The Mobizio Blog
Practical writing on production AI engineering, product delivery, and the decisions behind reliable software.
Most MVPs fail not because they were built badly but because they were scoped to prove nothing. An eight-week playbook for deciding what goes in, what stays out, and how you will know it worked.
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Everyone claims to be AI-native. Here is the concrete difference it makes to how a product gets specified, tested, costed and staffed, and the parts that do not change at all.
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Three ways to make a model behave the way your product needs. They solve different problems, cost very different amounts to run, and most teams reach for the wrong one first.
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Inference cost scales with usage, which means a popular AI feature can cost more than it earns. How to find that out during design rather than after launch.
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The model gets the attention, but AI products fail on ordinary backend problems: slow operations in a request cycle, no idempotency, and data nobody modelled properly.
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Traditional tests assert exact outputs, which non-deterministic features cannot provide. What replaces them, and why the alternative is not "test it manually".
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