Insights
Field notes on shipping AI inside real systems.
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Forward-deployed engineering: the model is the easy part
What a forward-deployed engineer does, how the role relates to staff augmentation, and what we learned connecting AI to our own platform's rules and workflows.
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Fraud detection on a public dataset: the metric that matters when 0.17% of rows are fraud
We trained four classifiers on a public card-transaction dataset to learn one thing: with fraud this rare, the number everyone quotes is the wrong one. What we measured, and what a production system would still need.
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Your first AI project: pick the win you can measure
The first AI project decides whether there is a second one. Start behind the firewall, on data you already have, with a human gate — and pick something you can measure in weeks.
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We rebuilt our own hiring pipeline with AI
Two stages of our own recruiting operation depended on scarce specialist time: assessing senior engineers, and turning interviews into client-ready profiles. We redesigned both around AI. What we built, what it took to trust it, and the numbers.
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Documentation that keeps itself current: the agent proposes, your team approves
Docs drift because updating them is nobody's job. Give the job to an agent that detects the drift, drafts the fix, and opens a pull request — and keep the approval where it belongs.
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Computer vision on the cameras you already have: what two proofs of concept taught us
Counting people and vehicles, tracking occupancy, checking for a hard hat — the models are ready and the cameras are already installed. The work is placement, thresholds, drift, and deciding what never gets recorded.
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Support ticket triage with AI: classify, enrich, route — and measure the reassignments
The first minute of every ticket is spent deciding what it is and who should see it. That minute is automatable — and the number that tells you if it worked isn't speed, it's how many tickets bounce.
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