Available Now · 25+ GenAI Products

Generative AI App Development Company

LLM Apps · RAG Pipelines · AI Agents · Copilots

Miracuves is a generative AI development company that builds production LLM applications - RAG knowledge assistants, AI agents, content generators, and ChatGPT-style products - with LangChain, OpenAI, Claude, and vector databases. You get grounded responses, guardrails, observability, and complete source code ownership from $3,699.

40+ GenAI Deployments RAG + Agent Delivery 100% IP Ownership NDA Day One
WhatsApp - Talk to Our Team Explore GenAI Solutions
Clutch Reviewed 5.0★ · Starting from $1,899 · View GenAI deployments
generate4 VARIANTS
promptProduct hero, studio light, brand palette
1seed 8841
2seed 8842
3seed 8843
4seed 8844
GenAI Stack PoweredGenAI Stack Powered OpenAI · Claude · LangChain · LlamaIndex · Pinecone · Hugging Face
9,000+Projects delivered
Your assetsNever used for training
4-10 WeeksDelivery timeline
$3,699Starting price
25+GenAI Products Shipped
100%Source code ownership
GenAI engineers active right now
OpenAIAnthropicDiffusionEvals9,000+ deliveredNDA day one
Related servicesAI Agent DevelopmentTensorFlow DevelopmentDocker & KubernetesGrocery Delivery App Development
LLM · RAG · AgentsProduction GenAI applications
25+ GenAI ProductsDeployed by Miracuves
LangChain · LlamaIndexOur orchestration standard
4-10 WeeksTypical RAG + agent sprint
100% Source CodeDelivered on handoff

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 5.0★

Third-party verified
Our GenAI Approach

How Miracuves delivers generative AI products - from 25+ production deployments

After shipping 25+ production GenAI applications across SaaS, legal tech, and customer support, Miracuves has a specific methodology for LLM delivery. We start from proven modules - document ingestion, embedding pipelines, retrieval with reranking, agent tool routing, and evaluation harnesses - not from a blank ChatGPT API call in a demo script.

Generative AI only creates business value when responses are grounded in your data, guarded against hallucination, and observable in production. Miracuves pairs LangChain or LlamaIndex orchestration with vector stores and your choice of OpenAI, Claude, or open-weight models - full source code and prompt assets yours on handoff.

Who this service is built for: SaaS founders, enterprise product teams, and support leaders who need LLM-powered assistants, internal knowledge search, content generation, or multi-step AI agents - not a thin API wrapper with no evals. Miracuves generative AI development fits when you need RAG over proprietary documents, role-based access, audit trails, and a company accountable for production reliability. If classical predictive ML is the better fit, we recommend our machine learning development or LLM development services instead.

RAG pipelines - chunking, embeddings, vector search, reranking, and citation-backed answers
LangChain and LlamaIndex for agent workflows, tool calling, and multi-step reasoning
Guardrails - PII redaction, prompt injection filters, toxicity checks, and human-in-the-loop review hooks
LLM observability - LangSmith or custom tracing for latency, cost, and answer quality per session
Deployment on AWS, Azure, or VPC - API keys in secrets manager, not client bundles

From our GenAI team - EU B2B SaaS knowledge assistant, 5 weeks

"12,000 Confluence pages, no internal search that worked, and sales engineers wasting hours finding pricing docs. Miracuves built a RAG assistant with Pinecone, Claude, and role-based document access - answers cite the source paragraph. Support ticket deflection rose 38% in month one. Delivered on week 5 for their enterprise rollout."

Written by the Miracuves Generative AI Team · May 2026 · View Deployed Portfolio →
25+
GenAI products in production
38%
Avg support deflection (RAG assistants)
4-10
Weeks from brief to production API
$3,699
Published starting investment anchor
100%
Prompt + pipeline code ownership
RAG
Grounded answers with citations
LLM Apps
Chat · Copilot
RAG
Vector search
Agents
Tool routing

Why GenAI at Miracuves

Time to first RAG MVP4-6 weeks
Model flexibilityOpenAI · Claude · OSS
Grounding standardCitations on every answer
Clone-ready baseChatGPT product from $3,699
Eval harnessRegression before each release
Source code ownership100% yours
25+ GenAI Products Deployed

What Miracuves builds - production GenAI you can ship

View ChatGPT Clone →
6 Days01

Generative AI

ChatGPT Clone

Chat UI with conversation history, streaming responses, admin panel, and OpenAI API integration - white-label ready.

