RAG development in India for AI that stays grounded in your data.
We build retrieval-augmented generation systems for US product and engineering teams: chatbots, semantic search, and document Q&A that answer from your private content, cite their sources, and say so when they do not know.
Chennai-based senior engineers. US time-zone overlap. Your IP, start to finish.
Daily overlap with US business hours
IP ownership, NDAs signed up front
Engineers only, no junior hand-offs
To a working system, not months
Transparent, fixed-scope pricing
Six stages decide whether the answer is right
Most RAG quality problems are not model problems. They live in the steps before the model ever sees a token. This is the pipeline we build and tune for every system.
Pull from your sources: docs, tickets, wikis, databases, PDFs.
Structure-aware splitting that keeps tables, headings, and code intact.
The right embedding model for your domain, versioned and reproducible.
Hybrid search: dense vectors plus keyword (BM25) for exact terms.
A cross-encoder reorders candidates so the best context lands on top.
The model answers from retrieved context, with citations back to source.
A demo is easy. A system you can trust is not.
Anyone can wire an embedding model to a vector database over a weekend. The gap between that demo and something your users rely on is real engineering. Here is where it lives.
Split every document into fixed 500-character blocks and hope sentences survive.
Chunk on document structure, preserve tables and code, and overlap context so answers do not get cut in half.
Pure vector search that misses product codes, error strings, and exact part numbers.
Hybrid retrieval that fuses semantic vectors with keyword search, so both meaning and exact terms get found.
Stuff the top 20 nearest chunks into the prompt and let the model sort it out.
Rerank candidates with a cross-encoder, then pass a tight, relevant context window the model can actually use.
Ship it, then react to complaints when the bot starts making things up.
Build an eval set from real questions and measure retrieval and answer quality on every change before it ships.
Confident answers with no way to check where they came from.
Every claim cites its source chunk, and the system says 'I do not know' when the context is not there.
Retrieval systems built for production
We work across the stack you already use, from pgvector and Postgres to managed vector stores, OpenAI, Anthropic, and open models. The architecture follows your data, not a template.
Grounded AI assistants over your data
Support copilots, internal knowledge bots, and customer-facing assistants that answer from your private content and cite their sources instead of guessing.
Search that understands intent
Hybrid semantic and keyword search across documents, code, tickets, and records, so people find the right thing on the first query, not the fifth.
Answers from long, messy documents
Contracts, manuals, research, and reports turned into a system you can ask plain questions of, with traceable answers and page-level references.
Measurable, trustworthy retrieval
Evaluation harnesses, regression tests, and guardrails that keep accuracy from quietly degrading as your data and models change.
Tell us what your AI needs to know
Bring us your data and the questions your users keep asking. We will scope a grounded RAG system, show you how we will measure it, and have something working in weeks.