Method
Most AI projects fail for a simple reason: they get automated because it's possible, not because the cost/value break-even justifies it. Our method always starts by calculating that break-even — before writing a single line of architecture.
An agent has development cost, operating cost (tokens, infrastructure, maintenance), and error cost when it decides wrong. It has value in time saved, human errors avoided, and freed-up capacity. If the value doesn't clearly outweigh the total cost, we don't recommend building it — no matter how technically feasible it is.
We run that calculation with data from your actual process, not generic benchmarks. And we hand it to you in writing, with the numbers, so the decision is yours and it's informed.
Our principles
We don't propose an agent because it can be built. We propose it when the break-even between cost and value justifies it — and we show it with numbers, not promises.
Maximum budget per process. Automatic cutoff when exceeded. Clear attribution: which agent spends what, on which model, for which task. If the break-even math doesn’t work, we tell you before building anything.
Accumulated processes, exceptions, and decisions live in your infrastructure, not ours. Over time your agents specialize enough that they stop depending on any single provider for that specific task.
We document what information the agent used, what it compared, and why it approved or escalated. We don't hand over a black box. We hand over an auditable judgment trail.
The AgentOps Audit is the entry point: a short, no-obligation diagnostic with concrete findings.