From $2,499React + NodeOpenAI
6 Days02

PROFESSIONAL NETWORK

Linkedin Clone

Document ingestion, Pinecone retrieval, citation-backed answers, and SSO - grounded in your Confluence, PDFs, or SharePoint.

From $3,399LangChainClaude
6 Days03

FREELANCE

Upwork Clone

Brand-voice templates, SEO briefs, multi-format output, and approval workflows for marketing teams.

From $2,199GPT-4oTemplates
6 Days04

FREELANCE

Fiver Clone

LangChain agents with CRM, calendar, and ticketing tool access - research, draft, and execute multi-step tasks.

From $2,199AgentsTool APIs
6 Days05

COMMUNICATION

Skype Clone

Stable Diffusion or DALL·E integration, prompt library, gallery, and credit-based billing for creative platforms.

From $1,899SDXLCredits
6 Days06

VIDEO CONFERENCING

Zoom Clone

IDE plugin or web copilot grounded in your codebase - repo indexing, PR summaries, and secure on-prem options.

From $1,899RAGPrivate repo
Honest note: Generative AI excels when you have documents, workflows, or user-facing chat needs - not when a simple search index or rule engine would suffice. Miracuves evaluates retrieval quality, cost per token, and compliance requirements before recommending an LLM stack. We will tell you if a non-GenAI approach fits better.
Technology Comparison

Custom GenAI pipeline vs API wrapper vs DIY - which fits your project?

Most vendors sell a ChatGPT iframe and call it done. Miracuves answers honestly - production GenAI needs retrieval, evals, guardrails, and observability. Your architecture choice determines accuracy, cost, and compliance.

Metric Miracuves GenAI · RAG + Agents
← MIRACUVES DEFAULT		 Off-the-Shelf Wrapper DIY Prompt Engineering
Answer accuracy Grounded - citations from your data Generic - no proprietary context Variable - no systematic eval
Hallucination control RAG + reranking + guardrails Minimal - raw LLM output Prompt-only - drifts over time
Time to production 2-8 weeks - full pipeline deployed Fast - days for basic chat UI Slow - no MLOps for LLMs
Compliance & audit PII filters · access control · trace logs Limited - vendor-dependent Ad hoc - rarely production-grade
Best for Enterprise RAG · agents · owned IP Quick demos · internal experiments One-off prototypes · no scale plan

Choose Miracuves GenAI if…

You need answers grounded in your documents · multi-step agents · published pricing and full code ownership · eval harness before go-live.

Consider an alternative if…

A no-code chatbot widget is enough · you only need classical predictive ML without LLMs · or you lack documents/data to ground retrieval. See ML Development → · See AI Development →

Technical Architecture

How Miracuves engineers structure GenAI projects for production

These are the specific decisions our generative AI engineering team makes on every project - choices that determine whether an LLM app stays accurate in production or becomes an expensive chatbot that hallucinates.

Architecture - Ingest → Embed → Retrieve → Generate

Document loaders normalize PDFs, HTML, and wiki exports. Chunking respects semantic boundaries. Embeddings land in Pinecone or Weaviate with metadata filters for tenant and role. Retrieval uses hybrid search plus cross-encoder reranking before the LLM sees context.

Orchestration - LangChain with explicit tool contracts

Agents declare tools with typed schemas - CRM lookup, calendar booking, ticket creation. We ban unbounded agent loops without max-step limits and cost caps. Prompt templates are versioned in git, not edited live in production dashboards.

Evaluation - Regression suite before every deploy

Golden question sets measure faithfulness, citation accuracy, and latency. LangSmith or custom eval runners block releases when scores drop. The most common inherited problem: teams that shipped ChatGPT wrappers with zero eval harness.

