Most AI outputs are only as good as the data behind them. We're building the layer that fixes that — one domain at a time. The problem with general-purpose models isn't their reasoning; it's their lack of grounded, specific context.
When you ask a model to write an ad, it defaults to what it knows: a bland, average amalgamation of every piece of text it scraped from the internet up until its training cutoff. It doesn't know what's trending on TikTok today, or what copy structure is actually converting for DTC apparel brands right now.
We go deep on one domain before the next. Our first dataset, Ads, gives you a live view of what competitors are running — and gives your agents real market copy to work with at generation time.
The generalization trap
Three surfaces. One data layer. Whether you're accessing via MCP for Claude, hitting our REST API, or pulling via CLI, the goal is the same: inject high-fidelity context into your workflows.
Consider the typical agent workflow without Agent Drive. An agent is prompted to “write a Facebook ad for running shoes.” It hallucinates a generic benefit statement and a tired hook.
With Agent Drive, that same agent first queries the live database: agentdrive pull --brand nike --limit 50. It analyzes current hooks, identifies the prevailing tone for summer campaigns, and generates a variant grounded in reality.
What people are saying
The reception from early partners has validated this approach. Marcus Webb, AI Lead at Monks, noted that “The corpus quality is unlike anything else we've used. Our copy agents finally have real signal to draw from.”
Similarly, Sarah Chen at Ogilvy mentioned it cut their competitive research time in half, allowing them to know what's working in market before briefing creative. This duality—serving both humans who need intelligence and agents who need context—is the core of Agent Drive.