Hey team, Paul here, Moa came from a habit I couldn't shake: Whenever something mattered, I'd ask multiple AI models instead of trusting the first answer. Not because one is "bad", but because each tends to notice and find different things. If all three models independently land on the same point, that's a pretty strong signal. And when they don't, the disagreement is often the most interesting part. So I built a tool around it. Moa runs all three in parallel, then a synthesis model cross-examines the answers. It follows the strongest reasoning, fact-checks divergent claims, and produces one unified response. You can see exactly which model contributed what through color-coded attribution. It's basically peer review for every prompt. Happy to answer any questions about how it works!

Moa

Peer review for every prompt

Peer review for every prompt