Field notes on shipping full-stack products, production AI systems, and data platforms — from engagements we've actually run.
What broke, what we monitored, and how we got retrieval accuracy from 70% to 94% after launch.
A practical framework for choosing your first data stack before you have a data team.
Why the cheapest architecture decision in month one is often the most expensive by month twelve.
In an industry that sells 'agile' as an excuse for scope creep, here's why we quote up front.
Building an eval harness that catches regressions before your users do.
Schema decisions that are easy to make early and painful to unwind later.
How to give your whole team dashboard access without everyone building their own metric.
Let's talk about what you're building — and maybe it becomes our next post.
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