Practical articles on what works in a real team, and what does not.
Book a CallEmbeddings power search, recommendations and retrieval in most AI systems. What they are in plain terms, where they help, and where they quietly fail.
Without a fixed set of test cases, every prompt or model change is a guess. How to build an AI evaluation set from real data, score it, and keep it useful.
Prompts and logs quietly become the least protected copy of your customer data. Where it accumulates, how to redact, and what to check before building.
Some problems are better served by a rule, a form or a fixed script. How to recognise them, and why using a model anyway costs more than it returns.
One provider bill tells you nothing about which feature or customer is expensive. How to attribute spend, and the controls that prevent a runaway month.
Pilots succeed and stall. What production actually requires that a pilot does not, and how to size the remaining work honestly before committing.
Adoption fails for predictable reasons, and almost none of them are the technology. What actually drives usage, and why volunteers beat mandates.
Using one model for everything overpays on simple work and underperforms on hard work. How to route by task, and what a genuine fallback looks like.
An agent with your credentials can do anything you can. Scoping permissions, the actions that always need approval, and why prompt instructions are not a control.
Tell us about a process you want to automate and we will say whether it is worth building.
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