What most GenAI agencies get wrong

Stuffing whole PDFs into the prompt. No reranking. API keys in frontend bundles. No citation requirements. Zero cost monitoring per user session. Miracuves has inherited all of these - starting with RAG discipline is faster than rebuilding trust with users.

rag_chain.py - LangChain RAG
# Production RAG chain with citationsn# Used in enterprise knowledge assistants from langchain_openai import ChatOpenAI, OpenAIEmbeddingsnfrom langchain_pinecone import PineconeVectorStorenfrom langchain.chains import create_retrieval_chainnndef build_rag_chain(index_name: str, tenant_id: str): llm = ChatOpenAI(model="gpt-4o", temperature=0) store = PineconeVectorStore( index_name=index_name, embedding=OpenAIEmbeddings(), namespace=tenant_id, ) retriever = store.as_retriever(search_kwargs={"k": 8}) return create_retrieval_chain(retriever, llm)
Tenant-scoped vector retrieval with LangChain - every answer traceable to source chunks. Used in every RAG product Miracuves ships.
Our Service Models

Three ways Miracuves delivers your GenAI 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
Chat UI
LLM API
Admin

Fixed Scope · Fixed Price

ChatGPT Clone Package

Miracuves deploys a white-label ChatGPT-style web app - streaming chat, conversation history, admin panel, and OpenAI integration - in 3-4 weeks. Full source code yours.

Starting from $3,699 - published anchor price
React frontend + Node backend + admin console
OpenAI API wired with streaming responses
LangSmith-ready tracing hooks included
Full source code · NDA · 60-day support
RAGPipeline Ingest Retrieve Generate Chunk Pinecone Eval

Custom Development · Full RAG Stack

Custom GenAI Development

Miracuves builds from your specification - custom RAG architecture, agent workflows, and integrations. Full team: GenAI engineer, backend, QA, PM.

Scoped and priced before development begins
Document ingestion + vector index + eval harness
Weekly sprint demos - working retrieval every sprint
Guardrails, SSO, and audit logging as required
Full source code · prompts · IP 100% yours
Wk 1
Wk 2
Wk 3
Wk 4

GenAI Ops · Monthly

Ongoing GenAI Development

Miracuves works as your ongoing GenAI partner - index updates, prompt improvements, model upgrades, and feature work on a monthly retainer.

From $2,999/month - cancel with 2 weeks notice
Dedicated Miracuves GenAI team assigned to your project
Direct communication with GenAI engineers - no account manager relay
Weekly quality reports - faithfulness, latency, token cost
Scales up or down as your GenAI needs evolve
Quality Standards

How Miracuves ensures every GenAI 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 RAG - ingest / embed / retrieve / generate separatedArchitecture
Prompt + pipeline versioning in git - reproducible releasesExperimentation
Eval regression suite - golden questions before every deployPerformance
Citation accuracy checks - answers must reference source chunksValidation
CI/CD - automated ingest, index rebuild, and staged rolloutDelivery
Tenant isolation - vector namespaces and RBAC on documentsData Quality
LLM observability - latency, cost per session, quality alertsMonitoring
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 RAG Eval Coverage Required

Unit tests for chunking, integration tests for retrieval pipelines, and contract tests for LLM API responses. Minimum eval score enforced before any release is promoted.

03

Eval Gate Before Production

Every release runs against golden Q&A sets measuring faithfulness and citation accuracy. Streaming latency and token cost are profiled under load before go-live.

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

Prompt Registry and Rollback Ready

Every deployed prompt and index version is tagged in git and the vector store. Rollback to a previous retrieval index is a configuration change - not a rebuild.

06

Post-Deployment Monitoring - 60-Day Active Support

LangSmith or custom dashboards track answer quality, latency, and cost from day one. Miracuves monitors GenAI health during the 60-day post-deployment window - proactive index updates, not reactive firefighting.

Technology Stack

The generative AI stack Miracuves ships with

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

OpenAI API
GPT-4o · embeddings · function calling
Claude API
Anthropic · long context · safety
LangChain
Agents · chains · tool routing
LlamaIndex
Document indexes · query engines
Pinecone
Vector DB · hybrid search
Weaviate
Self-hosted vectors · GraphQL
Hugging Face
Open models · inference endpoints
Python 3.11+
FastAPI services · async pipelines
FastAPI
LLM API gateway · streaming SSE
Redis
Session cache · rate limiting
Postgres
Chat history · audit logs
AWS S3
Document storage · ingestion
LangSmith
Tracing · eval datasets
Docker
Containerized inference workers
Kubernetes
Auto-scaling LLM workers
React
Chat UI · admin consoles
Our Process

From brief to deployed GenAI app - what happens and when

Every generative AI engagement follows the same delivery spine - whether you start from our ChatGPT clone base or a custom RAG architecture. You always know what Miracuves is building, what documents or APIs you need to provide, and what ships at each milestone. Timelines reflect standard GenAI delivery; enterprise RAG runs milestone-based with the same quality gates.

