Enterprise RAG & Knowledge Intelligence
Turn scattered enterprise data into trustworthy, cited AI answers — retrieval-augmented generation, semantic search and knowledge assistants that beat hallucination.
What this means for your business
Your organisation already holds the knowledge to answer most questions — it's just scattered across documents, wikis, tickets and databases. Retrieval-augmented generation turns that sprawl into accurate, cited answers. We build the retrieval, grounding and evaluation layer that makes enterprise AI trustworthy — respecting permissions and keeping answers fresh as your knowledge changes.
Accurate AI, grounded in your own knowledge
RAG Pipelines
Retrieval-augmented generation tuned for accuracy.
Vector & Semantic Search
Find meaning, not just keywords, across your data.
Document Intelligence
Ingest, chunk and structure unstructured content.
Grounding & Citations
Answers backed by sources users can verify.
Access & Permissions
Respect entitlements at retrieval time.
Continuous Evaluation
Measure relevance, faithfulness and freshness.
From documents to dependable answers
Connect
Map and ingest your knowledge sources.
Index
Embed and structure into a vector store.
Ground
Wire retrieval, citation and guardrails.
Tune
Evaluate and improve answer quality.
Frequently asked questions
What is retrieval-augmented generation (RAG)?
RAG retrieves the most relevant passages from your own content at query time and gives them to the model as context — so answers are grounded in your data and can cite their sources, rather than relying on the model's memory.
Can it respect who's allowed to see what?
Yes. Retrieval is permission-aware — users only get answers grounded in content they're entitled to see, enforced at query time against your existing access controls.
How do you keep answers current?
We automate re-indexing as content changes, so the assistant reflects your latest documents. Freshness is tracked as part of ongoing evaluation.
Where does this run and how is data protected?
In your cloud or a private endpoint, with no training on your data. We design for your data-residency and compliance requirements from the start.