
  }

  .ta-process-card:hover .ta-process-icon-wrap {
    transform: scale(1.08);
    background: #fff !important;
    border-color: #fff !important;
  }

  .ta-process-card:hover .ta-process-icon-wrap svg {
    stroke: #000000 !important;
    fill: none;
  }

  .ta-process-icon-wrap svg {
    width: 22px;
    height: 22px;
    stroke: #ffffff !important;
    stroke-width: 2;
    stroke-linecap: round;
    stroke-linejoin: round;
    fill: none;
    transition: all 0.3s var(--ease);
  }

  .ta-process-card h3 {
    font-size: 17.5px !important;
    font-weight: 800 !important;
    color: #ffffff !important;
    margin-bottom: 8px !important;
    letter-spacing: -0.01em !important;
  }

  .ta-process-card p {
    font-size: 12.8px !important;
    line-height: 1.62 !important;
    color: rgba(255, 255, 255, 0.78) !important;
    font-weight: 500 !important;
    margin: 0 !important;
  }

  .ta-step-label {
    position: absolute;
    left: 50%;
    transform: translateX(-50%);
    text-align: center;
    z-index: 10;
    pointer-events: none;
  }

  .ta-step-label.top {
    top: 15px;
  }

  .ta-step-label.bottom {
    bottom: 15px;
  }

  .ta-step-label span {
    display: block;
    font-size: 10px;
    font-weight: 800;
    text-transform: uppercase;
    letter-spacing: 0.12em;
    white-space: nowrap;
  }

  .ta-step-label.top span {
    margin-bottom: 8px;
  }

  .ta-step-label.bottom span {
    margin-top: 8px;
  }

  .ta-step-dot {
    width: 10px;
    height: 10px;
    border-radius: 50%;
    margin: 0 auto;
  }

  .ta-proc-svg {
    position: absolute;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    overflow: visible;
    pointer-events: none;
    z-index: 2;
  }

  .ta .tpills {
    display: flex;
    gap: 16px;
    justify-content: center;
    flex-wrap: wrap;
    margin-top: 56px;
  }

  .ta .tpill {
    padding: 18px 28px;
    border-radius: var(--r14);
    text-align: center;
    background: rgba(255, 255, 255, 0.02);
    border: 1px solid rgba(255, 255, 255, 0.08);
    transition: all 0.35s var(--spring);
    box-shadow: 0 2px 10px rgba(0, 0, 0, 0.01);
  }

  .ta .tpill:hover {
    transform: translateY(-4px);
    box-shadow: 0 16px 36px rgba(0, 0, 0, 0.3);
    border-color: rgba(167, 13, 42, 0.5);
    background: rgba(255, 255, 255, 0.04);
  }

  .ta .tpill strong {
    display: block;
    font-size: 24px;
    font-weight: 800;
    color: #fff;
    letter-spacing: -0.03em;
    line-height: 1.1;
    background: linear-gradient(135deg, #fff 0%, rgba(255, 255, 255, 0.7) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .tpill span {
    display: block;
    font-size: 11.5px;
    color: rgba(255, 255, 255, 0.6);
    text-transform: uppercase;
    letter-spacing: 0.08em;
    margin-top: 6px;
    font-weight: 700;
  }

  @media (max-width: 991px) {
    .ta-process-grid {
      grid-template-columns: 1fr;
      gap: 40px;
      justify-items: center;
    }

    .ta-process-grid::before {
      content: '';
      position: absolute;
      top: 40px;
      bottom: 40px;
      left: 50%;
      transform: translateX(-50%);
      width: 3px;
      background: linear-gradient(to bottom, #a70d2a, #990000, #a70d2a, #990000, #a70d2a);
      z-index: 1;
    }

    .ta-process-step {
      height: auto;
      width: 100%;
      max-width: 320px;
      flex-direction: column;
      align-items: center;
      padding: 0;
    }

    .ta-process-card {
      width: 100%;
      height: auto;
      min-height: 200px;
      padding: 30px 24px;
    }

    .ta-step-label {
      position: relative !important;
      top: auto !important;
      bottom: auto !important;
      left: auto !important;
      transform: none !important;
      margin-bottom: 12px;
    }

    .ta-step-label.bottom {
      display: flex;
      flex-direction: column-reverse;
      align-items: center;
    }

    .ta-step-label.top {
      display: flex;
      flex-direction: column;
      align-items: center;
    }

    .ta-step-dot {
      margin: 6px auto !important;
      width: 12px;
      height: 12px;
      box-shadow: 0 0 10px currentColor;
    }

    .ta-proc-svg {
      display: none;
    }
  }

  /* ══ S11 PRICING REDESIGN ══ */
  .ta .pcrd {
    background: var(--l0);
    border: 1px solid var(--bl2);
    border-radius: var(--r20);
    padding: 44px 34px;
    position: relative;
    transition: all 0.4s var(--spring);
    overflow: hidden;
    box-shadow: 0 4px 16px rgba(12, 7, 22, 0.02);
  }

  .ta .pcrd::before {
    content: '';
    position: absolute;
    top: 0;
    left: 0;
    right: 0;
    height: 5px;
    background: var(--rg);
    opacity: 0;
    transition: opacity 0.3s var(--ease);
  }

  .ta .pcrd:hover {
    transform: translateY(-8px);
    box-shadow: 0 24px 50px rgba(12, 7, 22, 0.06), 0 10px 24px rgba(167, 13, 42, 0.05);
    border-color: rgba(167, 13, 42, 0.3);
  }

  .ta .pcrd:hover::before {
    opacity: 1;
  }

  .ta .pcrd.hot {
    border-color: var(--red);
    box-shadow: 0 16px 40px rgba(167, 13, 42, 0.08);
    background: var(--l1);
  }

  .ta .pcrd.hot::before {
    opacity: 1;
  }

  .ta .ptag {
    position: absolute;
    top: -1px;
    left: 50%;
    transform: translateX(-50%);
    background: var(--red);
    color: #fff;
    font-size: 10px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.1em;
    padding: 5px 18px;
    border-radius: 0 0 8px 8px;
    white-space: nowrap;
    box-shadow: 0 2px 8px rgba(167, 13, 42, 0.25);
  }

  .ta .pname {
    font-size: 19px;
    font-weight: 800;
    color: var(--lx1);
    margin-bottom: 14px;
    letter-spacing: -0.02em;
  }

  .ta .pamt {
    font-size: 40px;
    font-weight: 850;
    line-height: 1;
    margin-bottom: 10px;
    letter-spacing: -0.04em;
    color: var(--red-d);
    background: linear-gradient(135deg, var(--red-d) 0%, var(--red) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .pnote {
    font-size: 12.8px;
    color: var(--lx3);
    margin-bottom: 26px;
    font-weight: 600;
  }

  .ta .pcrd ul {
    display: flex;
    flex-direction: column;
    gap: 11px;
    margin-bottom: 32px;
  }

  .ta .pcrd li {
    position: relative;
    padding-left: 26px;
    font-size: 13.8px;
    line-height: 1.6;
    color: var(--lx2);
    font-weight: 550;
  }

  .ta .pcrd li::before {
    content: '✓';
    position: absolute;
    left: 0;
    font-weight: 900;
    color: var(--green);
    font-size: 12px;
    display: inline-flex;
    align-items: center;
    justify-content: center;
    width: 18px;
    height: 18px;
    background: rgba(0, 230, 118, 0.08);
    border-radius: 50%;
    margin-top: 2px;
  }

  /* ══ S12 CASE STUDY REDESIGN ══ */
  .ta .cs-dark {
    background: var(--dk1);
    position: relative;
  }

  .ta .cs-dark::before {
    content: '';
    position: absolute;
    inset: 0;
    background: radial-gradient(circle at 0% 100%, rgba(167, 13, 42, 0.08) 0%, transparent 68%);
    pointer-events: none;
  }

  .ta .cs-dark .tc-inner {
    position: relative;
    z-index: 1;
  }

  .ta .cs-dark h2 {
    color: var(--dx1);
  }

  .ta .cs-dark h2 em {
    color: var(--red-l);
  }

  .ta .cs-dark .body {
    color: var(--dx2);
  }

  .ta .cs-dark .eyp {
    background: rgba(8, 4, 12, 0.75);
    border-color: var(--bd2);
  }

  .ta .cs-dark .eyp strong {
    color: #fff;
  }

  .ta .cs-dark .el {
    background: var(--red);
  }

  .ta .cs-dark .cst {
    color: var(--dx1);
  }

  .ta .cs-dark .csb {
    color: var(--dx2);
  }

  .ta .cs-dark .csr {
    border-color: var(--bd);
  }

  .ta .cs-dark .csn {
    background: var(--ra);
    color: var(--red-l);
    border-color: var(--rb);
  }

