Agentic RAG
Answers your team can actually cite.
The problem
Naive RAG retrieves the top few chunks by vector similarity and hopes the answer is in them. That fails on comparisons, on multi-hop questions, on anything requiring a document the query does not lexically resemble — and it fails silently, returning a fluent answer with no signal that the retrieval missed.
How we approach it
We treat retrieval as a reasoning loop, not a lookup. The system reformulates the question, searches across several strategies, judges whether the evidence is sufficient, and searches again if it is not. When the corpus genuinely does not contain the answer, it says so — which is the behaviour that earns a team's trust.
Capabilities
What's included
Hybrid retrieval
Dense vectors and keyword search combined, then reranked — recovering what pure similarity misses.
Query planning
Complex questions decomposed into sub-queries and recombined into one grounded answer.
Self-correcting loops
The system grades its own retrieved context and retries when the evidence is thin.
Structure-aware chunking
Splitting that respects headings, tables, and clauses rather than a fixed token count.
Citations & provenance
Every claim traced to its source passage, so answers can be verified rather than trusted.
Permission-aware access
Retrieval filtered by the asker's identity — no document surfaced to someone who cannot open it.
Use cases
Where this tends to pay off
- Internal knowledge assistants over policy, engineering, and process documentation
- Contract and clause search across a large agreement archive
- Technical support grounded in current product documentation and past tickets
- Research assistants that synthesise across hundreds of long documents
Which process would you automate first?
Bring us one workflow that costs your team real hours. We'll tell you honestly whether an agent is the right tool for it — and what it would take to build.