AI agents, inside your tools.
The use case is chosen. We build an agent connected to your data and software, with the permissions, controls and recovery options needed to use it.
A specific use.
Find information
Search documents and present useful sources for an answer.
Prepare a request
Gather information, qualify the need and suggest a draft for approval.
Coordinate a task
Move an action between tools within explicitly defined limits.
What we build.
An agent on real examples
The agreed journey, evaluated against representative cases from your work.
An evaluation set built from your own cases, with the expected answer for each and the score the agent reaches on them.
Connections and permissions
Access required for the task, approvals and identified error handling.
Service accounts scoped to the task in your identity provider, with the list of actions the agent may take without asking.
A supervised launch
Acceptance criteria, useful activity records and team instructions.
A readable trace of every run, in a dashboard you keep, and a switch that stops the agent without a deployment.
How long it takes.
Six to twelve weeks, driven far more by the state of your sources than by the agent itself. From week two you can put questions to it and read what comes back, and the evaluation score is published every week after that.
What we need from you.
Documents that are actually current
An agent repeats whatever it reads, including a procedure replaced two years ago. Pointing us at the folder someone keeps up to date saves more time than any work on the prompt.
Twenty real questions, with their answers
Written by someone who does the job, including the awkward ones. This is what turns a vague sense that it works into a number we can both watch.
A named person who approves
Someone accountable for what the agent sends out and for widening what it may do alone. Without that name, everything it produces stays in a review queue.