  .ta .cs-dark .rbar .rpill {
    background: rgba(255, 255, 255, 0.03);
    border-color: var(--bd);
  }

  .ta .cs-dark .rbar .rpill strong {
    color: var(--red-l);
  }

  .ta .cs-dark .rbar .rpill span {
    color: var(--dx3);
  }

  .ta .cs-dark .btn-g {
    border-color: var(--bd3);
    color: var(--dx1) !important;
  }

  .ta .csr {
    display: flex;
    gap: 20px;
    align-items: flex-start;
    padding: 22px 0;
    border-bottom: 1px solid var(--bd);
  }

  .ta .csr:last-child {
    border-bottom: none;
    padding-bottom: 0;
  }

  .ta .csn {
    width: 40px;
    height: 40px;
    border-radius: var(--r10);
    display: flex;
    align-items: center;
    justify-content: center;
    font-size: 13.5px;
    font-weight: 850;
    flex-shrink: 0;
    background: var(--ra);
    color: var(--red-l);
    border: 1px solid var(--rb);
    transition: transform 0.3s;
  }

  .ta .csr:hover .csn {
    transform: scale(1.08) rotate(3deg);
  }

  .ta .cst {
    font-size: 16px;
    font-weight: 800;
    margin-bottom: 8px;
    color: var(--dx1);
    letter-spacing: -0.01em;
  }

  .ta .csb {
    font-size: 13.8px;
    line-height: 1.7;
    color: var(--dx2);
  }

  .ta .rbar {
    display: flex;
    gap: 14px;
    flex-wrap: wrap;
    margin-top: 26px;
  }

  .ta .rpill {
    padding: 16px 20px;
    border-radius: var(--r10);
    text-align: center;
    background: rgba(255, 255, 255, 0.03);
    border: 1px solid var(--bd);
    transition: all 0.28s var(--ease);
  }

  .ta .rpill:hover {
    border-color: rgba(167, 13, 42, 0.4);
    background: rgba(167, 13, 42, 0.08);
    transform: translateY(-3px);
    box-shadow: 0 8px 20px rgba(167, 13, 42, 0.12);
  }

  .ta .rpill strong {
    display: block;
    font-size: 24px;
    font-weight: 800;
    color: var(--red-l);
    letter-spacing: -0.03em;
    line-height: 1.1;
  }

  .ta .rpill span {
    display: block;
    font-size: 11px;
    color: var(--dx3);
    margin-top: 4px;
    font-weight: 700;
    text-transform: uppercase;
    letter-spacing: 0.08em;
  }

  .ta .cs-dark .btn-g:hover {
    border-color: var(--red);
    background: var(--ra);
  }

  /* Left-right symmetry card boxes */
  .ta .cs-grey .testi {
    background: var(--l0);
    border-color: var(--bl2);
    box-shadow: 0 8px 30px rgba(12, 7, 22, 0.03);
    border-radius: var(--r20);
    padding: 36px;
    border: 1px solid var(--bl2);
  }

  .ta .cs-grey .tlbl {
    color: var(--red);
    font-size: 11px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.14em;
    margin-bottom: 16px;
    display: block;
  }

  .ta .cs-grey .tq {
    color: var(--lx1);
    font-size: 16px;
    font-weight: 550;
    line-height: 1.76;
    font-style: italic;
    letter-spacing: -0.01em;
  }

  .ta .cs-grey .tau {
    border-color: var(--bl2);
    padding-top: 20px;
    margin-top: 20px;
    display: flex;
    align-items: center;
    gap: 14px;
    border-top: 1px solid var(--bl2);
  }

  .ta .cs-grey .tav {
    width: 44px;
    height: 44px;
    border-radius: 50%;
    background: var(--ra);
    border: 1px solid var(--rb);
    display: flex;
    align-items: center;
    justify-content: center;
    font-size: 14px;
    font-weight: 850;
    color: var(--red);
  }

  .ta .cs-grey .tnm {
    color: var(--lx1);
    font-weight: 850;
    font-size: 15px;
  }

  .ta .cs-grey .trl {
    color: var(--lx3);
    font-size: 12.8px;
    margin-top: 2px;
    font-weight: 550;
  }

  .ta .cs-grey .tvfy {
    margin-top: 20px;
    padding: 14px 18px;
    background: var(--l1);
    border-radius: var(--r10);
    font-size: 13.8px;
    font-weight: 700;
    border: 1px solid var(--bl2);
  }

  .ta .cs-grey .proj-brief {
    background: var(--l0);
    border-color: var(--bl2);
    box-shadow: 0 8px 30px rgba(12, 7, 22, 0.03);
    border-radius: var(--r20);
    padding: 26px 30px;
    border: 1px solid var(--bl2);
  }

  .ta .cs-grey .pb-title {
    color: var(--red);
    font-size: 11px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.14em;
    margin-bottom: 16px;
  }

  .ta .cs-grey .pb-row {
    border-color: var(--bl2);
    display: flex;
    align-items: center;
    justify-content: space-between;
    padding: 10px 0;
    border-bottom: 1px solid var(--bl2);
  }

  .ta .cs-grey .pb-row:last-child {
    border-bottom: none;
  }

  .ta .cs-grey .pb-label {
    color: var(--lx3);
    font-weight: 600;
    font-size: 13.5px;
  }

  .ta .cs-grey .pb-val {
    color: var(--lx1);
    font-weight: 700;
    font-size: 13.5px;
  }

  .ta .cs-grey .pm-item {
    background: var(--l0);
    border-color: var(--bl2);
    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.02);
    border-radius: var(--r12);
    padding: 16px;
    border: 1px solid var(--bl2);
  }

  .ta .cs-grey .pm-num {
    color: var(--red-d);
    font-size: 24px;
    font-weight: 850;
    letter-spacing: -0.02em;
    background: linear-gradient(135deg, var(--red-d) 0%, var(--red) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .cs-grey .pm-lbl {
    color: var(--lx3);
    font-weight: 700;
    font-size: 10.5px;
    margin-top: 4px;
    text-transform: uppercase;
    letter-spacing: 0.08em;
  }

  /* ══ S13 REVIEWS REDESIGN ══ */
  .ta .rev-card {
    background: rgba(255, 255, 255, 0.02);
    border: 1px solid rgba(255, 255, 255, 0.06);
    border-radius: var(--r20);
    padding: 34px;
    transition: all 0.35s var(--ease);
    box-shadow: 0 8px 30px rgba(0, 0, 0, 0.25);
    backdrop-filter: blur(12px);
    -webkit-backdrop-filter: blur(12px);
  }

  .ta .rev-card:hover {
    background: rgba(255, 255, 255, 0.05);
    border-color: rgba(167, 13, 42, 0.35);
    transform: translateY(-8px);
    box-shadow: 0 30px 60px rgba(167, 13, 42, 0.12), 0 10px 30px rgba(0, 0, 0, 0.45);
  }

  .ta .rev-stars {
    color: #ffd700;
    font-size: 16px;
    letter-spacing: 2px;
    margin-bottom: 14px;
    text-shadow: 0 0 10px rgba(255, 215, 0, 0.3);
  }

  .ta .rev-source {
    color: var(--dx3);
    font-size: 11.5px;
    font-weight: 750;
    text-transform: uppercase;
    letter-spacing: 0.1em;
    margin-bottom: 16px;
  }

  .ta .rev-quote {
    color: var(--dx2);
    font-size: 15px;
    line-height: 1.76;
    font-style: italic;
    font-weight: 500;
  }

  .ta .rev-author {
    display: flex;
    align-items: center;
    gap: 16px;
    padding-top: 20px;
    margin-top: 20px;
    border-top: 1px solid rgba(255, 255, 255, 0.06);
  }

  .ta .rev-av {
    width: 42px;
    height: 42px;
    border-radius: 50%;
    background: rgba(255, 255, 255, 0.08);
    border: 1px solid rgba(255, 255, 255, 0.12);
    display: flex;
    align-items: center;
    justify-content: center;
    font-size: 14px;
    font-weight: 850;
    color: #fff;
  }

  .ta .rev-name {
    color: #fff;
    font-weight: 800;
    font-size: 14.5px;
  }

  .ta .rev-role {
    color: var(--dx3);
    font-size: 12.5px;
    margin-top: 2px;
    font-weight: 500;
  }

  .ta .rev-tag {
    font-size: 11px;
    color: var(--red-l);
    font-weight: 750;
    margin-top: 16px;
    text-transform: uppercase;
    letter-spacing: 0.06em;
  }

  .ta .sR .rev-card {
    background: rgba(255, 255, 255, 0.05);
    border-color: rgba(255, 255, 255, 0.1);
  }

  .ta .sR .rev-card:hover {
    background: rgba(255, 255, 255, 0.1);
    border-color: rgba(255, 255, 255, 0.25);
  }

