Available Now · 50+ AI Solutions

AI Development Company

Machine Learning · Deep Learning · NLP · Computer Vision

Miracuves is an AI development company that builds intelligent applications using machine learning, deep learning, natural language processing, and computer vision. From intelligent automation to generative AI, we deliver production-ready AI solutions with complete source code ownership and full IP safety.

200+ AI Deployments 40+ AI Engineers 100% Source Ownership NDA Day One
Clutch Reviewed 4.9★ · Starting from $3,699 · View live deployments
Miracuves Delivery RecordAI Team
6–14w
Delivery timeline
$3,699
Starting price
50+
AI solutions
100%
IP assignment
AI engineers active right now
ML Pipeline Console ACTIVE
FRAMEWORK TensorFlow / PyTorch
MODEL OPS MLflow Tracking
INFERENCE 98% Avg. Precision
DEPLOYMENT Docker / K8s / SageMaker
35+ AI EngineersDedicated ML/DL/NLP/CV specialists
40+ AI SolutionsDeployed by Miracuves
6–14 WeeksDelivery timeline
TF + PyTorchCertified engineering team
96% Model Acc.Average production precision

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

More than 3900+ Companies Trust us Worldwide
Our AI Approach

How Miracuves delivers AI solutions — from 200+ projects of real experience

After delivering 200+ AI deployments, Miracuves has a proven methodology for building intelligent applications. We start from 50+ production-grade AI modules — already trained, validated, and integrated with data pipelines, APIs, and deployment infrastructure — not from a blank notebook.

Our approach covers the full AI lifecycle: problem definition, data preparation, model development, deployment, and ongoing monitoring. Every project ships with complete documentation, model cards, training pipelines, and inference APIs.

Who this service is built for: Founders and product teams building intelligent applications — chatbots, recommendation engines, document processors, predictive analytics, content generators, and computer vision systems. Miracuves AI development fits when you need a production-ready AI solution with published pricing, full IP ownership, and a company accountable for delivery. If your problem can be solved with a simple rules-based system or off-the-shelf API, we will say so upfront.

Problem definition framework — is this a classification, regression, NLP, or CV problem?
Data pipeline built on every project — ingestion, cleaning, feature engineering, versioning
Model experimentation tracked via MLflow — every experiment logged and reproducible
CI/CD for ML — automated training, evaluation, and deployment pipelines from first commit
Model monitoring and drift detection — production observability from day one

From our AI team — Document Processing Platform, 10 weeks

"Enterprise-grade document intelligence pipeline processing invoices, contracts, and forms. Used LayoutLM for document understanding, deployed as a FastAPI service with Redis caching and PostgreSQL storage. Achieved 85% automation rate for document classification and 92% extraction accuracy on structured fields. Delivered with full training pipeline, model card, and API documentation."

Written by the Miracuves AI Team · June 2026 · View Deployed Portfolio →
200+
AI projects deployed across industries
96%
Average model accuracy in production
60%
Faster deployment vs building from scratch
50+
Pre-built AI solution modules
6–14w
Miracuves delivery for scoped AI projects
98%
Client satisfaction rate
Data Prep
ETL + Feature Engineering
Model Dev
Training + Evaluation
Deployment
API + Container + Monitor

Why AI at Miracuves

Time to production MVP6–14 weeks
Model types supportedML · DL · NLP · CV
Cost saving vs in-house teamUp to 50%
Pre-built AI modules ready50+ solutions
Production accuracy96% avg. precision
Source code ownership100% yours
Technology Comparison

AI Development vs Off-the-Shelf API vs DIY Data Science — which is right for your project?

Most AI companies avoid this question because they only sell one approach. Miracuves answers it honestly — your AI strategy determines long-term cost, accuracy, and maintenance.

Metric AI Development (Miracuves)
← MIRACUVES DEFAULT
Off-the-Shelf API DIY Data Science Team
Model Accuracy 96% avg. — custom trained on your data 70–85% — generic, not tuned to your domain Variable — depends on team expertise
Time to Production 6–14 weeks — pre-built modules accelerate Days — quick integration, limited capability 6–12 months — hire, build infra, iterate
Customization Full — model architecture, data, training all yours Limited — only what the API exposes Full — complete control over every decision
Data Privacy 100% — your data stays on your infrastructure Shared — data sent to third-party servers Full — complete control over data
Best For Custom AI · high accuracy · IP ownership Quick prototypes · simple tasks Large teams · research · long timelines

Choose AI Development if…

You need high accuracy trained on your proprietary data · full IP ownership of models · customization beyond what APIs offer · production-ready with monitoring and retraining.