Brief & NDA

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

Step 01

Scope & Plan

LLM provider, retrieval stack, and eval criteria 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

Golden-question eval suite, citation checks, and latency profiling on staging before go-live.

Step 04

Launch & Handoff

Source code, prompt library, vector index docs, API specs, and observability dashboards delivered. 60 days active support.

Step 05
Same DayNDA turnaround
4-10 WeeksStandard GenAI delivery
24 HoursFirst commit after scope
60 DaysPost-launch support
Transparent Pricing

What generative AI development costs at Miracuves

We publish prices because we are confident in what we deliver. LLM API usage is billed separately at provider rates - Miracuves quotes engineering and infrastructure, not hidden token markups.

ChatGPT Clone Base

$3,299 from

Fixed price · 3-4 week delivery · scoped

  • ChatGPT-style web app - React + Node
  • Streaming chat, history, admin panel
  • OpenAI API integration wired
  • Full source code on handoff
  • 60-day post-launch support
  • NDA protected from day one
View ChatGPT Clone
Most Requested

Custom RAG / Agent Build

Custom Quote

Scoped before build · milestone billing

  • Full GenAI team - backend + frontend + QA
  • Document ingestion + vector index + eval harness
  • Weekly sprint demos - working retrieval
  • Guardrails, SSO, and audit logging
  • Full source code · complete IP transfer
  • Milestone billing - no pay before delivery
Get a Scope & Quote

Ongoing GenAI Ops

$2,999/mo

Monthly retainer · cancel with 2 weeks notice

  • Index updates and prompt improvements
  • Eval monitoring and model upgrades
  • 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: GenAI projects fail when scope is vague. We document retrieval architecture, eval criteria, and LLM provider choice before kickoff - so cost reflects real engineering, not surprise SOW changes.

What affects GenAI project cost at Miracuves

ChatGPT clone pricing stays fixed when scope matches the base product. Custom builds scale with: document volume and formats, number of data sources, multi-tenant RBAC, agent tool integrations (CRM, ticketing), on-prem vs cloud LLM hosting, fine-tuning requirements, and compliance (HIPAA, GDPR) scope.

Typical GenAI budget ranges

ChatGPT clone base: from $3,699 · 3-4 weeks.
Custom RAG assistant: $14,000-$35,000 · 6-10 weeks depending on data complexity.
Multi-agent platform: $25,000-$60,000 · 8-14 weeks.
Ongoing retainer: from $2,999/month for index and prompt maintenance.
LLM API costs are passed through at provider rates - quoted separately in writing.

Six days is Miracuves build time, not calendar time

The six days are ours, and they do not run past six. What can add time sits on your side: developer account verification, merchant onboarding and compliance approvals are controlled by the app stores and your payment provider, not by us. We list exactly what you need ready on the first call so you can start those in parallel.

See what you provideFACT-005, audited quarterly
Client Reference

What a real GenAI project looks like at Miracuves

An EU-based B2B SaaS company had 12,000 Confluence pages and no internal search that sales engineers trusted. Support spent 40% of tickets answering questions already documented - and GDPR rules blocked sending docs to public ChatGPT.

01

The Challenge

Build a private knowledge assistant grounded in Confluence and PDF playbooks - with role-based access, citation links, and EU data residency - scoped to five weeks before enterprise customer rollout.

02

What Miracuves Delivered

LangChain ingestion pipeline, Pinecone with tenant namespaces, Claude for generation, cross-encoder reranking, and a React chat UI with source citations. Deployed on AWS eu-west-1 with SSO and audit logs for every query.

03

Outcome

Delivered on week 5. Support ticket deflection rose 38% in month one. Sales engineers reported 2+ hours saved per week finding pricing and integration docs. Full source code and prompt library transferred - their team extended the index to Slack archives themselves.

38%Ticket deflection
5 WeeksFull delivery
100%Source owned
View All Case Studies →

Client Testimonial

"We could not send our playbooks to public AI tools - compliance would not allow it. Miracuves built a RAG assistant that cites the exact Confluence paragraph, respects role permissions, and runs in our EU region. Week-five delivery was real - our CS team had it before our enterprise launch."

