AI agents that do real work, safely.
We design, build, and harden agents that are trusted with real systems and real data. Not demos.
Where agents earn their place.
Answering the same questions all day
Customer and guest questions answered instantly from your own documents, with a clean hand-off to a person when it matters.
Finding answers in internal knowledge
Search over wikis, tickets, and docs that cites its sources, so your team can check the answer instead of trusting it blindly.
Repetitive back-office work
Reading emails, forms, and PDFs, pulling out what matters, and filing it in the systems you already use.
Operations and on-call
Agents that triage alerts, gather context, and draft the fix, with a human approving anything that changes production.
What you get.
Not a prototype that works on stage. A system you can run, measure, and trust.
An agent running in production
Deployed on your infrastructure or ours, connected to the tools it needs and nothing more.
Evals that tell you it works
A test suite built from your real cases, run before every change, so quality is measured instead of guessed.
Guardrails against misuse
Defenses against prompt injection and data leaks, scoped permissions, and human approval where the stakes are high.
Monitoring for cost, speed, and failures
Dashboards and alerts for token spend, latency, and errors, plus pinned model versions so nothing changes underneath you.
A clean hand-off
Documentation and a runbook your team can use, or ongoing support from us if you would rather not own it.
How an engagement runs.
Scope
1 to 2 weeks
We pick the workflow, define what good looks like, and agree on a fixed outcome and end date.
Build
3 to 6 weeks
A working agent early, then weekly iterations against the eval suite and your feedback.
Harden
1 to 2 weeks
Security review, guardrails, monitoring, and load testing before real users touch it.
Hand off
Ongoing, optional
Your team takes it over with docs and a runbook, or we keep running it for you.
Questions.
Which models do you use?
Whichever fits the job and your constraints. We pin versions and re-run the evals before any switch.
Will our data leave our control?
We use model providers’ business APIs, which don’t train on your data, and keep your data in your own cloud accounts wherever possible. If you need more control, we can run private open-weight models in your own environment. Retention and access are agreed up front.
How do we know it is working?
The eval suite and production monitoring. You see the same numbers we do.
What does it cost?
Every engagement is fixed-price, agreed after a free scoping call, so you know the cost before we start.