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RAG Development Services

Enterprise RAGAI Systems

Scalable Retrieval-Augmented Generation with Next.js, vector databases, embeddings, and LLMs—built for accurate, grounded answers from your business knowledge.

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200+
APIs BUILT
8+
Years Experience
98%
Uptime
50+
Experts
✦Retrieval-Augmented Generation
✦Vector Search & Embeddings
✦Pinecone · Chroma · pgvector
✦Document Ingestion Pipelines
✦Semantic Knowledge Bases
✦LangChain RAG Chains
✦OpenAI & Claude RAG
✦Hybrid Search
✦Chunking Strategies
✦Enterprise PDF Search
✦RAG Evaluation Suites
✦Next.js AI Platforms
✦Retrieval-Augmented Generation
✦Vector Search & Embeddings
✦Pinecone · Chroma · pgvector
✦Document Ingestion Pipelines
✦Semantic Knowledge Bases
✦LangChain RAG Chains
✦OpenAI & Claude RAG
✦Hybrid Search
✦Chunking Strategies
✦Enterprise PDF Search
✦RAG Evaluation Suites
✦Next.js AI Platforms
// RAG retrieval chainconst chunks = await vectorStore.similaritySearch(  query, { k: 5, filter: { tenantId } });const answer = await llm.generate({ context: chunks });// Outputanswer.citations → ["policy.pdf#p12", ...]
Why RAG With Us

The RAG Advantage

RAG connects LLMs to your data—so answers stay grounded in policies, docs, and operational knowledge instead of generic model memory.

We engineer ingestion, retrieval, reranking, and citation pipelines on Next.js with production observability and eval suites.

OpenAI EmbeddingsLangChain RAGPinecone & ChromapgvectorHybrid SearchNext.js AI SDKDocument PipelinesRAG Eval Harness
RAG Implementation Process

How The RAG Pipeline Works

From document ingestion to grounded generation—we follow a proven RAG engineering workflow with vector stores, retrieval tuning, and quality evaluation.

01
Upload Business Data
Ingest PDFs, APIs, documents, databases, CRM data, and enterprise knowledge systems with access controls.
02
Generate Embeddings
Chunk documents and convert them into embeddings using OpenAI or domain-specific embedding models.
03
Store in Vector Database
Persist vectors in Pinecone, Weaviate, ChromaDB, pgvector, or OpenSearch with hybrid search where needed.
04
Configure Retrieval
Tune top_k, reranking, metadata filters, and citation rules for accurate context assembly.
05
AI Retrieval + Generation
Retrieve relevant knowledge and generate grounded answers with monitoring, evals, and guardrails.
Enterprise RAG Solutions

Built With RAG

Sector-specific retrieval systems for healthcare, finance, legal, and education—with embeddings, hybrid search, citations, and guardrails built for production knowledge bases.

01
Industry RAG
Healthcare RAG

Retrieve patient records, medical documents, and AI-generated diagnosis insights in real time.

Capabilities
Vector DBMedical AIEmbeddingsSecure APIs
02
Industry RAG
Finance RAG

AI-powered financial analytics with real-time document retrieval and intelligent reporting.

Capabilities
AI ReportsFraud AnalysisPDF SearchLLM
03
Industry RAG
Legal RAG

Search legal contracts, case files, and compliance documents using AI semantic search.

Capabilities
Semantic SearchContractsAI AssistantDocument AI
04
Industry RAG
Education RAG

AI tutors powered by educational datasets, notes retrieval, and smart learning assistants.

Capabilities
AI TutorKnowledge BaseLearning AIEmbeddings
Technology Stack

RAG Tech Stack

A production RAG architecture—product UX, ingestion and embeddings, vector retrieval, and cloud delivery built as one platform, not disconnected tools.

Architecture flow
Product → Retrieval → Vectors → Delivery
Layer 01
Product Layer

RAG interfaces users trust

3
  • Next.js
  • TypeScript
  • Tailwind CSS
Layer 02
Retrieval & Embeddings

Ingest, chunk, and embed content

3
  • OpenAI
  • LangChain
  • Hybrid Search
Layer 03
Vector & Storage

Indexes, metadata, and persistence

3
  • Pinecone
  • ChromaDB
  • PostgreSQL
Layer 04
Runtime & Delivery

Scale, deploy, and evaluate

3
  • Docker
  • Vercel
  • RAG Evals
Why Choose Us

Reasons To Choose Miraculous Soft

Deep AI product engineering experience, strong delivery discipline, and a focus on measurable outcomes—so your RAG initiative becomes a real competitive advantage.

01
Grounded Answers

We build RAG so responses cite your data—reducing hallucinations and improving trust in enterprise AI.

02
Semantic Search Excellence

Hybrid retrieval, reranking, and metadata filters deliver the right context for every user query.

03
Secure Knowledge Access

Tenant isolation, PII controls, and audit logs keep sensitive documents protected in production RAG.

04
Measurable Quality

RAG eval suites track recall, faithfulness, and latency—so retrieval quality improves release over release.

Ready To Build Your RAG System?

Launch enterprise RAG systems for every business sector—with modern architecture, guardrails, and a team that ships.

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