Ryan de Melo
I'm a CTO and hands-on builder. Over eighteen years I've taken platforms from zero to production scale: a B2B API business from $0 to $1.1B, ML systems serving 550M+ recommendations a day, and the payments and credit infrastructure behind $22B+ in commerce. These days I build GenAI infrastructure for regulated enterprises.
This is where I write down the parts that don't fit in a slide: architecture decisions, what actually changes at scale, and the unglamorous work of building teams. Start below, or read more about me .
Featured
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I Asked My AI Agent for an Itemized Bill. It Got Awkward.
A month of heavy AI agent use, itemized: only a fifth of the tokens wrote code, almost half could run on a cheaper model, and the prompt cache quietly bills you for every coffee break.
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Resolving Stuck Receivables With RAG and Agents
A production system that resolves stuck accounts-receivable mismatches by retrieving over invoices, contracts, remittances, and email, then proposing a fix a human approves before any money moves.
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The Enterprise AI OS: My Thesis for the Next Five Years
The durable enterprise AI layer is not a model or a chatbot. It is an operating system that gives agents identity, permissions, tools, memory, and an audit trail over the systems a company already runs.
Recent Posts
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OpenClaw and the Agents That Act: What Non-Technical People Should Actually Know
A plain-English guide to OpenClaw and the new wave of AI that does things instead of just talking. What it is, how it compares to the big-company versions, and the one idea everyone needs before they hand it the keys.
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What Eighteen Years of Platform Builds Taught Me About AI Hype
After eighteen years of watching big data, mobile, microservices, cloud, and now AI agents arrive on the same script, here is how I separate the durable capability from the narrative.
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Building GenAI for Regulated Industries Without Getting Fired
Two ways to fail when you ship GenAI into a regulated business: never ship at all, or ship something nobody can audit. The narrow path between them, from building this in financial services and industrial operations.
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MCP a Year In: What Held Up, What Didn't
Sixteen months of building production systems on the Model Context Protocol. The interoperability bet paid off. Auth, versioning, and the demo-to-production gap are still where teams bleed.