About

Agents need a runbook.
Teams need a shared one.

We're building the workflow layer that sits above whatever agent each engineer uses — templates, checkpoints, audit — so AI coding is reproducible across five engineers, not just the one who figured out the right prompt.

The problem

  • AI coding agents generate code but leave the rest of the workflow manual
  • Your best engineer gets a 10× boost with Cursor or Gemini — the other four guess
  • Every agent's workflow dies at the PR. Ticket context, reviews, tests, deploys are all disconnected
  • Every team writes the same prompt five ways — no shared template, no shared context, no shared gates
  • Autonomous agents ship to main with no human checkpoints and no audit trail
  • Source code and prompts sent to third-party servers that weren't supposed to see them

Our approach

  • A shared runbook — 7-phase template — every engineer re-runs on any ticket
  • Visual canvas designer where the tech lead defines the exact flow once, for the whole team
  • Kanban + reports give PMs real-time visibility regardless of which agent each engineer uses
  • Stack-aware runbooks — the canvas palette reshapes to the tools your team picks
  • Checkpoint nodes that pause execution for human review at configurable phase boundaries
  • Works as an MCP server — no code crosses our boundary. Your agent keeps everything local.
Principles

What we believe

Privacy by architecture
Code never crosses the MCP boundary. Your agent reads and writes files locally. We only receive workflow metadata — status, paths, counts. Not a policy, the shape of the system.
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 Run Task generates data -- velocity, cost, cycle time. Reports exist so leads can make evidence-based decisions about process and tooling.
Stack-agnostic by design
RailRun doesn't care which stack you run. The canvas palette reshapes around your tools — Gradle for Android, Vite for web, pytest for backend, Terraform for infra, dbt for data. One runbook engine, every surface.

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.