LK

L.K., VP Product

B2B SaaS · EU Knowledge Assistant

⭐ Read Clutch & Google Reviews →

Project Brief

Solution typeEnterprise RAG Assistant
Delivery timeline5 weeks
StackLangChain · Pinecone · Claude
Key integrationsConfluence · SSO · AWS eu-west-1
Document corpus12,000 pages · multi-role RBAC
Source code100% client-owned
38%
Ticket deflection
5w
Delivery time
2h+
Saved per rep/week
Client Reviews

What clients say about Miracuves generative AI development

Across RAG assistants, ChatGPT clones, content generators, and agent workflows - from B2B SaaS to legal tech - verified on Clutch and Google.

★★★★★

Clutch · Enterprise RAG

"Miracuves built our internal knowledge assistant in five weeks - grounded answers with citations, SSO, and EU hosting. Support deflection hit 38% in the first month. Their GenAI team understood compliance constraints from day one and never suggested shortcuts we could not ship."

LK

L.K., VP Product

B2B SaaS · EU

LangChain · RAG · Claude
★★★★★

Google Reviews · ChatGPT Clone

"We launched a white-label ChatGPT product for our agency clients in under four weeks. Miracuves delivered the full React frontend, admin panel, and API integration - our team rebranded and resold it immediately. Source code was clean and documented. Best ROI project we ran last year."

JT

J.T., Founder

Digital Agency · North America

ChatGPT Clone · OpenAI · White-label
★★★★★

Clutch · Legal Tech

"Contract review was drowning our paralegals. Miracuves built a RAG system over our clause library with highlight citations and human review queues. Turnaround on first-pass review dropped 45%. They set up eval tests so we catch regressions when we update the index - that discipline is rare."

NP

N.P., COO

Legal Tech · UK

RAG · Pinecone · Eval harness
5.0 / 5.0Clutch average · 14 reviews
4.8 / 5.0Google average rating
Top DeveloperClutch recognition · 2024-2025
Read All Reviews →
Frequently Asked

Questions about generative AI development at Miracuves

What is generative AI development at Miracuves?

Miracuves designs, builds, and deploys production LLM applications - RAG knowledge assistants, ChatGPT-style products, AI agents, and content generators. We handle orchestration (LangChain/LlamaIndex), vector retrieval, guardrails, eval harnesses, and deployment. You receive full source code and prompt assets with 100% IP ownership.

How long does a RAG project take?

A scoped RAG assistant - document ingestion, vector index, chat UI, and eval suite - typically ships in 6-10 weeks. Our ChatGPT clone base delivers in 3-4 weeks from $3,699. Complex multi-agent systems take 10-14 weeks. Working retrieval prototypes are demonstrated within the first 2-3 weeks so you can validate approach early.

Which LLM providers does Miracuves support?

OpenAI (GPT-4o), Anthropic (Claude), and open-weight models via Hugging Face inference endpoints. Provider choice depends on context length, cost, compliance, and latency requirements. Miracuves architects for provider swap - you are not locked to a single vendor API.

How do you reduce hallucinations in production?

RAG with citation requirements, cross-encoder reranking, metadata filters, and golden-question eval suites before every release. We do not rely on prompt instructions alone. Answers that cannot be grounded in retrieved chunks return a controlled fallback - not invented facts.

Can Miracuves deploy GenAI in our private cloud or VPC?

Yes. We deploy on AWS, Azure, or GCP with data residency controls. For strict compliance, we support on-prem vector stores and self-hosted open models. API keys and document stores never ship in client-side bundles.

What documents can be indexed for RAG?

PDFs, Word docs, HTML, Confluence, SharePoint, Notion exports, ticketing systems, and database records via connectors. Miracuves assesses chunking strategy and access control per source during discovery. OCR is available for scanned documents.

Do I own the source code and prompts?

Yes - 100% IP ownership on every engagement. Application code, prompt templates, ingestion pipelines, and infrastructure-as-code transfer on handoff. LLM API usage is billed at provider rates; Miracuves does not resell tokens with hidden markup.

What is included in post-launch support?

60 days of active support covering bug fixes, index tuning guidance, and eval interpretation. Ongoing index updates, prompt improvements, and model upgrades are available through our monthly GenAI ops retainer from $2,999/month.

Get Started

Ready to build your GenAI application 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.

500+GenAI applications delivered
4-10 WeeksGenAI delivery
100%Source code yours
Same DayNDA turnaround
WhatsApp - Start Now Contact & Brief Form

NDA signed before we discuss your project details

Page reviewed by the Miracuves Generative AI Team · Last updated May 2026 · Clutch & Google Reviews