Built for the handoff nobody wanted to manage manually

AppHandoff was built by developers who experienced the AI prototype-to-production gap firsthand. After watching multiple teams struggle to ship Lovable and Bolt prototypes to real backends, we built the coordination layer that was missing: automated scanning, shared context via MCP, and a Kanban board where agents and humans work side by side.

The dangerous part of AI-assisted delivery is not code generation. It is the gap between one repo's assumptions and another repo's reality. A Lovable frontend calls endpoints that the backend has not implemented yet. A backend refactor changes a response schema that the frontend still expects in the old shape. Neither side knows about the other's changes until staging blows up.

AppHandoff treats that gap as a first-class workflow with contracts, scans, tickets, and reviewable fixes. The scanner runs on every push. Mismatches surface as structured tickets with the endpoint path, schema delta, and severity. Implementation plans are drafted with project context so the fix is unambiguous.

The product leans toward PR-first delivery, machine-readable context, and visible role ownership rather than opaque background automation. Agents draft; humans approve. Every action is auditable.

We favour small, reviewable changes over large batch deployments. Every feature ships through a pull request, every agent action is logged, and every implementation plan is visible to the next person who picks up the ticket.

We are based in the Netherlands and operate as part of Team K2K. The platform is used by solo developers, agencies managing multiple client projects, and engineering teams integrating AI tools into their existing workflows.

The product is built on a conviction that AI coding agents are most valuable when they operate within guardrails. Agents should not commit directly to production branches. They should draft plans, open pull requests, and wait for human approval. AppHandoff enforces this workflow while making agents dramatically more productive.

When an agent connects to AppHandoff via MCP, it gets structured access to the project state it needs: scan results, API specs, open tickets, database schemas, and implementation history. It does not need to infer architecture from file trees or README prose. The context is pre-analyzed and tool-accessible.