Chat with your knowledge — and actually trust the answer.
Retrieval systems grounded in your documents, contracts, and policies. Cited, permission-aware, and honest about what they don't know — built to clear the 85%+ accuracy bar real businesses need.
Your organisation already knows the answer to most questions — it's just buried in contracts, wikis, tickets, and PDFs nobody can search properly.
A retrieval-augmented system puts that knowledge one question away. Ask in plain language; get an answer drawn from your material, with citations you can click to verify. It says "I don't know" when the answer isn't there, and only shows each person what they're allowed to see.
The catch: the afternoon-demo version of this is wrong often enough to be dangerous. Getting from a convincing demo to a trustworthy system is the actual work — and it's the work we've done, repeatedly, in production.
Climbing past the 60% ceiling.
Every shortcut tops out where a toy stops being useful. Here's how each technique we add lifts real-world accuracy.
Naïve "embed & retrieve"
Top-K chunks from a vector DB, straight to the model. The afternoon demo.
+ Content-aware chunking
Split by clause, table, and section — not blind character counts.
+ Hybrid retrieval
Semantic search plus keyword (BM25) so names, dates, and IDs land.
+ Reranking & evals
A reranker filters false positives; evals catch drift. Production-grade.
Knowledge you can query.
Grounded answers
Responses drawn only from your source material — no free-floating guesses.
Citations
Every answer links back to the exact source, so it's verifiable in one click.
Hybrid retrieval
Semantic + keyword search with reranking for that 85%+ accuracy floor.
Permission-aware
Retrieval scoped to each user's access — no leaking restricted content.
Eval dashboards
Accuracy measured continuously so regressions are caught early.
Freshness
Index updates with your content, with flags when sources look stale.
Deep where it counts. Fluent everywhere else.
Our depth is in the retrieval pipeline — chunking, hybrid search, reranking, and evals. That's where accuracy is won or lost, and it's what separates our systems from a weekend demo.
Stack-flexible. Any model, any vector store, on our cloud or inside your own VPC for data that can't leave. Already invested in a particular database or embedding model? We'll build around it.
Models
4Embeddings
5Vector stores
5Retrieval
5Evals
5Deploy
5"Adyatech built us a system that doesn't just predict — it tells us why. That changed how our entire team works."
Before you ask.
That's the afternoon-demo version, and it tops out around 60–70% accuracy — fine for a toy, dangerous for a business. Production RAG needs content-aware chunking, hybrid (semantic + keyword) retrieval, reranking, and a real eval set. That's the difference between a demo and a system your team can actually trust.
Three ways. It only answers from retrieved source material and cites it, so you can check. It's tuned to say "I don't know" rather than invent. And every answer is logged against an eval set so we catch accuracy drift before your users do. Honesty about uncertainty is a feature, not a bug.
Yes. Retrieval is permission-aware — the system only surfaces content a given user is entitled to see. A frontline agent and a director asking the same question get answers scoped to their access. Critical for legal, finance, and HR knowledge.
The index updates as your content does, and answers reflect the current source. The system can also flag when it's relying on material that looks stale, so nobody acts on an out-of-date policy. Keeping answers fresh is part of the build, not an afterthought.
Often paired with.
Knowledge buried
where no one can find it?
Tell us what your team keeps re-asking and re-searching. We'll come back with whether RAG fits — and how we'd hit the accuracy you need — within one business day.