The demo is easy.
The integration is the job.
We connect language models to the systems that actually run your business — under your permissions, inside your cloud, with every request traced. Then we put the result in a screen your team already has open.
This is the trace, not the chat bubble
A single question, from the moment a user presses enter to the moment it is written to the audit log. Two seconds of wall clock, six steps, and a guardrail at each one. If a vendor cannot show you this view of their system, they cannot debug it either.
- Total
- 2.04 s
- Cost
- $0.004
The request inherits the signed-in user's identity. Nothing downstream can see a document they could not open themselves.
Hybrid search across the document store and the warehouse. Filters applied at query time, not after — a filtered-out row never reaches the model.
A cross-encoder reorders the top 50 candidates down to the 6 that actually answer the question. This is where most of the accuracy comes from.
The model composes the answer from those 6 passages, with citations. If retrieval came back empty it says so instead of improvising.
One explicit, allow-listed action against your system of record — scoped, idempotent and reversible.
Prompt, retrieved ids, model version, tokens and cost written to the trace store. Replayable six months later.
We meet your stack where it is
No rip-and-replace, no data lake you have to build first. If a system has an API, a database or an export, it can be a source. Model providers sit behind the same interface, so swapping one is a deploy rather than a rewrite.
Or hire GenAI engineers directlyAutonomy is a dial, and it starts low
We climb this ladder one rung at a time, and only when the rung below has an approval rate that earns it. Most clients get the return they wanted on rung one.
Read-only assistants
Lowest riskAnswer, summarise, compare, cite. Cannot change anything. This is where almost every engagement should start, and where most of the measurable value already is.
Draft-and-approve agents
Human in the loopThe agent prepares the reply, the ticket update or the journal entry; a named person approves it. Approval rates become your accuracy metric.
Permissioned action agents
Highest risk — lastA short, explicit list of write actions the agent may take unattended, each idempotent, rate-limited, logged and reversible. We design the authorisation model before the agent.
Four things we tell every client in the first meeting
Your data is the project
Sixty per cent of an AI build is access, chunking, permissions and freshness. Anyone quoting you a two-week chatbot has not looked at your SharePoint.
Latency is a product decision
A two-second answer inside a workflow beats a four-second answer that is marginally better. We budget latency the way we budget cost.
Evals or it did not happen
A graded question set, built with your subject-matter experts, is the only way to know a prompt change helped. It ships with the system.
Nobody wants another chat window
The best integrations disappear into a screen your people already have open. We put the AI where the work is, not beside it.
Send us one workflow and one system
That is enough for us to come back with a scoped integration, a latency budget and a number — usually within a week.