Insights
Technical guides, case breakdowns, and production patterns from building real AI systems. Written for engineers and operators, not audiences.
Latest
Deflection rate gets all the attention, but the numbers that drive real business decisions are resolution rate, escalation quality, and CSAT delta. Here is how to instrument them from day one.
Most teams reach for fine-tuning when retrieval-augmented generation would be cheaper, faster, and more maintainable. A practical decision framework for choosing the right approach.
Flaky tool calls, missing context windows, and no human-in-the-loop path are responsible for the majority of agent failures we have seen. Patterns we use to fix all three.
What you need to log, trace, and alert on before you launch any AI feature. A practical starting point that does not require a dedicated ML ops team.
We replaced a static contact form with an intent-capturing AI qualifier on a client site and measured the results over 45 days. The numbers were surprising.
The biggest failure mode in AI projects is not technical, it is discovering in week 6 that you built the wrong thing. A scoping process that prevents it.
Practical AI content, published when there's something worth saying — typically 2–3 posts a month.
