Advice from people who run this every day.
We operate our own fleet of agents, runners and benchmarks. Our advice comes from that practice: what works, what it costs and what breaks.
Architecture review
A second look at your AI system before it ships or scales.
We review how your system calls models, handles tools and data, recovers from failure and gets tested. You get a written report with the changes that matter most, in order.
Model selection and spend
The right model for each task, at a cost you can plan.
We measure candidate models on your own tasks, then set up routing, limits and reporting so spend follows the work and not the newest release.
AI product strategy
Where AI belongs in your product, and where it doesn't.
We work with your product and engineering leads to choose the features worth building, the ones to skip, and how to tell which is which after launch.
Agent operations and governance
Run agents with clear owners, limits and approvals.
Task boards, review gates, approval steps and readable records that let agents work unattended while people stay in charge.
How an engagement works.
Understand the decision.
A short call to understand the decision you face, the system involved and the people who run it.
Read the real system.
We read the code, configuration and data flows, and talk to the people who operate them day to day.
Write it down.
A written report: what we found, the changes we recommend in order, and what each one costs to make.
Stay close.
We stay available while your team makes the changes, and we can build them with you if you want.
A good fit if
- You are
- About to ship or scale an AI system
- You need
- A clear view of model spend
- You want
- Agents that run without constant watching
Ship what you use.