Capability — AI Engineering & Integration
AI that's been measured, not just demoed.
LLM-powered tooling and agentic workflows, built with an eval suite from day one — so what works in a demo still works after a thousand real users.
Process
Five steps, in order.
We don't call it done until it's been measured against real examples, not three cherry-picked ones.
Scoping
We define the task, the success metric, and the failure modes actually worth worrying about — not every hypothetical edge case.
Prototype
The fastest honest path to a working proof of concept: prompting, retrieval, or a fine-tune, whichever the task actually needs.
Evaluation
A benchmark built from real examples, run before anything ships — the eval set is what "working" means, not a vibe.
Productionize
Guardrails, monitoring, a cost and latency budget, and a defined fallback for when the model is wrong or unavailable.
Operate
Usage and drift monitoring after launch, with a set cadence for re-evaluating as the model or the data changes.
Standards
What we hold the model to.
The same discipline we'd want from any system we can't fully predict.
No ship without a benchmark. The eval set is the definition of "working," not a demo someone liked.
Versioned in git and reviewed in pull requests — not edited live in a dashboard nobody remembers to update.
A review step wherever a wrong answer is expensive — not every output, just the ones that matter.
Set before build, not discovered on the first bill or the first angry user waiting on a response.
A defined fallback for every point where the model can be wrong, slow, or simply down.
What goes to a third-party model, and what doesn't, decided explicitly — not by default.
Deliverables
What you're left with.
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