We built it twice. The second time, we got the question right.
GUST 1.0 was a multi-agent system for refactoring legacy code. Paying enterprise customers used it on real production workflows. What we learned from those deployments is the reason GUST today looks nothing like what we set out to build.
- 01 · GUST 1.0
A multi-agent system, in real production.
GUST 1.0 was a multi-agent system for refactoring legacy code, deployed with paying enterprise customers, running real production workflows. Not a demo. Not a benchmark. Customer code.
A SaaS company in Deloitte's Finnish Fast 50 used it to convert hundreds of React Native files into React Web for their US client in half the time. A sentiment analytics platform used it to build and deploy a full product in three months. The product shipped. The lesson it surfaced was the more interesting part.
- 02 · What surfaced
The AI was not the bottleneck.
Memory loss was. Lost scope was. Untraced decisions were. Quiet drift was. The same reliability failures behind every stalled enterprise AI project showed up in our own deployments, in front of paying customers.
We had built a refactoring tool. What we kept running into was the layer underneath it. The same layer that would be missing under any agentic AI workflow anyone might want to deploy. The interesting question was never the code. It was whether agentic AI could be governed end to end, in production, against the policies of a real organisation.
- 03 · GUST today
We stopped building the tool. We started building the substrate.
GUST today is the product those deployments told us to build. Not another agent. Not another framework. The coordination layer underneath them. Scopes. Persistent context. Propagation rules. Audit by default. The substrate that lets any agent, in any framework, be governed end to end.
That is what the rest of this site is about. This page is about the people who got us here.