About

Engineering needs structure.
Not just more AI.

We are building the operations layer that connects AI code generation to the rest of the engineering workflow -- tickets, reviews, tests, and deployment.

The problem

  • AI coding assistants generate code but leave the rest of the workflow manual
  • Tickets, branches, PRs, reviews, tests, and deployments are disconnected artifacts
  • PMs have no visibility into what engineers are building or how fast
  • KMP teams get second-class support -- most tools only understand single-platform projects
  • Uncontrolled AI agents with no human checkpoints and no audit trail
  • Source code sent to third-party servers with no PII scanning

Our approach

  • A structured 7-phase pipeline that connects every step from ticket to production
  • Visual canvas designer where leads define the exact workflow their team follows
  • Kanban board and reports that give PMs real-time visibility into team velocity
  • First-class KMP support with iosMain read-only protection built in
  • Checkpoint nodes that pause AI execution for human review at configurable points
  • BYOK architecture -- your data goes to the LLM provider you choose, using your key. We never see it.
Principles

What we believe

Privacy by architecture
Source code stays on the developer's machine. LLM calls go direct to the provider. We only receive metadata. This is not a policy -- it is how the system is built.
Humans in the loop
AI generates. Humans approve. Checkpoint nodes exist so that no AI output reaches production without explicit review at the points your team chooses.
Built for teams, not solos
The buyer is a PM or lead. The users are engineers. The value is in team visibility -- knowing who is shipping what, how fast, and at what cost.
Structured, not open-ended
Phases, nodes, and templates define the workflow. Engineers follow the pipeline. There is no blank canvas where AI runs unchecked.
Measurable outcomes
Every flow unit generates data -- velocity, cost, cycle time. Reports exist so leads can make evidence-based decisions about process and tooling.
Android and KMP first
We do not try to be everything for every language. We focus on Android and Kotlin Multiplatform -- the ecosystem where structured AI execution is most needed.

Get in touch

Interested in Enterprise, have a partnership idea, or just want to talk? Send us a message and we will get back to you.