LLMs that cite their sources.
Retrieval-augmented generation is easy to demo and hard to trust. We build RAG systems with measured faithfulness, hybrid retrieval tuned on your corpus, and answer pipelines that say “I don't know” instead of inventing policy.
✓ faithfulness target: ≥ 0.92 · groundedness measured per release
✓ ip transfer: complete · lock-in: none
✓ delivery: hyderabad · timezone overlap: US/EU
# every claim on this page is contractually testable
Naive RAG fails quietly, and quietly is worse than loudly.
Chunk-and-pray pipelines hit 60–70% answer quality and plateau. The failures are silent: wrong-but-plausible answers your users stop reporting and start distrusting. Production RAG is a retrieval engineering problem first and a prompting problem second — and retrieval quality is measurable.
Capabilities
Hybrid retrieval engineering
Dense embeddings (BGE-M3, OpenAI, Cohere) fused with BM25 and reranking. Chunking strategies tested per document type, not copied from a tutorial.
Grounded generation
Citation-enforced prompting, answerability classification, and refusal calibration. The model answers from your documents or says it can't.
RAG evaluation harness
Faithfulness, answer relevance, and context precision measured on a golden set drawn from real user queries — run in CI on every change.
Agentic & multi-step workflows
LangGraph-orchestrated agents for tasks that need tools, multi-hop retrieval, or human-in-the-loop checkpoints — with full trace visibility.
Document & data pipelines
OCR, layout parsing, table extraction, metadata enrichment, and incremental sync — the ingestion layer that determines your ceiling.
Access-aware retrieval
Row-level security carried into the vector store. Users retrieve only what they're entitled to see — enforced at query time, audited per request.
The approach
A sequence, because the order is the point: each phase gates the next on evidence.
Corpus audit
We profile your documents — formats, structure, duplication, freshness — and build the golden eval set from real questions your users actually ask.
Retrieval baseline
Hybrid retrieval tuned and measured before any generation work. If retrieval can't find the answer, no prompt will save you.
Generation & guardrails
Grounded prompting, citation enforcement, refusal calibration, and inline PII/safety filters — each change gated by the eval suite.
Production & feedback loop
Deployment with per-query tracing, user feedback capture wired into the eval set, and drift monitoring on both corpus and queries.
Deliverables
- Hybrid retrieval pipeline tuned on your corpus
- Golden eval set + automated RAG metrics in CI
- Citation-enforced generation layer
- Ingestion pipeline with incremental sync
- Access-control-aware vector store design
- Per-query trace and feedback instrumentation
- Cost model: tokens, embeddings, infrastructure
- Runbooks and team training