  .ta .sR .rev-stars {
    color: #ffe644;
  }

  .ta .sR .rev-source {
    color: rgba(255, 255, 255, 0.7);
  }

  .ta .sR .rev-quote {
    color: #fff;
  }

  .ta .sR .rev-author {
    border-color: rgba(255, 255, 255, 0.14);
  }

  .ta .sR .rev-av {
    background: rgba(255, 255, 255, 0.15);
    border-color: rgba(255, 255, 255, 0.24);
  }

  .ta .sR .rev-role {
    color: rgba(255, 255, 255, 0.65);
  }

  .ta .sR .rev-tag {
    color: #ffe644;
  }

  .ta .rev-summary {
    display: flex;
    align-items: center;
    gap: 32px;
    flex-wrap: wrap;
    background: var(--l0);
    border: 1px solid var(--bl2);
    border-radius: var(--r14);
    padding: 24px 36px;
    box-shadow: 0 8px 24px rgba(12, 7, 22, 0.02);
  }

  .ta .rev-sum-item {
    display: flex;
    flex-direction: column;
  }

  .ta .rev-sum-item strong {
    font-size: 24px;
    font-weight: 850;
    color: var(--red-d);
    letter-spacing: -0.02em;
    line-height: 1.1;
    background: linear-gradient(135deg, var(--red-d) 0%, var(--red) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .rev-sum-item span {
    font-size: 11.5px;
    color: var(--lx3);
    text-transform: uppercase;
    letter-spacing: 0.08em;
    margin-top: 5px;
    font-weight: 700;
  }

  .ta .rev-sum-sep {
    width: 1px;
    height: 42px;
    background: var(--bl2);
    flex-shrink: 0;
  }

  /* ══ S14 RELATED SERVICES REDESIGN ══ */
  .ta .rcrd {
    display: flex;
    align-items: center;
    gap: 18px;
    background: var(--l0);
    border: 1px solid var(--bl2);
    border-radius: var(--r14);
    padding: 22px 24px;
    transition: all 0.35s var(--spring);
    text-decoration: none !important;
    box-shadow: 0 4px 16px rgba(12, 7, 22, 0.02);
  }

  .ta .rcrd:hover {
    transform: translateX(6px);
    border-color: rgba(167, 13, 42, 0.35);
    box-shadow: 0 16px 36px rgba(167, 13, 42, 0.08);
  }

  .ta .rict {
    width: 44px;
    height: 44px;
    border-radius: var(--r10);
    display: flex;
    align-items: center;
    justify-content: center;
    background: var(--ra);
    border: 1px solid var(--rb);
    flex-shrink: 0;
    transition: transform 0.3s var(--spring);
  }

  .ta .rcrd:hover .rict {
    transform: scale(1.1) rotate(4deg);
    background: rgba(167, 13, 42, 0.1);
  }

  .ta .rict svg {
    width: 20px;
    height: 20px;
    color: var(--red);
  }

  .ta .rcat {
    font-size: 10px;
    font-weight: 850;
    text-transform: uppercase;
    letter-spacing: 0.1em;
    color: var(--lx3);
    margin-bottom: 5px;
  }

  .ta .rtit {
    font-size: 15px;
    font-weight: 800;
    color: var(--lx1);
    letter-spacing: -0.01em;
  }

  /* ══ S15 FAQ REDESIGN ══ */
  .ta .fitem {
    background: var(--l0);
    border: 1px solid var(--bl2);
    border-radius: var(--r14);
    overflow: hidden;
    transition: border-color 0.3s var(--ease), box-shadow 0.3s var(--ease);
    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.01);
  }

  .ta .fitem:hover {
    border-color: rgba(167, 13, 42, 0.28);
    box-shadow: 0 12px 28px rgba(12, 7, 22, 0.05);
  }

  .ta .fq {
    font-size: 16px;
    font-weight: 800;
    line-height: 1.35;
    color: var(--lx1);
    padding: 24px 28px 0;
    letter-spacing: -0.02em;
  }

  .ta .faq-a {
    font-size: 14px;
    line-height: 1.74;
    color: var(--lx2);
    padding: 12px 28px 24px;
    font-weight: 500;
  }

  /* ══ S16 CTA BANNER REDESIGN ══ */
  .ta .ctawrap {
    display: flex;
    align-items: center;
    justify-content: space-between;
    gap: 64px;
    flex-wrap: wrap;
  }

  .ta .ctal {
    flex: 1;
    min-width: 280px;
  }

  .ta .ctar {
    display: flex;
    flex-direction: column;
    gap: 14px;
    flex-shrink: 0;
    min-width: 260px;
  }

  .ta .ctapr {
    display: flex;
    gap: 36px;
    margin-top: 32px;
    flex-wrap: wrap;
  }

  .ta .ctapr strong {
    display: block;
    font-size: 28px;
    font-weight: 850;
    color: var(--red-d);
    letter-spacing: -0.03em;
    line-height: 1.1;
    background: linear-gradient(135deg, var(--red-d) 0%, var(--red) 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
  }

  .ta .ctapr span {
    display: block;
    font-size: 11.5px;
    color: var(--lx3);
    text-transform: uppercase;
    letter-spacing: 0.08em;
    margin-top: 6px;
    font-weight: 700;
  }

  .ta .ctanote {
    font-size: 12.8px;
    color: var(--lx3);
    text-align: center;
    font-weight: 600;
  }

  /* Responsive adjustments */
  @media(max-width:1100px) {

    .ta .hi,
    .ta .sp5050a {
      grid-template-columns: 1fr;
    }

    .ta .hr {
      display: block;
      margin-top: 48px;
      width: 100%;
    }

    .ta .hl {
      padding-bottom: 0;
    }

    .ta .sp5050 {
      grid-template-columns: 1fr;
      gap: 56px;
    }

    .ta .g4 {
      grid-template-columns: 1fr 1fr;
    }

    .ta .g5 {
      grid-template-columns: 1fr 1fr 1fr;
      gap: 30px;
    }

    .ta .procl {
      display: none;
    }

    .ta .ctawrap {
      flex-direction: column;
      align-items: flex-start;
      gap: 40px;
    }

    .ta .ctar {
      width: 100%;
    }

    .ta .hsr {
      flex-wrap: wrap;
      justify-content: center;
      gap: 20px 24px;
    }

    .ta .hsep {
      display: none;
    }

    .ta .tbrow {
      display: grid;
      grid-template-columns: repeat(3, 1fr);
      gap: 24px 32px;
      justify-items: center;
    }

    .ta .tsep {
      display: none;
    }
  }

  @media(max-width:900px) {
    .ta .stack16 {
      grid-template-columns: repeat(3, 1fr);
    }

    .ta .approach-cards {
      grid-template-columns: 1fr 1fr 1fr;
    }

    .ta .proj-metrics {
      grid-template-columns: 1fr 1fr 1fr;
    }

    .ta .two-col {
      grid-template-columns: 1fr;
    }

    .ta .tc-panel {
      padding: 68px 32px;
    }

    .ta .tc-panel .tc-inner {
      max-width: none;
      margin: 0;
    }

    .ta .qsteps {
      grid-template-columns: 1fr;
      gap: 16px;
    }
  }

  @media(max-width:768px) {
    .ta .W {
      padding: 0 24px;
    }

    .ta .S {
      padding: 80px 0;
    }

    .ta .hero {
      padding-bottom: 80px;
    }

    .ta h2 {
      font-size: 28px;
      letter-spacing: 0;
      line-height: 1.16;
    }

    .ta h3 {
      font-size: 18px;
      letter-spacing: 0;
    }

    .ta .lead {
      font-size: 16px;
      line-height: 1.7;
    }

    .ta .body {
      font-size: 14.5px;
      line-height: 1.72;
    }

    .ta .hh1 {
      font-size: 38px;
      line-height: 1.35;
      letter-spacing: 0;
    }

    .ta .sh {
      display: block;
      margin-bottom: 34px;
    }

    .ta .pill {
      margin-bottom: 20px;
    }

    .ta .eyp {
      max-width: 100%;
      white-space: normal;
      line-height: 1.35;
    }

    .ta .cbody {
      white-space: pre-wrap;
      overflow-wrap: anywhere;
      font-size: 11.5px;
      line-height: 1.75;
      padding: 20px 18px;
    }

    .ta .revl,
    .ta .revr {
      transform: none;
    }

    /* Symmetrical 1-column layouts for 3-card blocks (Services, Pricing, Reviews, Related) */
    .ta .g3 {
      grid-template-columns: 1fr;
      max-width: 600px;
      margin-left: auto;
      margin-right: auto;
      gap: 24px;
    }

