AI for Engineering, Product & Data Teams

Engineering, product, platform and data/analytics teams.

Build with AI deliberately and reason clearly about how it fails — where it earns its place in the product, and where it adds risk in production.

Shipping AI features is easy; shipping ones that don’t mislead users or leak data is not. Evaluation is often thin, models drift silently after launch, and the team that built the feature is the one expected to answer for it.

A starting agenda — every session is shaped around your team, your tools and the risks you’re managing.

1. Where AI improves a product — and where it’s the wrong tool Where AI improves a product — and the cases where it’s the wrong tool for the job.
2. How AI features fail: hallucination, thin evaluation, silent drift The ways AI features fail in production: hallucination, thin evaluation and silent drift.
3. Evaluating an AI feature before and after it ships How to evaluate an AI feature before it ships and keep watching it afterwards.
4. Data, privacy and security when you build on AI Data, privacy and security considerations when you build on top of AI.
5. Reasoning about confident-but-wrong output in your own systems How to reason about confident-but-wrong output in systems your own team owns.
6. A build-and-review checklist for AI features A build-and-review checklist for shipping AI features safely.

Every team’s needs are different. We’re happy to talk it through and tailor the session to yours — let’s talk →