Enterprise AI search that stays accurate.
We build retrieval systems that let AI answer from your own knowledge, accurately and with sources. Clean ingestion, vector search, reranking, and freshness sync, so answers stay grounded in your real data instead of drifting or hallucinating.
What we actually build
Large language models are powerful but they don't know your business, and left on their own they'll confidently make things up. Retrieval-augmented generation, or RAG, fixes that by grounding every answer in your actual documents, so the AI retrieves the relevant facts first and answers from them.
Good RAG is more than dropping files into a vector database. It takes careful ingestion that preserves structure, embeddings tuned to your domain, reranking to surface the genuinely relevant passages, and source attribution so every answer can be traced back. Done poorly, retrieval returns near-misses and the answers suffer.
We build the full pipeline and keep it fresh, so as your content changes the system stays current. The result is AI that answers from your knowledge with citations you can trust, whether it's powering internal search, a support assistant, or a product feature.
What's included
Structured ingestion
We parse your documents in a way that preserves meaning and structure, so retrieval has clean material to work with.
Domain-tuned embeddings
Embeddings chosen and tuned for your content, so semantically relevant passages actually surface.
Reranking
A second pass that reorders results by true relevance, cutting the near-misses that hurt answer quality.
Source attribution
Every answer cites where it came from, so users can verify and trust the response.
Freshness sync
When your source content changes, the index updates, so answers never go stale.
Access controls
Retrieval respects your permissions, so users only ever see what they're allowed to.
From first call to production
Audit your data
We assess your content, its formats, structure, and volume, and design the right retrieval approach.
Build the pipeline
Ingestion, embeddings, vector store, and reranking, tested against real queries for accuracy.
Ground & attribute
Wire retrieval into the AI so answers stay grounded and every response carries its sources.
Deploy & keep fresh
Ship it with a sync process that keeps the index current as your content evolves.
The impact you can expect
Common questions
What is RAG, in plain terms?
Retrieval-augmented generation. Instead of letting an AI answer from memory (where it can make things up), the system first retrieves the relevant passages from your own documents, then answers using only those. That keeps answers grounded and lets every response cite its source.
Why not just use ChatGPT with our files?
Dropping files into a general tool works for small, simple cases but degrades as content grows: retrieval returns near-misses, answers lose accuracy, and there's no control over freshness or permissions. A purpose-built pipeline handles scale, keeps answers accurate, and respects who can see what.
How do you stop it from hallucinating?
By grounding every answer in retrieved passages and attributing sources, the AI answers from your real content rather than inventing. When the relevant information isn't found, the system says so instead of guessing.
Can it respect our access permissions?
Yes. Retrieval is permission-aware, so a user only ever retrieves and sees content they're authorized to access.
Ready to scope your project?
- Free 30-min scoping call
- Prototype in days
- No lock-in — you own the code