    .ta .g2 {
      grid-template-columns: 1fr;
    }

    .ta .g4,
    .ta .g5 {
      grid-template-columns: 1fr 1fr;
      gap: 16px;
    }

    .ta .hsr {
      display: grid;
      grid-template-columns: 1fr 1fr;
      gap: 12px;
    }

    .ta .hsi {
      width: 100%;
      min-height: 92px;
      padding: 16px;
      border: 1px solid var(--bl2);
      border-radius: var(--r14);
      background: var(--l0);
      box-shadow: 0 6px 18px rgba(12, 7, 22, 0.04);
    }

    .ta .hsi strong {
      font-size: 18px;
      line-height: 1.2;
    }

    .ta .hsi span {
      font-size: 10px;
      line-height: 1.35;
    }

    .ta .hsep,
    .ta .tsep {
      display: none;
    }

    .ta .sgrid {
      grid-template-columns: 1fr 1fr;
      gap: 14px;
    }

    .ta .scard {
      padding: 22px 18px;
    }

    .ta .sn {
      font-size: 30px;
    }

    .ta .sl {
      font-size: 12.5px;
    }

    .ta .tech-adv {
      padding: 22px 18px;
    }

    .ta .fadv-row {
      align-items: flex-start;
      gap: 6px;
    }

    .ta .fadv-label,
    .ta .fadv-val {
      line-height: 1.45;
    }

    /* Trust Bar: Transform into vertical list of horizontal rows matching Miracuves.com mobile layout */
    .ta .tbrow {
      grid-template-columns: 1fr;
      gap: 12px;
      justify-items: stretch;
      max-width: 600px;
      margin: 0 auto;
    }

    .ta .titem {
      display: flex !important;
      flex-direction: row !important;
      align-items: center !important;
      gap: 16px !important;
      background: rgba(255, 255, 255, 0.02) !important;
      border: 1px solid rgba(255, 255, 255, 0.06) !important;
      border-radius: var(--r14) !important;
      padding: 16px 20px !important;
      width: 100% !important;
      text-align: left !important;
    }

    .ta .titem .ico {
      width: 40px !important;
      height: 40px !important;
      border-radius: 8px !important;
      flex-shrink: 0 !important;
      background: rgba(167, 13, 42, 0.08) !important;
      border-color: rgba(167, 13, 42, 0.2) !important;
    }

    .ta .titem .ico svg {
      width: 18px !important;
      height: 18px !important;
      color: var(--red-l) !important;
    }

    .ta .titem h4 {
      font-size: 14.5px !important;
      color: #ffffff !important;
      font-weight: 700 !important;
      margin: 0 !important;
    }

    .ta .titem p {
      font-size: 12px !important;
      color: rgba(255, 255, 255, 0.6) !important;
      text-transform: none !important;
      letter-spacing: 0 !important;
      margin-top: 3px !important;
      font-weight: 500 !important;
    }

    .ta .approach-cards {
      grid-template-columns: 1fr 1fr;
    }

    .ta .cs-right {
      gap: 14px;
    }

    .ta .rev-summary {
      flex-direction: column;
      align-items: center;
      gap: 20px;
      padding: 24px;
      text-align: center;
    }

    .ta .rev-sum-sep {
      display: none;
    }

    /* Mobile card view for comparison table */
    .ta .ctw {
      overflow: visible;
      border: none;
      border-radius: 0;
      margin-top: 28px;
      box-shadow: none;
    }

    .ta .ct,
    .ta .ct thead,
    .ta .ct tbody,
    .ta .ct tr,
    .ta .ct th,
    .ta .ct td {
      display: block;
      width: 100%;
    }

    .ta .ct thead {
      display: none;
    }

    .ta .ct tbody {
      display: grid;
      gap: 16px;
    }

    .ta .ct tr {
      border: 1px solid var(--bd2);
      border-radius: var(--r14);
      overflow: hidden;
      background: rgba(255, 255, 255, 0.03);
      box-shadow: 0 16px 36px rgba(0, 0, 0, 0.22);
    }

    .ta .ct th,
    .ta .ct td {
      min-width: 0 !important;
      border: 0 !important;
      padding: 15px 18px;
    }

    .ta .ct td {
      border-top: 1px solid var(--bd) !important;
      font-size: 13.5px;
      line-height: 1.55;
    }

    .ta .ct td:first-child {
      border-top: 0 !important;
      background: rgba(255, 255, 255, 0.05);
      color: #fff !important;
      font-size: 15px;
      font-weight: 850 !important;
    }

    .ta .ct td:not(:first-child)::before {
      display: block;
      margin-bottom: 5px;
      font-size: 10px;
      font-weight: 850;
      line-height: 1.3;
      letter-spacing: 0.08em;
      text-transform: uppercase;
      color: rgba(255, 255, 255, 0.52);
    }

    .ta .ct td:nth-child(2)::before {
      content: 'Machine Learning - Miracuves Default';
      color: var(--red-l);
    }

    .ta .ct td:nth-child(3)::before {
      content: 'AutoML Platform';
    }

    .ta .ct td:nth-child(4)::before {
      content: 'DIY Data Science';
    }

    .ta .ct .ch {
      border-left: 0 !important;
      border-right: 0 !important;
      background: rgba(167, 13, 42, 0.08) !important;
    }
  }

  @media(max-width:600px) {
    .ta .stack16 {
      grid-template-columns: repeat(2, 1fr);
    }

    .ta .tc-panel {
      padding: 48px 20px;
    }

    .ta .sgrid,
    .ta .g4,
    .ta .g5 {
      grid-template-columns: 1fr;
    }

    .ta .scard,
    .ta .scrd,
    .ta .vcrd,
    .ta .pcrd,
    .ta .rev-card,
    .ta .fitem {
      border-radius: var(--r14);
    }

    .ta .fadv-row {
      flex-direction: column;
      align-items: flex-start;
      padding: 13px 0;
    }

    .ta .fadv-val {
      font-size: 14px;
    }

    .ta .pb-row {
      flex-direction: column;
      align-items: flex-start;
      gap: 3px;
    }

    .ta .rbar {
      display: grid;
      grid-template-columns: 1fr 1fr;
      gap: 10px;
    }

    .ta .rpill {
      width: 100%;
      padding: 14px 12px;
    }
  }

  @media(max-width:480px) {
    .ta .W {
      padding: 0 16px;
    }

    .ta .S {
      padding: 60px 0;
    }

    .ta .hero {
      padding: 76px 0 100px;
    }

    .ta .hi {
      gap: 34px;
    }

    .ta .hr {
      margin-top: 34px;
    }

    .ta .hh1 {
      font-size: 34px;
      line-height: 1.4;
      letter-spacing: 0;
    }

    .ta .hh1 .h1s {
    display: block;
  }

  .ta .hh1 .dim {
      margin-top: 10px;
      font-size: 0.72em;
      letter-spacing: 0;
    }

    .ta .hdim {
      margin-top: 16px;
      font-size: 20px;
      letter-spacing: 0;
    }

    .ta .hh1 em {
      padding: 0;
      border-radius: 0;
    }

    .ta h2 {
      font-size: 25px;
      letter-spacing: 0;
    }

    .ta .lead {
      font-size: 15.5px;
    }

    .ta .htrust {
      gap: 8px;
      margin: 22px 0;
    }

    .ta .htr {
      width: 100%;
      justify-content: flex-start;
      border-radius: var(--r10);
    }

    .ta .hsr {
      grid-template-columns: 1fr;
    }

    .ta .hsi {
      min-height: 0;
      padding: 15px 16px;
    }

    .ta .hsch {
      padding: 18px 18px 14px;
      align-items: flex-start;
      flex-direction: column;
      gap: 4px;
    }

    /* Hero Stats Card: Rework into perfect 2x2 grid matching Miracuves.com mobile layout */
    .ta .hscg {
      grid-template-columns: 1fr 1fr;
      gap: 16px;
      padding: 24px 16px;
    }

    .ta .hsci {
      border: none !important;
      padding: 0 !important;
      text-align: center;
    }

    .ta .hscn {
      font-size: 26px !important;
      font-weight: 800 !important;
      color: #ffffff !important;
      -webkit-text-fill-color: #ffffff !important;
    }

    .ta .hscll {
      font-size: 9px !important;
      color: rgba(255, 255, 255, 0.6) !important;
      text-transform: uppercase !important;
      letter-spacing: 0.08em !important;
      font-weight: 700 !important;
      margin-top: 3px !important;
    }

    .ta .hshow {
      grid-template-columns: 1fr;
    }

    .ta .hstatus-grid {
      grid-template-columns: 1fr;
    }

    /* Force S02 Trust Bar cards to full width */
    .ta .tbrow {
      max-width: 100%;
    }

