Agents that act in your tools, and prove it.
What we build.
Operations agents
Triage, routing and data entry across your systems, with approval steps where the stakes are high.
Support agents
Resolve common requests on their own, and hand the rest to a person with the context attached.
Internal assistants
Look things up, draft and file in the tools your team already uses.
Evals and guardrails
For an agent you already have: find out how often it gets it right, and put limits where it does not.
How an engagement runs.
The tasks, the tools it may touch, and where a person approves.
Tools, prompts and evals, shown every week on real tasks.
Rollout with approvals, logging and limits switched on.
Success rate, speed and edge cases watched after launch.
A typical engagement.
- Duration
- [6–10 weeks]
- Team
- [2–3 engineers]
- You get
- The agent, its evals, guardrails and a runbook
- Starts with
- An AI Audit or a scoping call
What it may and may not do.
On its own
- Look up records in your systems
- Draft replies and summaries
- Fill in fields and route requests
- Flag what it is unsure about
Only with a person’s approval
- Send anything to a customer
- Change money, prices or contracts
- Delete or overwrite data
- Anything that cannot be undone
[The exact lists are agreed with you during the Think stage.]
Before you hand an agent the keys.
How do you stop an agent doing the wrong thing?
It can only use the tools and permissions we give it. Risky steps wait for a person to approve them, and every action is logged.
How do you measure it?
Against a set of real tasks from your team, scored for success and speed, on every change.
Which models do you use?
Whatever fits the task and your hosting rules. We recommend during the audit, with the reasoning written down.
Can it work with our systems?
Yes, through their APIs, with the least access the task needs.