RAG
Grounded in your data
Private
Runs where your data lives
Evaluated
Measured, not vibes
In prod
Across 40+ countries
How retrieval-grounded answers are built
What we architect.
RAG & knowledge systems
Retrieval pipelines that answer from your documents with citations — not a model guessing from memory.
Agents & automation
Tool-using agents that read, decide and act inside your systems, with guardrails and a human in the loop where it counts.
Vector search & data
Embeddings, chunking and indexes tuned for your corpus, so retrieval is fast and relevant at scale.
Evaluation & model ops
Offline evals, monitoring and a rollback path — so you know a change is better before it ships, and after.
We run this ourselves.
The freight assistant and sales automation you see on this site are built on exactly this stack — so the architecture is proven, not theoretical.
Your data stays yours
Open-weight models and private deployment options, so sensitive documents never leave your control.
Grounded, not guessing
Retrieval and citations keep answers tied to your sources, cutting hallucination where it matters.
Measured continuously
Evaluation harnesses and monitoring catch regressions before your users do.
From use case to production.
- 01
Frame the use case
We pin down the task, the data and how you'll know it's working — the eval before the build.
- 02
Prototype & evaluate
A working pipeline on your data, scored against the metric that matters to you.
- 03
Deploy & monitor
We ship it where your data lives and wire the monitoring that keeps it honest.
Asked, answered.
Anything else — write to us and we answer within one business day.
Put AI into production.
Bring the use case and the data question. We'll tell you honestly whether AI is the right tool — and if so, how we'd build it.