The Intelligent Engineering Sprint proves the operating model on your backlog. DevNitro is that operating model, built as software: architecture your AI inherits, work that runs from your backlog, context that survives handoffs, and governance across your whole organization.
Same AI your team already uses. A different system around it.
DevNitro runs multiple agents against your real codebase at once, clearing issues straight from your backlog. Parallel work is explicit and governed, not scattered across private chats.
Not a better coding agent. A level above: the system that turns individual AI use into organizational capability.
DevNitro starts from an opinionated blueprint: a full production-grade solution with your conventions, data access, auth, and tests already wired. The AI inherits the architecture before it writes a line. That is where the correction tax goes.
Coding assistants guess. DevNitro knows.
Define work as epics and issues. DevNitro claims them, spins up workers, and executes against your codebase. Leadership plans in the backlog. The backlog is what runs.
Conversations and decisions persist across sessions, interruptions, and people. A new session, or a different engineer, resumes with the full history and can search prior context. Knowledge stops leaking out at the end of every chat.
Agents, skills, commands, and personas are defined at four levels: company, team, project, and user. Set a security review at the company level and every team inherits it. Let a project add its own. This is how AI stops being scattered and starts being governed.
Each worker builds in its own worktree. Work merges back only after build and test gates pass, and DevNitro can navigate and screenshot the running app to confirm it actually works. Verification is in the pipeline, not bolted on after.
Model vendors change. Pricing moves. Your operating model should not depend on any one of them. Choose the provider per company, team, or run.
You can teach your team to approximate this by hand with the tools you already have. Or you can run it as software. Many organizations do both.
| By hand, with your existing tools | DevNitro | |
|---|---|---|
| Context | Discipline: you save it and re-feed it each session | Persists automatically across sessions and handoffs |
| Architecture | Enforced by review, after the fact | Inherited from the blueprint, every file |
| Governance | Tribal knowledge, per developer | Company, team, project, and user levels |
| Parallel work | Coordinated by hand, one prompt at a time | Governed autonomous runs with visible capacity |
| Verification | Your review catches what it can | Build and test gates plus visual verification |
| Provider | Locked to each tool's model | Claude, Copilot, Codex, or Ollama, swappable |
The Intelligent Engineering Sprint proves it on your backlog with a measured before and after. DevNitro is what keeps it running after.
DevNitro is built using its own blueprints, agents, and operating model every day. The platform runs on the same practice it sells. It was created by Chad Carter: 29 years architecting systems Fortune 500 companies depended on, a decade as a Microsoft MVP, and the author of best-selling technical books. He defined the Intelligent Engineering, the operating model for engineering organizations in the AI era. It's what comes after vibe coding.
A mid-level developer costs roughly $15,000 to $20,000 per month, fully loaded. You only need to recover a small slice of that time for a seat to pay for itself. Everything above that is upside.
Every developer on an autonomous run needs an active seat. Start self-serve today and onboard your team yourself, or have it installed on your backlog with a measured before and after through the Intelligent Engineering Sprint.
Book an assessment and we will scope it on your real backlog, or start self-serve today.
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