The Zero-to-One Product Squad is a specialised multi-agent AI system that takes your data product idea from brief to a functional vertical slice, fast and cheap: under an hour, for a few dollars in model spend. You inject requirements and taste, while the agents do the heavy lifting, and show their work.
Every number below comes from the actual Langfuse trace for this pipeline run, not a projection.
8.9M tokens processed for just $9.99. The system's aggressive prompt caching architecture helped avoid $26.07 in standard input rates, saving 62% ($16.08) on costs.
| Agent | Cost | Turns |
|---|---|---|
| Analyst | $1.01 | 4 |
| Builder, build | $4.06 | 4 |
| Builder, revision (3 rounds) | $1.31 | 13 |
| Critic, hygiene (2 rounds) | $1.15 | 4 |
| Polisher, engineer (2 rounds) | $1.92 | 32 |
| Gatekeeper, security | $0.53 | 1 |
| Total | $9.99 | 58 |
* seriously - the exact cost was 9.9879336
Eight real moments where the agents exercised autonomous taste and technical discernment during the build.
Three more views from the same live demo.
I'm David Shawcross. Most of my work is zero-to-one product: taking bets from concept to reality alongside small, resource-strapped cross-functional teams. The Zero-to-One Product Squad is my attempt to run that same motion with agents standing in for the team, proving the thesis that a network of specialised agents can handle the end-to-end execution of a full product innovation squad, and massively speed up an IC in the process.
If you want to see the code, talk about how this applies to your team, or tell me what's broken, I'd like to hear it.