Parker AI: from an idea to an AI platform

High-spend advertisers had two bad options. Agencies that took days to turn around ideas. Or AI tools that wrote generic copy with no link to live market signal.

Parker's bet was simple. Pull in what brands and creators post right now across TikTok, Instagram, Facebook, and Reddit. Combine it with the brand's own Meta ad performance. Let agents turn that signal into hooks, scripts, and full creative briefs.

First the pipes. I set up ingestion workers that pull from each platform, normalize everything into one schema, and store it in Postgres and Qdrant. Failures retry with backoff and land in a dead-letter queue for review instead of silently dropping data.

Then retrieval. Vectors for similarity, Postgres for facts and filters. Chunking tuned for long social posts and ad copy. Re-ranking that pays attention to what the query asks for. Most bad AI answers start here, not in the model, so this layer got a lot of care.

Then the agents. Mastra runs ideation: hooks, angles, scripts, and briefs, saved into an idea bank teams can reuse. Every tool call is typed and traced through Langfuse. Anything high-impact, like a client email, waits for a human to approve it.

And the surface. Strategists live in Slack, so Parker delivers there. Alerts, weekly reports, and ad digests land in Slack, and a Next.js dashboard covers the deeper work.

Today Parker powers creative strategy for brands spending up to $2M a month on Meta ads. Idea turnaround went from days to minutes. The pipelines, agent rails, and eval setup I built are the base the rest of the team builds on.