    .ta .approach-cards {
      grid-template-columns: 1fr;
    }

    .ta .stack16 {
      grid-template-columns: 1fr;
    }

    .ta .proj-metrics {
      grid-template-columns: 1fr;
      gap: 12px;
    }

    .ta .anote,
    .ta .hnote,
    .ta .wcall {
      padding: 18px 16px;
    }

    .ta .mrow {
      align-items: flex-start;
    }

    .ta .mrow span {
      min-width: 0;
    }

    .ta .ck {
      font-size: 13.8px;
      padding: 6px 0;
    }

    .ta .pcrd {
      padding: 32px 20px;
    }

    .ta .pamt {
      font-size: 34px;
    }

    .ta .vbody,
    .ta .sb,
    .ta .rev-card,
    .ta .cs-grey .testi,
    .ta .cs-grey .proj-brief {
      padding: 22px 18px;
    }

    .ta .fq {
      padding: 20px 18px 0;
      font-size: 15px;
    }

    .ta .faq-a {
      padding: 10px 18px 20px;
      font-size: 13.5px;
    }

    .ta .rbar {
      grid-template-columns: 1fr;
    }

    .ta .ctapr {
      display: grid;
      grid-template-columns: 1fr 1fr;
      gap: 14px;
      width: 100%;
    }

    .ta .ctar {
      min-width: 0;
    }

    .ta .ctar .btn {
      width: 100%;
      justify-content: center;
      white-space: normal;
      text-align: center;
    }

    /* Hero Button Stacking: full-width block buttons easy for thumbs */
    .ta .hctas {
      flex-direction: column;
      width: 100%;
    }

    .ta .hctas .btn {
      width: 100%;
      justify-content: center;
      padding: 18px 24px;
      font-size: 15px;
    }

    .ta .rcrd {
      width: 100%;
    }
  }

  
  
    
    
    
    
      
      
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              NDA Day One
          

          
            [WhatsApp — Talk to Our Team](https://wa.me/919830009649)
            [Explore ML Solutions](https://miracuves.com/schedule-consultation/)
          

          
            Clutch Reviewed 4.9★
            ·
            Starting from $3,699
            ·
            [View ML deployments](https://miracuves.com/portfolio/)
          
        

        
        
          
          
            
            
            
              
            
            
              
#### ML Stack Powered

              
TensorFlow · PyTorch · scikit-learn · MLflow · Kubeflow · SageMaker

            
          

          
            
              Miracuves Delivery RecordML Engineering
              
              
                
                  6–12 Weeks
                  Delivery timeline
                
                
                  $3,699
                  Starting price
                
                
                  30+
                  ML Models in Production
                
                
                  95%
                  Avg Model Accuracy
                
              
              
                ML engineers active right now
              
            
          

                    
          
            [E-commerce ML

              
#### Recommendation Engine

              
Deployed in 8 weeks · From $12,999](https://miracuves.com/contact/)
            [Manufacturing

              
#### Predictive Maintenance

              
Deployed in 8 weeks · 60% downtime cut](https://miracuves.com/contact/)
          

          
          
            
              
                
              
              ML Pipeline Console
              ACTIVE
            
            
              
                FRAMEWORK
                TensorFlow / PyTorch
              
              
                EXPERIMENT TRACKING
                MLflow + Kubeflow
              
              
                VALIDATION
                Holdout + Drift Checks
              
              
                DEPLOYMENT
                K8s / SageMaker / Edge
              
            
          
        
      
    
  

  
  
    
      
        **30+ ML Engineers**Specialized in AI/ML development
        
        **25+ ML Models**In Production
        
        **MLOps Standard**MLflow + Kubeflow on every project
        
        **TensorFlow+PyTorch**Certified Engineers
        
        **95% Model Accuracy**Avg across deployed systems
      
    
  

  
  
    
      
        
          
              
              
              
            
          
            
#### White-Label Ready

            
Fully rebrandable on delivery

          
        
        
        
          
              
            
          
            
#### NDA Day One

            
IP protected first call

          
        
        
        
          
              
              
            
          
            
#### Full Source Code

            
Delivered at handoff

          
        
        
        
          
              
              
            
          
            
#### 60-Day Support

            
Post-launch included

          
        
        
        
          
              
              
            
          
            
#### 100% IP Ownership

            
Yours — always

          
        
        
        [#### Clutch Reviewed 4.9★

            
Third-party verified](https://miracuves.com/reviews-awards/)
      
    
  
		
			
				
				
					More than 6,000+ Companies Trust us Worldwide				
				
				
				
					        
            
                
					                            
                                
									                                    
										[![Miracuves Ranked among top 1000 companies globally by Clutch](https://miracuves.com/wp-content/uploads/2024/03/Miracuves-Clutch-top-1000-company.webp "Machine Learning Development 1")](https://clutch.co/profile/miracuves-solutions#reviews)                                    
									                                
                            
						                            
                                
									                                    
										[![Miracuves is a top-minus service provider in the United States by Clutch](https://miracuves.com/wp-content/uploads/2024/03/Miracuves-clutch-top-managed-service-provider-US.webp "Machine Learning Development 2")](https://clutch.co/profile/miracuves-solutions#reviews)                                    
									                                
                            
						                            
                                
									                                    
										![MediaCube is the best managed IT service provider in New York City by Expertise.com](https://miracuves.com/wp-content/uploads/2024/03/Miracuves-Expertise-Provider.webp "Machine Learning Development 3")                                    
									                                
                            
						                            
                                
									                                    
										![MiraQs as an official member of Forbes Business Council ](https://miracuves.com/wp-content/uploads/2024/03/Mircuves-Official-Memeber-Forbes-Council.webp "Machine Learning Development 4")                                    
									                                
                            
						                            
                                
									                                    
										[![Miracuves is a verified agency with design rush. ](https://miracuves.com/wp-content/uploads/2024/03/DesignRush-Top-Design-Agencies-in-India.webp "Machine Learning Development 5")](https://www.designrush.com/agency/profile/miracuves#reviews)                                    
									                                
                            
						                            
                                
									                                    
										[![MiraQs is ranked as a trustworthy service provider with Trustpilot. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-TrustPilot-Top-Software-Development-Company.webp "Machine Learning Development 6")](https://www.trustpilot.com/review/miracuves.com)                                    
									                                
                            
						                            
                                
									                                    
										[![Miracuves is one of the top managed service providers, companies by themanifest. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-The-Manifest-Top-Managed-Software-Development-Company.webp "Machine Learning Development 7")](https://themanifest.com/company/miracuves-solutions)                                    
									                                
                            
						                            
                                
									                                    
										[![Miracuves ranked as a top software development company by Design Rush. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-Designrush-Top-Software-Development-Company.webp "Machine Learning Development 8")](https://www.designrush.com/agency/profile/miracuves#reviews)                                    
									                                
                            
						                            
                                
									                                    
										[![Miracuves is recognized as top software developed company by goodfirms. ](https://miracuves.com/wp-content/uploads/2025/04/Miracuves-GoodFirms-Top-Software-Development-Company.webp "Machine Learning Development 9")](https://www.goodfirms.co/company/miracuves-solution)                                    
									                                
                            
						                
            
			        
						
				
				
				
		
  
  
    
      
        
        
          
            
              **Our ML Approach**
            
          
          
## How Miracuves delivers machine learning systems — from 30+ production deployments

          
After deploying 30+ production ML systems across manufacturing, fintech, and e-commerce, Miracuves has a specific methodology for machine learning delivery. We start from proven pipeline modules — data ingestion, feature engineering, model training, validation, deployment, and monitoring — not from an untracked Jupyter notebook.

          
ML pipelines deliver consistent, reproducible results from a unified codebase.
            For production deployments, this eliminates the gap between data science experimentation and engineering — one pipeline,
            automated retraining, full model artifacts yours on handoff.

          
**Who this service is built for:** Product leaders, data teams, and enterprises that need predictive models in production — recommendation engines, fraud scoring, demand forecasting, churn prediction, predictive maintenance, or computer vision QC. Miracuves ML development fits when you have (or can collect) labeled data, need models served via API with monitoring, and want a company accountable for MLOps — not a one-off notebook. If your problem is better solved with rules or a simple dashboard, we say so upfront.

          
            End-to-end ML pipeline — data ingestion, feature engineering, model training, deployment, monitoring
            TensorFlow and PyTorch as primary frameworks — scikit-learn for classical ML, MLflow for experiment tracking
            Kubeflow orchestration for distributed training and automated model retraining pipelines
            GPU-accelerated training on AWS SageMaker with automated hyperparameter tuning
            Production monitoring with Grafana and Prometheus — drift detection, accuracy tracking, automated alerts
          

          
            
From our ML team — Factory predictive maintenance, 8 weeks

            
"500 machines, 2TB of sensor data monthly, and zero visibility into failures until production stopped. Miracuves built a TensorFlow LSTM pipeline with MQTT ingestion, automated retraining every 24 hours, and a Grafana dashboard our maintenance team actually uses. Downtime dropped 60% in Q1 — the system paid for itself in four months."

