LLM features grounded in your data.
A general-purpose chatbot answers from the open internet. Your users need answers from your documentation, policies, catalogues and systems — with sources they can check. That is what retrieval-augmented generation (RAG) done properly provides.
When you need this
- Users ask questions your product should answer from internal knowledge
- A pilot chatbot gives confident but wrong answers
- Search across your documents is keyword-based and misses meaning
- You need LLM output your team can trace back to a source
What we deliver
- Retrieval pipeline: ingestion, chunking, embeddings, vector search
- Context and prompt engineering tuned to your domain
- Evaluation suite measuring answer quality against a reference set
- Source citation and confidence handling in the product UI
- Quality monitoring in production
Technologies we typically use
Questions we hear often
Will it stop hallucinations completely?
No system can guarantee that, and you should be wary of anyone who promises it. Grounding, retrieval quality and evaluation are designed to reduce hallucinations substantially and make errors detectable — we measure and report the actual rates.
Does our data leave our infrastructure?
That depends on the design. We can build fully self-hosted pipelines, use API providers with no-training agreements, or mix both — the data-flow decision is made explicitly with you during architecture.
Have a project in mind?
A 30-minute call is enough to scope most ideas — an honest read on feasibility, approach and effort.
Book a 30-minute project call