Knowledge infrastructure • AI workflow systems • Developer experience
That means finding where products lose users to confusion, then building the AI-native systems and operational tooling to fix it: from workflow automation and support data analysis to troubleshooting architecture and docs-as-code operations. Deep experience across enterprise networking, cloud security, and developer-facing platforms.
What set him apart was how he thought about the work around the writing.
How I can help
Reusable AI workflow systems for research, content operations, migrations, and auditing, designed as operational infrastructure, not one-off tools. Built to compound: improvements to shared knowledge and workflow rules benefit every future task that runs through the system.
Technical documentation for networking, cloud infrastructure, security, and developer platforms, built from real support data and user journey analysis. Troubleshooting architecture, decision guides, and content strategy designed to reduce support volume and improve self-serve outcomes.
Support-case analysis, escalation pattern identification, and content prioritization systems that surface where knowledge gaps are driving the most recurring tickets, and translate that signal into a content strategy with measurable outcomes.
Impact, by the numbers
Documentation systems, operational tooling, and support-driven workflows, with measurable outcomes.
Research reduced from days to ~60 minutes
Designed reusable AI-assisted workflows for research, drafting, auditing, triage, and documentation quality checks that reduced manual work and became shared infrastructure across multiple teams.
Read the full case study →695 files in 3 days
Reduced a projected multi-week migration into a 3-day operational workflow by automating documentation updates across 695 files, 349 URL mappings, and hundreds of supporting assets.
Read the full case study →High-volume support category
Analyzed support escalations across the product area's largest support category and built a four-guide troubleshooting suite for enterprise networking products, reaching thousands of views in the first three months.
Read the full case study →Weekly production digest
Built automated reporting workflows that surfaced stalled pull requests, tracked queue state, and improved review prioritization for the documentation team.
Read the full case study →In their words
He's the kind of writer who is constantly pushing the envelope for what a technical writer can do. He built a full AI toolkit — agents, skills, custom commands — that connected MCP servers, documentation sources, and GitHub into workflows that let writers move through research, drafting, and publishing dramatically faster. Proactive, reliable, and completely unfazed by ambiguity. I'd work with Márcio again without hesitation.
He's proactive — for every feature I built, he asked about documentation needs before it even crossed my mind. He's organized, keeping careful track of release dates, and very good at quickly learning new tech all across our product suite. Márcio also invented workflows to use LLMs for large docs refactors that would have been tedious and error prone by hand. And to top it all off: he's one of the kindest people I've worked with — you want him on your team!
Featured writing
Selected posts on technical writing, AI workflows, and documentation systems.
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About
Developer documentation has moved from reference material to infrastructure. It's increasingly the interface AI agents use to understand products, generate implementations, and answer customer questions, which means it needs to be intentionally architected for retrieval, clarity, and reliability, not simply published. Most documentation systems aren't designed for that reality yet.
At Cloudflare, I built the systems to close that gap: AI-native workflow tooling adopted across multiple teams, support data analysis pipelines that surfaced where customers were actually struggling, troubleshooting architecture redesigned from real escalation patterns, and operational infrastructure that compressed multi-week projects into days. The writing was one output. The systems thinking was the work.
I started in technology journalism, which trained the instinct that still shapes everything: the gap between what organizations know internally and what users actually understand. That gap is what I'm paid to close.
In practice, that means treating documentation as part of the product experience itself: something that directly affects adoption, support volume, developer trust, and how effectively both humans and AI systems can use a platform. The goal isn't better writing. It's better knowledge infrastructure.
Contact
Recently departed Cloudflare in May 2026 and actively looking. I'm a good fit for companies with complex technical products where documentation is a strategic function, not an afterthought. I'm most interested in roles where there's scope to work across writing, AI tooling, and documentation systems.