Practical articles on what works in a real team, and what does not.
Book a CallForecasting projects fail on data rather than models. What history you need, why backtesting misleads, and how to judge a forecast that is always somewhat wrong.
Prompts change behaviour in production yet often live outside version control. How to manage changes, test them, and roll back when one goes wrong.
Invoices, contracts and forms arrive as PDFs and scans. What a working extraction pipeline looks like, where accuracy is lost, and how to verify at scale.
Traditional monitoring says the service is up. It does not say the answers got worse. What to log, what to alert on, and how to catch silent degradation.
Demos are built to succeed. What to ask about failure rates, data handling, model changes and what happens when you leave.
Free text is unusable downstream. Schema enforcement, what still fails when the shape is guaranteed, and how to validate meaning rather than format.
Which steps of an automated process still need a person, how to design the handover so it is not a rubber stamp, and when review checks stop being worth the delay.
When running your own model makes sense against calling an API: the real cost crossover, the operational burden, and the requirements that decide it.
The integration layer that decides whether AI automation is reliable: retries, idempotency, ordering and what to do when an upstream system is down.
Tell us about a process you want to automate and we will say whether it is worth building.
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