AI & MCP Integrations
AI investments measured like investments, not press releases
Half the AI on the market is a demo in search of a problem. We hold every integration to the same bar as any other business investment: what does it save, what does it win, and how will we know? If the honest answer is 'nothing,' we say so before you spend.
The discipline starts in scoping. For every proposed AI feature we write down the hypothesis: the metric it should move, by roughly how much, and how we'll measure it. A support assistant should measurably deflect tickets or shorten response times. A drafting tool should measurably cut hours. If we can't articulate the metric, the feature isn't ready to build — it's a hunch.
It continues in deployment. AI systems fail differently than normal software: confidently, plausibly, and quietly. So we ship with evaluation and logging — what the system said, what it was grounded in, where humans corrected it — and we review those logs. Guardrails and human handoff aren't afterthoughts; they're part of the original design.
And it ends with an honest verdict. Some experiments won't clear the bar; we kill them and tell you why, which costs us a project and earns your trust. The AI features that survive this process are the ones that show up in your numbers — which is the only place technology matters.
What this looks like in practice
- A written hypothesis and target metric before any AI feature is built
- Fast prototypes tested against your real workload, not curated demos
- Evaluation, logging, and guardrails shipped with every feature
- Human handoff paths designed for the cases AI shouldn't touch
- Honest kill decisions when an experiment doesn't clear the bar
The bottom line
We're not selling AI; we're selling outcomes that occasionally require it. That distinction is why our AI features work.
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