Start from the problem
AI costs rising
“Our AI unit economics are getting worse, and nobody can say why.”
Lower cost per accepted task on the in-scope routes, within agreed quality and latency limits, measured against a week-one baseline.
Is it worth fixing? →AI coding with no measurable impact
“We bought AI coding tools, but we can’t show they improved delivery.”
Improvement in the delivery measures chosen at kickoff for one pilot team, without worse defect rates or review burden.
Is it worth fixing? →AI-built app not launch-ready
“We have an AI-built product, but we can’t confidently launch it.”
The production requirements agreed for your launch pass, and a deploy and rollback are demonstrated in your environment.
Is it worth fixing? →AI feature breaks on change
“Our AI feature breaks whenever we change a model or a prompt.”
A release gate and production monitoring that catch the failure classes agreed at kickoff, proven by catching an injected regression.
Is it worth fixing? →How it works
1 · Diagnostic
A few structured questions about the outcome you need, the size of the problem, your decision window and who else is involved. About three minutes, no call.
2 · Decision brief
An engineer replies in writing: what we understood, what is still uncertain, whether there is a business case, the safest next step, and what would make us decline. It is useful even if the answer is no.
3 · The right next step
For a small problem, that is a guide or a tool you can use yourself. For an uncertain one, a fixed-price diagnostic with a stop/go decision. For a well-defined one, a scoped engagement with acceptance criteria agreed before work starts.
We never need credentials, raw prompts, customer records or a private repository to tell you whether something is worth doing. The free read is capped at about an hour of an engineer’s time, so it stays honest about what it can and cannot establish.
None of these? Describe your situation and we will tell you straight whether we can help.