Consider an alternative if…

Your use case is simple and covered by an existing API · you have a full in-house data science team already · your accuracy requirements are low. We will tell you honestly →

Technical Architecture

How Miracuves engineers structure ML pipelines for production

These are the specific decisions our engineering team makes on every AI project — choices that determine whether a model ships on time and maintains accuracy in production.

Architecture — Modular ML Pipeline

Strict separation: Data Ingestion → Feature Engineering → Model Training → Evaluation → Deployment → Monitoring. Every stage is independently testable, versioned, and deployable. This is how Miracuves delivers a new AI module in 6 weeks without rearchitecting the pipeline.

Experiment Tracking — MLflow for Every Run

Every training run is logged with parameters, metrics, artifacts, and environment. The most common problem inherited from other teams: models trained in notebooks with no reproducibility. We eliminate this on day one as a hard standard — not a suggestion.

Inference — Containerized with Monitoring

Models are packaged in Docker containers with REST APIs via FastAPI. Every endpoint has Prometheus metrics for latency, throughput, and prediction drift. We monitor for data drift and trigger retraining automatically when accuracy drops below threshold.

What most AI agencies get wrong

Training on the full dataset without validation splits. No feature store. Models that pass tests but fail in production due to data drift. No monitoring or retraining pipeline. Miracuves has inherited every one of these — starting correctly is always faster than cleaning up.

inference_api.py — FastAPI Model Server
# Production inference endpoint with monitoring # Used in all Miracuves AI deployments from fastapi import FastAPI, HTTPException from pydantic import BaseModel import joblib, numpy as np app = FastAPI(title="Miracuves Inference API") class PredictionRequest(BaseModel): features: list[float] class PredictionResponse(BaseModel): prediction: float confidence: float model_version: str model = joblib.load("model/prod/model.pkl") scaler = joblib.load("model/prod/scaler.pkl") @app.post("/predict", response_model=PredictionResponse) async def predict(req: PredictionRequest): try: X = scaler.transform(np.array(req.features).reshape(1, -1)) pred = model.predict(X)[0] proba = model.predict_proba(X).max() return PredictionResponse( prediction=float(pred), confidence=float(proba), model_version="v2.1.0" ) except Exception as e: raise HTTPException(status_code=500, detail=str(e))
Containerized with Docker, deployed on AWS SageMaker or GCP Vertex AI. Metrics exported to Prometheus for drift monitoring and automated retraining triggers.
Our Service Models

Three ways Miracuves delivers your AI 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
Customer
Driver App
Admin

Readymade Clone · Fixed Price

White-Label Clone Delivery

Miracuves deploys a production-grade clone under your brand — iOS + Android + Admin Panel — in 3–9 days. Source code fully yours.

Starting from $2,499 — fixed price, no surprises
90+ solutions matched to your vertical
Branding, configuration, white-labelling applied
Admin panel included in every delivery
Full source code · NDA · 60-day support
Pipeline Training Repository UI Layer Events API / Cache Widgets

Custom Development · Scoped

Custom AI Build

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

Scoped and priced before development begins
Clean architecture designed specifically for your product
Weekly sprint demos — working software every sprint
App Store and Play Store submission managed
Full source code · IP 100% yours
Wk 1
Wk 2
Wk 3
Wk 4

Ongoing Retainer · Monthly

Ongoing AI 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 team assigned to your product
Direct communication — no account manager relay
Weekly sprint demos — deliverables every cycle
Scales up or down as your product evolves
Quality Standards

How Miracuves ensures every AI 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.

Clean architecture — Presentation / Domain / Data separatedArchitecture
MLflow experiment tracking — every run logged and reproducibleState
Automated ML pipeline — data ingestion to deployment in one workflowPerformance
Physical device QA — tested on real iOS and Android hardwareQA
CI/CD pipeline — automated builds and tests from day oneDevOps
No secrets in source — API keys in environment config onlySecurity
App Store-ready — provisioning, compliance, review passedDelivery
Enforced QA Gates

Our 6 Continuous Delivery Gateways

Every line of code, asset asset, and build 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 Test Coverage Required

Unit tests for business logic, widget tests for UI components, and integration tests for critical user flows. Minimum coverage enforced before any release build is created.

03

Release Builds Profiled — Not Debug Builds

Miracuves profiles performance using AI DevTools on every release build. Debug build performance is not representative of what users experience and is never accepted as sufficient.

