Fooyo blog
Longer reads on what we are building, and what we are learning while shipping it.
When an AI system writes the script, generates the voiceover, and assembles the final cut, ownership of that video depends on how much a human directed and edited the result, not on which tool did the work.
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When a managed AI contract comes up for renewal, the default move is to sign the extension and move on. That is the wrong instinct. A renewal is the only point in the relationship where the vendor needs you more than you need them, and the terms worth renegotiating fall into
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A cross-border storefront in Southeast Asia has to do three things at once: show the right price in the right currency, speak to the shopper in a language they trust, and settle payment through a method that's actually available in their country.
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A human-in-the-loop review gate is a checkpoint built into an AI agent's workflow where a person must approve, reject, or edit an action before it executes. Done well, it stops costly mistakes at the two or three points where they actually happen, without forcing a human to
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Forward deployed engineering is a delivery model where software engineers work inside a client's organization, not behind a vendor's product roadmap, to build and ship AI systems that run in that client's live production environment.
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When an AI system answers a question from a knowledge base, the answer is only as trustworthy as the exact document revision it pulled from. Version control for knowledge bases means recording not just that a document changed, but which specific version of that document
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Seasonal demand forecasting for attractions is the practice of using historical visitation data, search trends, weather patterns, and booking signals to predict how many people will show up at a specific place on a specific day, often weeks in advance.
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A tourism board dashboard is only useful if it answers one question clearly: is this destination getting more valuable visitors, or just more visitors? The metrics that actually matter are the ones tied to economic outcome and capacity, not vanity traffic.
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An enterprise RAG (Retrieval-Augmented Generation) system that answers questions across a company's documents is only safe if it retrieves differently for different people. The core requirement is simple to state and hard to build: access control has to be enforced at the
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