          

          
            
              
              
            
            Written by the Miracuves ML Engineering Team · May 2026 · [View
                Deployed Portfolio →](https://miracuves.com/portfolio/)
          
        

        
        
          
          
            30+Production ML systems deployed
            60%Avg downtime reduction (predictive maintenance)
            94%Peak model accuracy on monitored projects
            6–12Weeks from brief to production API
            $3,699Published starting investment anchor
            100%Model artifacts + code ownership
          

          
          
            
              
                  
                  
                
              Training
              Model fitting
            
            
              
                  
                  
                
              Inference
              Prediction
            
            
              
                  
                  
                  
                
              Pipelines
              MLOps workflows
            
          

          
          
            
#### Why ML at Miracuves

            Time to first production model6–12 weeks
            Experiment reproducibilityMLflow on every run
            Inference deploymentAPI · Batch · Edge
            Monitoring includedDrift + accuracy alerts
            Framework flexibilityTensorFlow · PyTorch · sklearn
            Source code ownership100% yours
          
        
      
    
  

    
    
    
      
        
          
            
              **30+ ML Systems Deployed**
            
          
          
## What Miracuves builds — production ML systems you can ship

        
        [Discuss Your ML Use Case →](https://miracuves.com/contact/)
      

      
        [8 Weeks01
          
          
            
E-commerce

            
### Recommendation Engine

            
Collaborative filtering and real-time personalization with A/B testing, feature store, and inference API.

            From $12,999PyTorchReal-time](https://miracuves.com/contact/)
        [8 Weeks02
          
          
            
Manufacturing

            
### Predictive Maintenance

            
IoT sensor ingestion, LSTM anomaly detection, Grafana alerts — failures predicted 48–72 hours ahead.

            From $18,999TensorFlowIoT + MQTT](https://miracuves.com/contact/)
        [10 Weeks03
          
          
            
Fintech

            
### Fraud Detection System

            
Real-time transaction scoring, ensemble models, sub-200ms inference, and analyst review dashboards.

            From $22,999PyTorch<200ms](https://miracuves.com/contact/)
        [6 Weeks04
          
          
            
Retail

            
### Demand Forecasting

            
Time-series models for SKU-level inventory planning with seasonality, promotions, and external signals.

            From $14,999scikit-learnProphet](https://miracuves.com/contact/)
        [6 Weeks05
          
          
            
SaaS

            
### Churn Prediction

            
Behavioral feature engineering, gradient boosting classifiers, and CRM-integrated risk scores for retention teams.

            From $11,999XGBoostCRM hooks](https://miracuves.com/contact/)
        [12 Weeks06
          
          
            
Manufacturing

            
### Computer Vision QC

            
Defect detection on production lines with CNN models, edge deployment via TensorFlow Lite, and audit trails.

            From $24,999TensorFlowEdge + Cloud](https://miracuves.com/contact/)
      

      **Honest note:** Machine learning is transformative for data-rich use cases — recommendation, prediction, classification, and anomaly detection. For simple rule-based automation or applications with insufficient training data, Miracuves may recommend a non-ML approach. We tell you which fits before any commitment.
    
  

  
  
    
    
      
        
          
            **Technology Comparison**
          
        
        
## Custom ML pipeline vs AutoML vs in-house — which fits your project?

        
Most development companies avoid this question because
          they only know one stack. Miracuves answers it honestly — your technology choice determines long-term cost,
          performance, and maintenance.

      

      
        
| Metric | Miracuves ML · Custom Pipeline  
← MIRACUVES DEFAULT | AutoML Platform | DIY Data Science |
| --- | --- | --- | --- |
| Model Accuracy | 95% target — custom tuned per use case | 70–85% — generic pre-built models | Variable — depends on team expertise |
| Pipeline Control | Full — data ingestion to monitoring | Limited — platform-provided pipeline only | Full — but requires DevOps setup |
| Time to Production | 6–12 weeks — full pipeline deployed | Fast — pre-built templates | Slow — build everything from scratch |
| Customization | Full — custom architecture, custom models | Constrained — platform model zoo only | Full — unlimited, requires expertise |
| Best For | Custom ML · production systems · full ownership | Quick prototyping · standard use cases | Research · in-house ML teams |

      

      
        
          
Choose Miracuves ML if…

          
You need custom models · end-to-end pipeline ownership · production-grade MLOps · a team accountable for model performance in production.

        
        
          
Consider an alternative if…

          
You only need a no-code AutoML dashboard · your team already has senior ML engineers and MLOps · or you lack sufficient labeled data and should start with data collection first. [See AI Development →](https://miracuves.com/service/artificial-intelligence-development/)

        
      
    
  

  
  
    
      
        
        
          
            
              **Technical Architecture**
            
          
          
## How Miracuves engineers structure ML projects for production

          
These are the specific decisions our ML engineering team makes on every
            project — choices that determine whether a model scales in production or becomes a notebook that cannot be
            operationalized.

          
            
              
### Architecture — Modular ML Pipeline

              
Strict separation: Data Ingestion → Feature Engineering → Model Training → Model Evaluation → Deployment → Monitoring. Every stage is independently deployable and testable. This is how Miracuves delivers production ML systems that can be retrained and redeployed without pipeline disruption.

            
            
              
### Experimentation — MLflow for Tracking, Kubeflow for Pipelines

              
MLflow tracks every experiment with full parameter, metric, and artifact logging. Kubeflow orchestrates the end-to-end ML pipeline — from data validation through model deployment. The most common problem inherited from other teams: untracked experiments in Jupyter notebooks with no reproducibility. We eliminate this on day one.

            
            
              
### Performance — GPU Training with Distributed Computing

              
All model training runs on GPU instances with automatic distributed computing for large datasets. We profile every training run with TensorBoard — CPU-only training is used for initial prototyping, never for production model training.

            
            
              
                
What most Machine Learning agencies get wrong

                
Untracked Jupyter notebooks with no reproducibility. Training on production data without holdout validation. Models deployed without monitoring or drift detection. No versioned artifacts — impossible to roll back. Miracuves has inherited every one of these — starting with MLOps discipline is always faster than cleaning up.

              
            
          
        

        
        
                    
            
              
              
              
              train_pipeline.py — sklearn ML Pipeline
            
            # Production ML pipeline with scikit-learn
# Used in recommendation + prediction products

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

def build_ml_pipeline(X, y):
    # Feature engineering + model training
    pipe = Pipeline([
        ('scaler', StandardScaler()),
        ('classifier', RandomForestClassifier(
            n_estimators=100,
            max_depth=10,
            random_state=42
        ))
    ])
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )
    pipe.fit(X_train, y_train)
    return pipe, (X_test, y_test)
            Calls scikit-learn for model training with full pipeline encapsulation. Feature scaling, model fitting, and train/test split in a single deployable artifact. Used in every ML product Miracuves ships.
          
        
      
    
  

  
  
    
      
        
          
            **Our Service Models**
          
        
        
## Three ways Miracuves delivers your ML project

        
Every engagement is with Miracuves as a company — a complete team, a
          defined process, and full delivery accountability. Choose the model that matches your project stage.

      

      
        
        
          Most Popular
          
            
              
              
                
                
                  
                  
                  
                  
                
              
              
              
                
                
                  
                  
                  
                  
                
              
              
              
                
                
                  
                  
                  
                  
                
              
              
              Data Ingest
              Model API
              MLOps Dash
            
          
          
            
Fixed Scope · Fixed Price

            
### ML Solution Package

            
Miracuves deploys a scoped ML module — data pipeline, trained model, inference API, and monitoring dashboard — in 6–12 weeks. Artifacts fully yours.

            Starting from $3,699 — published anchor price
            Use-case templates: recommendation, fraud, forecasting, churn
            Data pipeline, training, deployment, monitoring included
            MLflow experiment tracking and model registry
            Full source code · model artifacts · NDA · 60-day support
          
        

        
        
          
            
              
                
                
                MLPipeline
                
                
                
                
                
                
                
                
                FeatureStore
                
                Trainer
                
                InferenceAPI
                
                
                
                
                
                ETL
                
                MLflow
                
                Monitor
              
            
          
          
            
Custom Development · Full Pipeline

            
### Custom ML Development

            
Miracuves builds from your specification — custom architecture, custom flows, unique
              features. Full team: engineer, backend, QA, PM.

