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
Calibrate an LLM-as-judge for AI agents
LLM-as-judge for AI agents is usable only after a human-labeled holdout calibration. Trust auto-scores then. Book the $25,000 AI Transformation Sprint.
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Build an AI agent eval set from real tickets
Build an AI agent eval set from real tickets. Stratify volume and failures, label correct answers, gate deploys. Book the $25,000 AI Transformation Sprint.
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What to measure in an AI agent eval suite
AI agent eval metrics that should gate every deploy: golden-set size, accuracy thresholds, confidence gates. Book the $25,000 AI Transformation Sprint.
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What is an AI readiness assessment?
An AI readiness assessment scores five dimensions: whether you can ship, what to fix first, what a good one contains, and when to skip it.
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The AI implementation framework that ships
Three phases, each ending in something that runs rather than something that reads. Why the order can't be rearranged, and what breaks when one is skipped.
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Why AI pilots fail before they reach production
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.
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No AI strategy without an AI 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.