Context and tools your agents can actually use.
An agent is only as good as the context it has and the tools it can call. We build the retrieval and tool layers — the parts that turn a chatty demo into a useful production system.
What we build
- →Knowledge base ingestion, chunking, and re-indexing
- →Hybrid retrieval (vector + keyword + metadata filters)
- →Re-ranking and citation extraction
- →Custom tool definitions for your APIs and databases
- →MCP servers for cross-team agent ecosystems
- →Memory systems — short, long, and episodic
What we believe
Retrieval quality > model size
A small model with great retrieval beats a frontier model with mediocre context. Most RAG fails at retrieval, not generation.
Tools should be small, typed, idempotent
Sprawling tool definitions confuse agents. We design tools the way we design APIs — explicit contracts, predictable behavior.
Citations are non-negotiable
Hallucinated sources are worse than no answer. Every retrieval-grounded response should be traceable back to source.
Have an agent that lacks the right context?
Tell us where retrieval is failing — wrong sources, missing tools, hallucinated answers — and we'll come back with a plan.