RAG & Knowledge Assistants
ChatGPT on your own data, without the hallucination embarrassments. Retrieval pipelines, enterprise knowledge assistants, and agentic RAG — engineered (chunking, hybrid search, reranking, evals), not plugged in.
Key Features
- Enterprise knowledge assistants over your docs, tickets, and data
- Retrieval engineering: chunking, hybrid search, reranking, evals
- Agentic RAG — retrieval plus actions, not just answers
- Accuracy baseline established in the Sprint, reported weekly in build
- Your data stays in your cloud — written DPA on request
- Sprint entry: 4 weeks on your real corpus
Every team wants "ChatGPT on our data." The difference between a demo and a system your staff trust is retrieval engineering: how the corpus is chunked, how hybrid search is tuned, how results are reranked, and how accuracy is measured — continuously, on your real questions.
We build RAG the way we build products: a Sprint first (4 weeks) that audits your corpus, stands up a retrieval prototype on your real data, and — most importantly — establishes an accuracy baseline on questions your team actually asks. Production builds then land in the AI MVP or Modernisation tiers, with evaluation and monitoring shipped as part of the system, and your data staying in your cloud.
Our own proof: TalkDrill's voice pipeline retrieves session context and speaking prompts for 50,000+ users — retrieval quality is the product there, and it transfers.
Benefits of Our RAG & Knowledge Assistants
Accuracy is a number, not a vibe
Every engagement establishes an eval set from questions your team actually asks. You see retrieval quality as a metric before rollout.
Engineering, not a plugin
Chunking strategy, hybrid keyword+vector search, reranking, and freshness pipelines — the unglamorous work that separates trustworthy from embarrassing.
Production RAG pedigree
TalkDrill retrieves session context and speaking prompts for 50,000+ users — retrieval quality is the product, measured in a live voice loop.
Answers that act
Agentic RAG connects retrieval to actions — the assistant that finds the policy can also file the claim, gated and audited.
Technologies We Use
We leverage the latest technologies and frameworks to deliver robust, scalable solutions.
How We Work
Our proven process ensures successful project delivery every time.
Corpus audit (week 1)
What you have, where it lives, what questions it must answer, and what "correct" means — written down before anything is built.
Retrieval prototype (weeks 2–3)
A working assistant over a slice of your real corpus, with chunking and search strategy tuned on your content.
Eval baseline (week 4)
An accuracy score on real questions, failure analysis, and a build recommendation with production costs stated.
Production build
Full corpus, freshness pipeline, monitoring, and rollout — in the AI MVP or Modernisation tier, fixed price.
Frequently Asked Questions
Related Services
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