Labs is where we build and test end-to-end AI agents that do complete jobs, not just assist with tasks. These start as experiments with consulting clients, then become standalone products when they prove out.
Most AI tools stop at recommendations. They surface insights, suggest next steps, then wait for a human to act. That's useful, but it's not transformative.
Labs agents are different. They execute end-to-end: research, decide, act, measure, iterate. Humans set goals and guardrails, then review outcomes. The agent handles everything in between.
We're starting with paid media optimization, where this approach makes sense: constant monitoring, rapid iteration, and decisions that compound over time.
Monitors performance, generates concrete change proposals, screens them against guardrails, and applies approved changes through official ad platform tools. Every change is logged with a diff and can be reverted.
Step 1: Pattern recognition. We see the same problem across multiple consulting engagements. Teams are spending time on work that could be fully automated, not just assisted.
Step 2: Pilot with clients. We build an agent and run it alongside a client's existing team. We measure outcomes, not just activity. The client gets results; we get learning.
Step 3: Productize. When an agent proves out, we package it as a standalone product. Early pilot clients get preferred pricing and input on the roadmap.
Step 4: Continuous improvement. Every deployment teaches the agent more. Performance compounds as we learn what works across different contexts.
We're looking for design partners who want to test end-to-end AI agents in their GTM motion. Early partners get hands-on collaboration and preferred terms.