04

Handoff Package — Not Just a Repository

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

05

App Store Submission — Full Compliance Managed

Miracuves handles provisioning profiles, signing certificates, store listing creation, screenshot assets, compliance checks, and App Store review coordination for both iOS and Android.

06

Post-Launch Monitoring — 60-Day Active Support

Crashlytics and Firebase Analytics configured pre-launch. Miracuves monitors crash rates and performance metrics during the 60-day post-launch support window — proactive, not reactive.

Technology Stack

The AI stack Miracuves ships with

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

TensorFlow
Deep learning · production serving
PyTorch
Research · dynamic computation
OpenAI API
LLMs · embeddings · GPT models
LangChain
LLM orchestration · RAG pipelines
Hugging Face
Transformers · pre-trained models
MLflow
Experiment tracking · model registry
Python 3.12
Primary language · ML ecosystem
Jupyter
Exploration · prototyping · viz
Docker
Containerization · reproducible envs
Kubernetes
Orchestration · auto-scaling
AWS SageMaker
Training · deployment · hosting
GCP Vertex AI
ML platform · autoML · pipelines
PostgreSQL
Relational data · feature store
Redis
Caching · rate limiting · queues
FastAPI
REST API · async inference
Streamlit
Dashboards · model demos · monitoring
Our Process

From brief to deployed AI app — what happens and when

Every AI engagement follows the same delivery spine — whether you start from a readymade clone or a custom spec. You always know what Miracuves is doing, what you need to provide, and what gets delivered at each step. Timelines below reflect our standard clone sprint; custom 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

Tested on real iOS/Android devices. Profiles optimized for Store guidelines.

Step 04

Launch & Handoff

Full code and docs delivered. Store submissions handled. 60 days active support.

Step 05
Same DayNDA turnaround
3–9 DaysMVP Sprint delivery
24 HoursFirst commit after scope
60 DaysPost-launch support
Transparent Pricing

What AI 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.

Readymade Clone

$2,499 from

Fixed price · 3–9 day delivery · scoped

  • AI app — iOS + Android
  • 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 a Clone Project
Most Requested

Custom AI Build

Custom Quote

Scoped before build · milestone billing

  • Full AI team — engineer + backend + QA
  • Custom architecture for your spec
  • Weekly sprint demos — working software
  • App Store and Play Store submission
  • Full source code · complete IP transfer
  • Milestone billing — no pay before delivery
Get a Scope & Quote

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
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 AI project cost at Miracuves

Readymade clone pricing stays fixed when scope matches the base product. Custom AI builds scale with: number of user roles (customer, driver, admin, vendor), real-time features (live GPS, chat, video), payment and compliance integrations (BaaS, KYC, multi-currency), multi-city or multi-language rollout, and third-party SDKs beyond the standard stack.

Typical AI budget ranges

Readymade clone: from $2,499 · 3–9 days.
Custom MVP: $8,000–$25,000 · 4–10 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 AI project looks like at Miracuves

A US-based logistics company needed to automate invoice processing, contract extraction, and compliance document classification — processing 50,000+ documents per month with high accuracy.

01

The Challenge

Manual document processing across 12 departments was taking 200+ hours per week with 15% error rates. Off-the-shelf OCR APIs failed on handwritten notes and multi-language invoices. Needed a custom AI solution that could handle diverse document formats.

02

What Miracuves Delivered

Custom document intelligence pipeline using LayoutLM for document understanding, Tesseract OCR with custom post-processing for handwriting, and a fine-tuned BERT model for entity extraction. Deployed as a FastAPI service with Redis caching and PostgreSQL for audit trail.

03

Outcome

85% automation rate for document classification. 92% extraction accuracy on structured fields. Processing time reduced from 200 hours to 18 hours per week. Full training pipeline, model cards, and API documentation delivered.

85%Automation rate
92%Extraction accuracy
12Departments deployed
View All Case Studies →

Client Testimonial

"We were drowning in manual document processing — 200 hours a week across 12 teams. Miracuves built an AI pipeline that handles 85% of it automatically. The model handles handwritten notes on invoices that every off-the-shelf API failed on. Our operations team went from firefighting to strategic work."

MK

M.K., VP Operations

US Logistics Platform · Document Automation

Project Brief

Solution typeDocument Intelligence Pipeline
Delivery timeline10 weeks
Models usedLayoutLM · BERT · Tesseract OCR
Key integrationsFastAPI · Redis · PostgreSQL
Doc volume50,000+/month
Source code100% client-owned
85%
Automation rate
92%
Extraction accuracy
90%
Cost reduction
Client Reviews

What clients say about Miracuves AI development

Across document processing, recommendation engines, and conversational AI — from enterprise teams to funded startups — verified on Clutch and Google.

