How I led product on Atlas Agent, an autonomous AI agent for SEO and agency work, and the two research engines that power it: Site Explorer and Keyword Explorer. Real tools, real approval gates, real users running their SEO through an agent instead of a dashboard.
Search Atlas is a live, NDA-protected product. The system names, counts, and screenshots below are the ones cleared for public sharing. I can't share anything beyond that — internal metrics, architecture, or roadmap details that go past what's already public.
SEO and agency teams don't struggle to find data. They struggle to act on it fast enough. A competitive audit means pulling backlinks from one tool, rankings from another, ad intelligence from a third, then manually stitching it into a recommendation. Keyword research gets done once in a spreadsheet and never opened again. The tooling exists. The time to use it well does not.
The bet behind this work was simple: instead of building another dashboard, build an agent that does the research and takes the action, in plain English, with a human still holding the approval button on anything that can't be undone.
I was the Product Manager who led the team behind Atlas Agent, Site Explorer, and Keyword Explorer, from defining the problem each one solves to the architecture that made an autonomous agent safe to hand real work to. I worked directly alongside engineering on the routing logic, the tool specs, and the approval-gate protocol, using AI-native engineering to move at a pace that normally takes a much bigger team.
"I stayed in the codebase alongside engineering, not just in the spec. That's what let this go from problem definition to three live systems on one team's timeline."