Fooyo blog
Longer reads on what we are building, and what we are learning while shipping it.
Return and refund automation uses AI agents to classify return requests, approve or reject refunds against policy, trigger exchanges, and update inventory in real time, without a human agent touching every ticket. Done properly, it does not just move tickets faster.
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Turning a single marketing brief into three market-ready video variants for Singapore, Japan, and China requires more than translation. It requires an AI system that can rewrite pacing, honorifics, visual references, and regulatory disclosures separately for each market while
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A 30-minute AI demo should show your own data or a close approximation of it moving through a real workflow, name the exact private-cloud deployment model, and end with a scoped next step, not a generic tour of features built for a different industry.
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AI agents can read from and write to WordPress, Contentful, and most legacy content systems today without a custom middleware layer, using three established methods: direct REST/API calls, Retrieval-Augmented Generation (RAG) over a content index, and the Model Context Protocol
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Connecting AI agents to a legacy ERP system that was built before REST APIs existed requires a translation layer, not a replacement project. The practical path is to wrap the old system with an API facade or middleware that turns its native interfaces (screen-scraping, flat
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When an agentic AI workflow makes a mistake, the outcome depends entirely on what was built before the mistake happened: whether the system detected the error, whether it stopped itself, whether a human could see what went wrong, and whether the action could be reversed.
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A ticketing system that only knows how many tickets it sold, not how many people are actually standing at the gate right now, is working with half the picture. Attractions and events lose money and goodwill in that gap: guests queue longer than they should, staff get deployed to
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For a Singapore SME entering China, a WeChat Mini Program typically offers faster deployment and lower upfront costs than a native app: Mini Programs launch in 2 to 6 weeks for $500 to $70,000, while native iOS and Android apps take 2 to 6 months and cost $20,000 to $150,000
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Singapore's national AI push is compressing the gap between policy ambition and enterprise reality, but it is not shrinking the actual build time required to get AI into production.
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