The Model Is the Easy Part: What Actually Breaks When AI Goes to Production
AI demos have become remarkably easy to build. A capable model, a prompt, an API call, and a developer can have something impressive running before the end of the day.
Production is different.
Once real users arrive, the conversation shifts away from which model performs best on a benchmark and toward much less glamorous problems: latency, inconsistent outputs, context management, moderation, observability, infrastructure costs, and the long tail of failures that demos rarely expose.
Dmytro Voroshylov has spent 12 years building consumer software, engineering platforms, and production AI systems. His experience spans B2B engineering products such as Blauberg Selector, a platform for ventilation-system selection, as well as a conversational AI startup serving hundreds of thousands of monthly users. Earlier in his career, he was involved in building and scaling three trivia games distributed through the App Store and Google Play, whose combined audience exceeded one million users. Across these projects,...
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