LLM & RAG Development
RAG pipeline development services, enterprise RAG architecture & vector database consulting with hybrid BM25 + dense embeddings & reranking.
Direct Answer Summary
Rohini builds production Retrieval-Augmented Generation (RAG) and custom LLM applications that ground AI answers in your proprietary documents, databases, and knowledge bases. Our pipelines combine layout-aware chunking, hybrid BM25 + dense vector search, cross-encoder reranking, and strict inline citations—delivering verifiable answers with measurable retrieval precision.
Retrieval
Hybrid + Rerank
BM25 keyword + dense vector RRF fusion + cross-encoder.
Vector Stores
pgvector / Qdrant
Metadata-filtered HNSW indexes for multi-tenant isolation.
Verification
RAGAS Evals
Automated faithfulness, context recall & latency benchmarks.
Starting From
₹50,000
Private knowledge assistants, doc search & AI copilots.
Technical Architecture Breakdown
1. Layout-Aware Ingestion & Chunking
PDFs, tables, HTML, and Markdown are parsed structurally (preserving headers and tables) into overlapping semantic chunks with rich metadata.
2. Hybrid Search (BM25 + Dense Vectors)
Combining lexical BM25 with semantic embeddings via Reciprocal Rank Fusion catches exact product SKUs, error codes, and conceptual queries alike.
3. Cross-Encoder Reranking & ACL Filtering
Pre-filtered tenant/role permissions ensure users only retrieve authorized documents, while rerankers surface the top-5 highest-signal passages.
4. Citation Grounding & Hallucination Guardrails
Prompts enforce strict source-only synthesis with inline clickable citations and explicit 'I don't know' fallbacks when context similarity is low.
Production Tech Stack Matrix
| Layer | Technologies | Architectural Rationale | Production Guarantee |
|---|---|---|---|
| Ingestion | Unstructured / PyMuPDF, Recursive & Semantic Splitters | Clean table/heading preservation without noise | Incremental delta re-indexing |
| Vector & Search | PostgreSQL pgvector, Qdrant, OpenSearch / BM25 | Unified metadata filtering + fast HNSW recall | < 40ms retrieval p95 |
| Reranking & LLM | Cohere / BGE Reranker, Claude / GPT / Gemini | High precision-at-K before generation | Source-cited responses |
| Evaluation | Golden QA Datasets, Faithfulness & Recall Metrics | Prevents silent accuracy regressions on updates | CI-gated prompt/chunk changes |
Commercial Use-Case Matrix
| Scenario | Core Requirement | Delivered System | Outcome |
|---|---|---|---|
| Enterprise Knowledge Copilot | Query 50k+ SOPs, HR & engineering docs | ACL-aware hybrid RAG + Slack/Web UI | Instant cited answers |
| Customer Support AI | Accurate answers from product docs & changelogs | Hybrid RAG + confidence fallback to human | 45%+ ticket deflection |
| Legal & Compliance Search | Clause comparison with zero fabricated citations | Strict grounded RAG + page-level links | 10x faster doc review |
Related Engineering & Architecture Guides
Frequently Asked Questions
Why does basic vector-only RAG fail on technical or business documents?
Dense vectors alone often miss exact SKUs, names, or error codes. Hybrid search (BM25 + dense vectors) plus reranking dramatically improves recall.
Is our private company data used to train public AI models?
No. We deploy via zero-data-retention enterprise APIs or private VPC-hosted models so your documents never train external models.
How do you keep the knowledge base updated when documents change?
We build automated ingestion pipelines that hash documents, update only changed chunks, and delete stale vectors.
Tailored Project Scoping
Scope your LLM & RAG Development project with Rohini
Share your requirements and our engineers will respond within 24 hours with architecture notes, timeline, and a practical estimate.