Ship AI code with confidence.
StackRails turns your repos, services and cloud (AWS, Azure or GCP) into one map. Your AI coding agents get context from it before they write. Then StackRails checks the code against your rules and reports what breaks one.
One map for every agent and every source
See why your agents work blind
What your AI coding agents can't see, what that costs you, and how StackRails gives them the whole map.
Your AI coding agents see one repo. Not your system.
The services, APIs and rules in your other repos stay out of sight. So they guess, and break what they can't see.
Industry data
- 96%of developers don't fully trust AI-generated code. Only 48% verify it.Sonar State of Code, 2026. 1,100+ developers surveyed.
- 1.7×more issues in AI-written pull requests than in human-written ones.CodeRabbit, December 2025. 470 PRs analyzed.
- 2.74×more security vulnerabilities in AI-generated code.CodeRabbit, December 2025. Same study.
Safe AI coding at scale
Roll out AI coding tools across your entire organisation without the risk. Gated by your permissions, and it never trains a model on your code. The guardrails that make enterprise-wide AI Coding Governance possible. Your security team signs off. Your developers get productive.
What Developers Get
StackRails works via a plugin to your coding agent, feeding it your codebase, architecture, and policies as context, then catching what doesn't fit before it ever reaches a pull request.
The portal your architects actually open
Six screens from the product: governance, architecture and conformance in one place. Sample data.
1/6Your architecture command center
Live conformance score, open violations, PR pass rate, and per-team governance, all at a glance.
One map of your system.
Every agent reads from it.
StackRails maps your repos, services, cloud and events into one live knowledge graph. From it, StackRails produces your Policy Pack and enterprise AI skills, and serves both to your AI coding agents through one MCP server. Then it checks every change, so nothing ships unless your policies pass.
Your repos, services, cloud and events…
github
gitlab
terraform
kubernetes
api gateway
lambda
azure
google cloud
backstage
datadog
jira…become one live knowledge graph.
From it, StackRails produces your Policy Pack and enterprise AI skills…
…and serves both to your AI coding agents through one MCP server.
Connect your systems
Point StackRails at your repos, IaC, live cloud, CMDB, and event bus. StackRails builds itself and stays current on every commit. Legacy systems are reached through their API endpoints, even where there's no source to read.
Serve the right context
Every AI coding agent gets the connected slice of your architecture, dependencies, and policies before it generates, through one vendor-neutral MCP server. Claude Code, Copilot, Cursor, and Codex alike.
Govern every change
Your Policy Pack is checked three times, with conformance evidence for your auditors.
See the three checks→How StackRails closes the gap
Every AI coding agent gets the right context from your code, architecture and policies, served through a vendor-neutral MCP, so AI-written code fits your system from the first draft.
Context for every agent
StackRails builds a connected model of how your enterprise is actually built across hundreds of repositories, then serves the right connected slice to every agent before it generates. When a developer works anywhere in the system, the AI sees the whole connected system around the file.
Policy & governance
Your approved patterns, conventions, security policies and technology choices become rules in one Policy Pack. StackRails gives them to your AI coding agents before they write, then checks the code against them. Platform teams define the rules once, instead of copying instruction files into every repo. New developers write code that looks like your best engineers wrote it.
Dependency mapping
Change a function, and every caller across your repositories becomes visible. Modify a schema, and every downstream consumer is shown, including loosely-coupled event-bus subscribers. StackRails models the full dependency chain so you see the impact of a change before you make it.
Live architecture documentation
StackRails writes your architecture docs from the code itself: services, APIs, events and dependencies. When the code changes, the docs change with it.
AI architectural reviews
Whichever AI coding agents you use, StackRails checks their new code against the map of your architecture and reports where it drifts, with a hint for the fix.
Your rules, before and after your AI agents write
Your AI coding agents get your rules before they write. Then StackRails checks the code against the same rules and reports what breaks one, on the pull request too.
Before the agent writes
Context and policy go into the agent's prompt, so the first pass is steered toward compliant code.
On the developer's machine
Your AI coding agent's output is checked on the developer's machine, before code is committed or pushed.
At the pull request
Check results are reported on the pull request as a conformance report, with an audit record for the architect's review.
Governed AI coding already ships at scale.
The leaders didn't get there by handing engineers an AI tool and hoping. They got there by giving every agent the right context and policy guardrails.
Category proof: the leaders pair AI generation with governance. These are not StackRails customers.
Same tools. Opposite outcomes.
Same agent. Same task. With StackRails, it gets your map before it writes. Then its code is checked.
- 01
Your APIs in context
The agent gets your approved APIs before it writes.
- 02
Your rules in context
The agent gets your rules with the task.
- 03
Rule breaks reported
After the agent writes, the code is checked and rule breaks are reported.
Save engineering hours. Less rework after the agent writes.
AI code that does not fit your system comes back as rework: review comments, fixes and rewrites. When the agent knows your system before it writes, there is less to fix.
- 01
Fewer fix loops
With your architecture and rules up front, the agent needs fewer rounds to get a change right.
- 02
Less duplicate code
The agent uses the clients, APIs and libraries you already have, instead of writing new ones.
- 03
Rule breaks reported early
Found on the developer's machine or at the pull request, before review.
In your environment. Gated by your permissions. Fast to deploy.
StackRails deploys in your own cloud (AWS, Azure, or GCP), hosted in our EU region under your KMS, or fully on-prem. In your own cloud or on-prem, your source never crosses a tenancy boundary. On the hosted EU option, only the architecture model (metadata, under your KMS) is managed by StackRails, never your source.
Your cloud
Run StackRails in your own cloud: AWS, Azure, or GCP. Your access role, your VPC, your KMS. Your source stays in your environment; the only egress is the MCP response to the AI assistant you already use.
Hosted EU
StackRails-managed in eu-north-1 (Stockholm) under your KMS. Enterprise posture without the infrastructure overhead.
On-prem / air-gapped
Self-host the full stack. No external dependencies beyond the embedding model. Air-gapped capable, suited to sovereign-cloud deployments.
Common questions about StackRails
What does StackRails do?
StackRails maps your code, architecture and policies, so every AI coding agent gets the right context before it writes. Then it checks every change before it merges, so AI-generated code fits your system and follows your standards.
Which AI coding agents does StackRails work with?
Claude Code, GitHub Copilot, Cursor and OpenAI Codex, and any other MCP-capable assistant. They all get the same context and the same policies through one vendor-neutral MCP server.
We already use CLAUDE.md, Cursor rules and Copilot instructions. Why is that not enough?
They work for one repo. Across many teams, markdown rules and local skills get copied, forgotten, contradicted or ignored. StackRails turns those scattered instructions into one governed Policy Pack. Your platform team defines it once, and StackRails distributes it to every agent, team and repo.
How does an AI coding agent get context across many repositories?
StackRails builds a connected model of how your systems are built across hundreds of repositories: services, API contracts, events, data models and dependencies. Before the agent generates, it gets the connected slice around the file it is working on, so it references other services correctly and does not invent endpoints.
What are the three checks?
Before the agent writes, context and policy go into its prompt, so the first pass is steered toward compliant code. On the developer's machine, the agent's output is checked before code is committed or pushed. At the pull request, a conformance report and audit let the architect approve before merge.
Does StackRails train a model on our code?
No. Your code is never used to train a model. It runs in your own cloud (AWS, Azure or GCP), hosted in the EU under your KMS, or fully on-prem.
Free. Yours either way.See it on a real codebase. Thirty minutes.
Live walkthrough of StackRails, the three checks, and the two-tier authorization model on a real codebase. Founder-led for first customers. Response inside one business day.