RAG Development Company in India
Build AI systems that know your data. We develop production RAG pipelines that retrieve relevant information from your documents, databases, and knowledge bases, delivering accurate, cited answers instead of hallucinated guesses.
Proof-First Delivery
Measurable Outcomes We Optimize For
What We Offer
Service Modules Built for Production
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
RAG Pipeline Development
End-to-end retrieval-augmented generation pipelines — document ingestion, chunking strategies, embedding generation, vector storage, retrieval, re-ranking, and LLM generation with citation extraction.
Hybrid Search Systems
Combine semantic search (vector similarity) with keyword search (BM25) for superior retrieval accuracy. Metadata filtering, faceted search, and query understanding for complex information needs.
Knowledge Base Construction
Transform unstructured documents into searchable knowledge bases. PDF parsing, table extraction, image OCR, document hierarchy preservation, and incremental indexing for growing data.
RAG Evaluation & Optimization
Systematic evaluation with RAGAS, custom metrics, and human-in-the-loop feedback. Measure retrieval precision, answer faithfulness, and relevance — then optimize chunk size, embedding models, and prompts.
Enterprise RAG with Access Controls
Multi-tenant RAG systems with document-level permissions, user role filtering, and audit logging. Employees only see answers from documents they are authorized to access.
Agentic RAG Systems
RAG systems that go beyond simple retrieval — query decomposition, multi-step reasoning, tool-use for structured data, and self-correction when initial retrieval is insufficient.
Beyond Naive RAG
We build production RAG, not demo RAG. Hybrid search, re-ranking with Cohere/cross-encoders, query expansion, and chunk optimization that achieves 85-95% accuracy on real enterprise data.
Evaluation-Driven Development
Every RAG system ships with evaluation pipelines. We measure retrieval precision, answer faithfulness, and relevance — then iterate based on data, not vibes.
Multi-Source Integration
RAG across Confluence, SharePoint, Google Drive, Slack, databases, and APIs. Unified search across all your knowledge sources with proper access controls.
Production Operations
Index refresh pipelines, embedding drift monitoring, query analytics, and cost optimization. RAG systems that stay accurate as your data grows and changes.
Delivery Proof
See Our Work in Action
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
Why Choose Boolean & Beyond
RAG Pipeline Development
End-to-end retrieval-augmented generation pipelines — document ingestion, chunking strategies, embedding generation, vector storage, retrieval, re-ranking, and LLM generation with citation extraction.
Hybrid Search Systems
Combine semantic search (vector similarity) with keyword search (BM25) for superior retrieval accuracy. Metadata filtering, faceted search, and query understanding for complex information needs.
Knowledge Base Construction
Transform unstructured documents into searchable knowledge bases. PDF parsing, table extraction, image OCR, document hierarchy preservation, and incremental indexing for growing data.
RAG Evaluation & Optimization
Systematic evaluation with RAGAS, custom metrics, and human-in-the-loop feedback. Measure retrieval precision, answer faithfulness, and relevance — then optimize chunk size, embedding models, and prompts.
FAQ
Frequently Asked Questions
Ready to Build Your RAG System?
Tell us about your knowledge sources and accuracy requirements — we'll design a RAG architecture that delivers reliable, cited answers from your data.