Knowing whether you are ready to build
Most AI projects are decided before engineering starts — by whether the workflow was measurable, the data was reachable, and anyone owned the metric. How to tell, and what to fix first.
Everything we've written on this
What is an AI readiness assessment, and do you actually need one?
A scored diagnostic across five operational dimensions that tells you whether you can ship and what to fix first. What a good one contains, what it can't tell you, and when to skip it entirely.
ReadAudit → Build → Operate: the three-phase model for AI that ships
Three phases, each ending in something that runs rather than something that reads. Why the order can't be rearranged, and what goes wrong when a phase gets skipped.
ReadWhy your AI pilot never reached production — and the five gates that get it there
Pilot purgatory is an engineering problem, not an ambition problem. Here are the eval, ownership, and rollback gates that separate a demo from a deployed agent.
ReadYou don’t have an AI strategy until you have an eval suite
A model you can’t measure is a model you can’t trust in production. How we build evals before we build the agent.
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Put a forward-deployed team on it.
If this is the kind of work you're trying to get into production, a 30-minute discovery call is the fastest path to a scoped plan.