★★★★★

Clutch · Document Intelligence

"Miracuves delivered a document processing AI that handles invoices, contracts, and forms our team was spending 200 hours a week on. The model accuracy on handwritten fields surprised our ops team. FastAPI deployment with Redis caching made integration trivial. Full pipeline documentation was delivered alongside the code."

MK

M.K., VP Operations

Logistics Automation · United States

AI · Document Processing · LayoutLM · FastAPI
★★★★★

Google Reviews · Recommendation Engine

"We needed a recommendation engine for our e-commerce platform that could personalise across 50,000+ products. Miracuves built a hybrid collaborative filtering and content-based system that increased our average order value by 34% in the first month. A/B testing infrastructure was built in from day one."

SL

S.L., CTO

E-Commerce Platform · Singapore

AI · Recommendation Engine · Collaborative Filtering
★★★★★

Clutch · Conversational AI

"Launched a multi-LLM customer support chatbot with RAG architecture in 8 weeks. Miracuves handled everything from data preprocessing to deployment on AWS SageMaker. The admin dashboard gives us full visibility into conversation analytics, and the retrieval-augmented generation ensures accurate, grounded responses."

RJ

R.J., Head of Product

Fintech Platform · United Kingdom

AI · Chatbot · RAG · Multi-LLM · AWS
4.9 / 5.0 Clutch average rating
4.8 / 5.0 Google average rating
Top Developer Clutch recognition · 2024–2025
Read All Reviews →
Frequently Asked

Questions about AI development at Miracuves

What types of AI solutions does Miracuves build?

Miracuves builds a wide range of AI solutions: conversational AI chatbots and assistants, recommendation engines, document processing pipelines, predictive analytics systems, content generation platforms, and computer vision applications. Each solution starts from one of 50+ pre-built AI modules and is customised to your specific data and use case.

Does Miracuves deliver the full model source code and training pipeline?

Yes — completely. Miracuves delivers the full model code, training pipeline, experiment logs, model card, inference API, and deployment configuration. Zero lock-in. Your team or any other AI team can retrain, fine-tune, or extend the models immediately after handoff.

How long does it take to deliver a production AI solution?

A scoped AI deployment — covering model development, training, evaluation, API deployment, and monitoring — ships in 6–14 weeks depending on complexity. Custom models requiring novel architecture or large dataset training take 12–20 weeks. All timelines are stated in writing before any payment is requested.

AI Development vs off-the-shelf API — which does Miracuves recommend?

For most custom solutions needing high accuracy, data privacy, and full IP ownership — custom AI development is right. Miracuves recommends off-the-shelf APIs honestly when your use case is simple, covered by existing APIs, and accuracy requirements are moderate. We will tell you which fits before any commitment.

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

For most projects, Miracuves needs labelled or unlabelled data relevant to your use case — historical records, documents, images, text, or user behaviour data. If you do not have sufficient data, Miracuves can help with data collection strategies, synthetic data generation, or transfer learning from pre-trained models.

How does Miracuves handle model monitoring and retraining?

Every Miracuves AI deployment includes Prometheus-based monitoring for prediction drift, data drift, latency, and throughput. Automated retraining pipelines are configured to trigger when accuracy drops below a configurable threshold. You receive alerts and can review retraining results before promoting to production.

Can Miracuves integrate AI into my existing application?

Yes. The majority of Miracuves AI deployments integrate with existing applications via REST APIs, message queues, or streaming pipelines. Whether you have a mobile app, web platform, or enterprise system, the AI service layer is designed to plug into your existing architecture with minimal changes.

How does Miracuves handle NDA and data confidentiality for AI projects?

Miracuves signs a bilateral NDA before any project details are shared. All training data stays on your infrastructure or in your cloud account. Model artifacts are delivered with full ownership transfer. An IP assignment agreement confirming 100% ownership is signed at project start — not at the end.

Get Started

Ready to build your AI solution with Miracuves?

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

200+AI deployments
6–14 WeeksDelivery timeline
100%Model IP yours
Same DayNDA turnaround
WhatsApp — Start Now Contact & Brief Form

NDA signed before we discuss your project details

Page reviewed by the Miracuves AI Development Team · Last updated June 2026 · Clutch & Google Reviews