            Scoped and priced before development begins
            Custom pipeline designed specifically for your data
            Weekly sprint demos — working model every sprint
            Model deployment and API endpoint management
            Full source code · model weights · IP 100% yours
          
        

        
        
          
            
              
                
                  Wk 1
                  
                    
                  
                  
                
                
                  Wk 2
                  
                    
                  
                  
                
                
                  Wk 3
                  
                    
                  
                  
                
                
                  Wk 4
                  
                    
                  
                  
                
              
            
          
          
            
ML Retainer · Monthly

            
### Ongoing ML Development

            
Miracuves works as your ongoing development partner — new features, releases, maintenance
              on a monthly retainer with weekly sprint demos.

            From $2,299/month — cancel with 2 weeks notice
            Dedicated Miracuves ML team assigned to your project
            Direct communication with ML engineers — no account manager relay
            Weekly model performance reports — accuracy, drift, latency metrics
            Scales up or down as your ML needs evolve
          
        
      
    
  

  
  
    
    
      
        
          
            **Quality Standards**
          
        
        
## How Miracuves ensures every ML delivery meets production standard

        
Every project passes through Miracuves' quality gates before handoff —
          not as a checklist, as a non-negotiable delivery standard applied to every codebase we ship.

        
          Modular pipeline — Data Ingestion / Feature Engineering / Training separatedArchitecture
          MLflow experiment tracking — full reproducibility on every runExperimentation
          GPU-accelerated training — distributed computing for large datasetsPerformance
          Holdout validation — tested on unseen data before deploymentValidation
          CI/CD pipeline — automated model training, evaluation, and deploymentMLOps
          No data leakage — strict temporal train/test separation enforcedData Quality
          Production monitoring — drift detection, accuracy tracking, automated alertsMonitoring
        
      
    

    
    
      
        
          
            **Enforced QA Gates**
          
        
        
## Our 6 Continuous Delivery Gateways

        
Every model artifact,
          training pipeline, and inference profile must successfully clear all six quality control gates before repository
          handoff.

        
          
            01
            
              
#### Code Review on Every Pull Request

              
Every line merged into your main branch is reviewed by a senior Miracuves engineer. No untested code
                reaches your production environment under any circumstances.

            
          
          
            02
            
              
#### Automated ML Test Coverage Required

              
Unit tests for feature engineering, integration tests for training pipelines, and contract tests for inference APIs. Minimum coverage enforced before any model is promoted to production.

            
          
          
            03
            
              
#### Holdout Validation Before Production

              
Every model is evaluated on temporally separated holdout data — never on training sets. Inference latency and throughput are profiled under production load before deployment is approved.

            
          
          
            04
            
              
#### Handoff Package — Not Just a Repository

              
Source code, model artifacts, environment setup guide, API documentation, deployment credentials, monitoring dashboards, and post-launch runbook — all included in every project handoff.

            
          
          
            05
            
              
#### Model Registry and Rollback Ready

              
Every deployed model version is registered in MLflow with parameters, metrics, and artifacts. Rollback to a previous version is a configuration change — not a rebuild.

            
          
          
            06
            
              
#### Post-Deployment Monitoring — 60-Day Active Support

              
Grafana dashboards track accuracy, drift, and inference latency from day one. Miracuves monitors model health during the 60-day post-deployment window — proactive retraining recommendations, not reactive firefighting.

            
          
        
      
    
  

  
  
    
      
        
          
            
              **Technology Stack**
            
          
          
## The ML stack Miracuves ships with

        
        
Matched to your architecture and delivery
          requirements — not a one-size-fits-all default.

      

            
        
          TF
          TensorFlow
          Deep learning · production inference
        
        
          PT
          PyTorch
          Research · dynamic computation graphs
        
        
          sk
          scikit-learn
          Classical ML · pipeline API
        
        
          Mf
          MLflow
          Experiment tracking · model registry
        
        
          Kf
          Kubeflow
          Pipeline orchestration · distributed training
        
        
          Py
          Python 3.11+
          Primary language · data science ecosystem
        
        
          Jp
          Jupyter
          Exploratory analysis · prototyping
        
        
          Dk
          Docker
          Containerized model deployment
        
        
          K8
          Kubernetes
          Orchestration · auto-scaling inference
        
        
          SM
          AWS SageMaker
          Managed training · GPU infrastructure
        
        
          Pg
          Postgres
          Feature store · metadata storage
        
        
          Rd
          Redis
          Feature caching · real-time inference
        
        
          FA
          FastAPI
          Model serving · REST API endpoints
        
        
          St
          Streamlit
          ML dashboards · model demos
        
        
          Gr
          Grafana
          Monitoring dashboards · metrics
        
        
          Pr
          Prometheus
          Model monitoring · alerting
        
      
    
  

  
  
    
      
        
          
            **Our Process**
          
        
        
## From brief to deployed ML system — what happens and when

        
Every Machine Learning engagement follows the same delivery spine — whether you
          start from a scoped ML module or a custom architecture. You always know what Miracuves is doing, what data you need to provide, and what gets delivered at each step. Timelines reflect standard ML delivery; complex builds run milestone-based with the same checkpoints.

      

      
        

          
          
            
            
              
              
            

            
            
              
                
                  
                
              
              
### Brief & NDA

              
Share your concept via WhatsApp. NDA signed same day. We ask 6 specific questions.

            

            
            
              
              Step 01
            
          

          
          
            
            
              
              
            

            
            
              
                
                  
                  
                
              
              
### Scope & Plan

              
Right solution base, stack, and model confirmed. No payment before scope is agreed.

            

            
            
              Step 02
              
            
          

          
          
            
            
              
              
            

            
            
              
                
                  
                  
                  
                
              
              
### Build & Demo

              
Repo created, architecture set. First commit in 24h. Weekly working demo runs.

            

            
            
              
              Step 03
            
          

          
          
            
            
              
              
            

            
            
              
                
                  
                  
                  
                
              
              
### QA & Polish

              
Holdout validation, latency profiling, and drift checks on staging data mirroring production.

            

            
            
              Step 04
              
            
          

          
          
            
            
              
            

            
            
              
                
                  
                
              
              
### Launch & Handoff

              
Model artifacts, pipeline code, API docs, and monitoring dashboards delivered. 60 days active support.

            

            
            
              
              Step 05
            
          
        
      

      
        **Same Day**NDA turnaround
        **6–12 Weeks**Standard ML delivery
        **24 Hours**First commit after scope
        **60 Days**Post-launch support
      
    
  

  
  
    
      
        
          
            **Transparent Pricing**
          
        
        
## What ML development costs at Miracuves

        
We publish prices because we are confident in what we deliver. No
          "contact us for pricing" pages. No hidden fees after scope is agreed.

      

      
        
        
          
### Scoped ML Module

          $3,699
              from
          
Fixed scope · 6–12 week delivery · published anchor

          
- ML solution — data pipeline + trained model + API
- Admin panel included as standard
- Branding and white-label applied
- Full source code on handoff
- 60-day post-launch support
- NDA protected from day one

          [Start an
            ML Project](https://wa.me/919830009649)
        

        
        
          Most Requested
          
### Custom ML Development

          Custom Quote
          
Scoped before build · milestone billing

          
- Full ML team — ML engineer + data engineer + MLOps
- Custom architecture for your spec
- Weekly sprint demos — working software
- Production deployment — Kubernetes, SageMaker, or edge
- Full source code · complete IP transfer
- Milestone billing — no pay before delivery

          [Get a
            Scope & Quote](https://miracuves.com/contact/)
        

        
        
          
### Ongoing Development

          $2,299/mo
          
Monthly retainer · cancel with 2 weeks notice

          
- Miracuves team assigned to your product
- New features, releases, and maintenance
- Weekly demos and sprint planning
- Direct communication — no relay
- Scales up or down as needed
- All code remains 100% yours

          [Discuss
            Ongoing Work](https://wa.me/919830009649)
        
      

      **Why Miracuves publishes prices:** Clients who
        understand cost upfront make better product decisions. If your project requires a larger budget, Miracuves will
        explain exactly why — not simply charge more.

      
        
          
What affects ML project cost at Miracuves

          
Scoped ML module pricing stays fixed when the use case matches a proven template (recommendation, churn, forecasting). Custom ML builds scale with: data volume and quality, model complexity (classical vs deep learning), real-time inference requirements, number of data sources, MLOps scope (retraining cadence, A/B testing), compliance (HIPAA, PCI), and edge vs cloud deployment.

        
        
          
Typical Machine Learning budget ranges

          
**Scoped ML module:** from $3,699 · 6–12 weeks.  

            **Custom ML platform:** $18,000–$80,000 · 10–20 weeks depending on scope.  

            **Ongoing retainer:** from $2,299/month for feature work and maintenance.  

            Every quote is written before payment — no surprise invoices after kickoff.

        
      
    
  

  
  
    
    
      
        
          
            **Client Reference**
          
        
        
## What a real ML project looks like at Miracuves

        
A manufacturing company with 500+ machines across three factories needed to reduce unplanned downtime. Their IoT sensor data was siloed, and maintenance was purely reactive — costing $2M+ annually in lost production.

        
          
            01
            
              
The Challenge

              
Five hundred machines generating 2TB of sensor data monthly with no centralized pipeline. Different sensor formats, missing timestamps, and no historical labeling of failure events. Needed a predictive system that could identify failures 48+ hours in advance.

            
          
          
            02
            
              
What Miracuves Delivered

              
Built a TensorFlow-based anomaly detection pipeline: IoT data ingestion via MQTT, feature engineering with scikit-learn, LSTM autoencoder for sequence anomaly detection, and a Grafana dashboard for real-time monitoring. Deployed on Kubernetes with automated retraining every 24 hours.

            
          
          
            03
            
              
Outcome

              
60% reduction in unplanned downtime in the first quarter. Identified bearing failures 72 hours before breakdown. System paid for itself within 4 months of deployment. Retrained daily with new sensor data — model accuracy improved from 82% to 94% over 6 months.

            
          
        

        
          **60%**Downtime reduction
          **8 Weeks**Full deployment
          **94%**Model accuracy
        
        [View All Case Studies →](https://miracuves.com/portfolio/)
      
    

    
    
      
        
        
          
Client Testimonial

          
"We had been dealing with unexpected machine breakdowns for years — each one costing us $15K+ in lost production. Miracuves didn't just build a model; they built an entire pipeline from our factory floor sensors to a dashboard our maintenance team could act on. The 60% downtime reduction exceeded our target. Their ML team understood our industrial context from day one."

          
            MR
            
              
M.R., VP of Operations

              
Manufacturing · Factory Predictive Maintenance

            
          
          [⭐ Read Clutch & Google Reviews →](https://miracuves.com/reviews-awards/)
          
        

        
        
          
Project Brief

          Solution typePredictive Maintenance (ML)
          Delivery timeline8 weeks
          InfrastructureTensorFlow + Kubernetes
          Key integrationsMQTT · IoT Sensors · Grafana
          Data volume2TB/month · 500 machines
          Source code100% client-owned
        

        
        
          
            -60%
            Unplanned downtime
          
          
            72h
            Early warning time
          
          
            $2M+
            Annual savings
          
        
      
    
  

  
  
    
      
        
          
            **Client Reviews**
          
        
        
## What clients say about Miracuves ML development

        
Across predictive maintenance, fraud detection, recommendation engines, and NLP projects — from manufacturing to fintech — verified on Clutch and Google.

      

      
        
        
          ★★★★★
          
Clutch · Predictive Maintenance

          
"Miracuves deployed a predictive maintenance system for our factory floor that reduced downtime by 60% in the first quarter. The TensorFlow pipeline ingests sensor data from 500 machines and predicts failures 72 hours in advance. Our maintenance team now works proactively instead of firefighting. The ROI was visible within 4 months."

          
            MR
            
              
M.R., VP of Operations

              
Manufacturing · 500-Machine Factory

            
          
          TensorFlow · Predictive Maintenance · IoT
        

        
        
          ★★★★★
          
Google Reviews · Fintech Platform

          
"We needed a real-time fraud detection system for our payment platform processing 50K transactions daily. Miracuves built an anomaly detection pipeline that flags suspicious transactions within 200ms. Our fraud loss dropped 80% in the first month. Their understanding of both ML engineering and production deployment was exceptional."

          
            SK
            
              
S.K., CTO

              
Payment Platform · South-East Asia

            
          
          PyTorch · Fraud Detection · Real-time
        

        
        
          ★★★★★
          
Clutch · Recommendation Engine

          
"Miracuves built our product recommendation engine from scratch — collaborative filtering with real-time personalization. A/B testing showed a 35% increase in click-through rates and 22% improvement in average order value. The ML team set up monitoring dashboards so we can track model performance ourselves. Professional and technically excellent."

          
            AL
            
              
A.L., Head of Product

              
E-commerce Platform · North America

            
          
          scikit-learn · Recommendation · A/B Testing
        
      

      
      
        
          **4.9 / 5.0**
          Clutch average rating
        
        
        
          **4.8 / 5.0**
          Google average rating
        
        
        
          **Top Developer**
          Clutch recognition · 2024–2025
        
        
        [Read All Reviews →](https://miracuves.com/reviews-awards/)
      
    
  

  
  
    
      
        
          
            **Related Services**
          
        
        
## Also building intelligent systems with these technologies at Miracuves

      
      
        [AI Platform

AI Development](https://miracuves.com/service/artificial-intelligence-development/)
        [Deep Learning

TensorFlow Development](https://miracuves.com/service/tensorflow-development/)
        [Language

Python Development](https://miracuves.com/service/python-development/)
        [Analytics

Data Science](https://miracuves.com/service/data-science/)
        [Pipelines

Data Engineering](https://miracuves.com/service/data-engineering-app-development/)
        [Vision

Computer Vision Development](https://miracuves.com/service/computer-vision-development/)
        [NLP

NLP Development](https://miracuves.com/service/nlp-development/)
        [Generative AI

LLM Development](https://miracuves.com/service/llm-development/)
      
    
  

  
  
    
      
        
          
            **Frequently Asked**
          
        
        
## Questions about ML development at Miracuves

      

      
        
          
Does Miracuves build custom ML models or use pre-built ones?

          
Miracuves builds custom models tailored to your data and use case. While we leverage proven frameworks like TensorFlow, PyTorch, and scikit-learn, every model is trained on your specific data with your specific success metrics. We do not deploy generic pre-built models as production solutions unless your use case genuinely calls for it.

        
        
          
What data do I need to provide for an ML project?

          
The data requirements vary by use case, but typically include historical records relevant to the prediction target — transaction logs for fraud detection, sensor readings for predictive maintenance, user behavior data for recommendation engines. Miracuves helps assess your data readiness in the discovery phase and can recommend data collection strategies if gaps exist.

        
        
          
How long does it take to deploy an ML solution?

          
A scoped ML solution — data pipeline, model training, API deployment, and monitoring dashboard — ships in 6–12 weeks depending on data complexity and model requirements. Custom builds with unique architectures take 10–16 weeks. All timelines include testing, validation, and deployment. Timelines are stated in writing before any payment.

        
        
          
What is included in the ML monitoring and maintenance?

          
Every ML delivery includes a monitoring dashboard (Grafana) tracking model accuracy, prediction latency, data drift, and system health. Automated alerts trigger when performance degrades. Miracuves includes 60 days of post-deployment support. Model retraining and ongoing MLOps are available through our monthly retainer model.

        
        
          
Can Miracuves deploy ML models on edge devices?

          
Yes. We support edge deployment using TensorFlow Lite and ONNX Runtime for devices with limited compute. Computer vision models, in particular, are optimized for edge inference with quantization and pruning. For cloud deployment, models run as containerized microservices on Kubernetes with auto-scaling.

        
        
          
Do I need a large dataset to work with Miracuves?

          
Not necessarily. Miracuves works with datasets of all sizes. For smaller datasets, we use classical ML approaches (scikit-learn) with careful cross-validation to avoid overfitting. For larger datasets, we leverage deep learning with GPU acceleration. If data is truly insufficient, we will recommend starting with data collection before model development.

        
        
          
How does Miracuves handle model versioning and reproducibility?

          
Every model experiment is tracked in MLflow — parameters, metrics, and artifacts are logged and versioned. The full pipeline is defined as code (Kubeflow), meaning any previous version can be fully reproduced. Model registry maintains a history of deployed versions with rollback capability. This is a non-negotiable standard on every project.

        
        
          
What happens if model accuracy degrades after deployment?

          
Monitoring dashboards track accuracy and drift continuously. If degradation is detected, Miracuves investigates root cause — data drift, concept drift, or infrastructure issues. Under the 60-day post-deployment support window, corrective retraining is included. For ongoing needs, our monthly retainer covers model retraining, pipeline updates, and continuous improvement.

        
      
    
  

  
  
    
      
        
        
          
            
              **Get Started**
            
          
          
## Ready to build your ML system with Miracuves?

          
Tell Miracuves what you are building. We will confirm the right
            solution base, service model, and delivery timeline — in writing, before any commitment is required from
            you.

          
            **30+**ML systems delivered
            **6–12 Weeks**ML delivery
            **100%**Source code yours
            **Same Day**NDA turnaround
          
        

        
        
          [WhatsApp — Start Now](https://wa.me/919830009649)
          [Contact &
              Brief Form](https://miracuves.com/contact/)
          
NDA signed before we discuss your project details

        
      
      
        
          
          
        
        Page reviewed by the Miracuves ML Engineering Team · Last updated May 2026 · [Clutch & Google Reviews](https://miracuves.com/reviews-awards/)
      
    
  

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