StackRails aligns AI-generated code to your architecture and governs what ships
StackRails gives every AI coding agent your architecture, dependencies, codebase, and policies as context before it writes, then checks every change before production.
So AI-generated code fits your system, follows your standards, and ships safely.
KiroVS CodeHand AI coding agents a codebase with no context. This is what you get.
of developers don't fully trust AI-generated code. Only 48% verify it.
Sonar State of Code, 2026. 1,100+ developers surveyed.
more issues in AI-written pull requests than in human-written ones.
CodeRabbit, December 2025. 470 PRs analyzed.
more security vulnerabilities in AI-generated code.
CodeRabbit, December 2025. Same study.
Your teams are already teaching AI your architecture, one repo at a time.
Today every team does it its own way: CLAUDE.md, .cursor/rules, Copilot instructions, local skills, prompt snippets, and tribal docs. That works for one repo. It breaks across an enterprise.
StackRails turns those scattered instructions into one centralized source of architecture knowledge, approved patterns, dependency knowledge, and governance for every developer, team, repo, and AI coding agent.
Repo-level instructions drift
Markdown rules and local skills get copied, forgotten, contradicted, or ignored across teams.
What agents need to know is scattered
Auth, logging, dependencies, ownership, data residency, event consumers, and legacy boundaries live across repos and systems.
Governance must be enforceable
StackRails serves context before generation, checks output after generation, and proves conformance at the pull request.
Safe AI coding at scale
Roll out AI coding tools across your entire organisation without the risk. Read-only by design, 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
Governance, architecture exploration, and conformance in one place. Product UI shown with sample data.
Your architecture command center
Live conformance score, open violations, PR pass rate, and per-team governance, all at a glance.
The right context in. Governed code out.
StackRails ingests your code, architecture, and systems from every source, understands them, and maps the dependencies. It feeds each AI agent the right context as it generates, including your policies, so the code meets your enterprise’s quality bar. Then it checks every change again, so nothing ships unless your policies pass.
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 a read-only, vendor-neutral MCP. Claude Code, Copilot, Cursor, and Codex alike.
Govern every change
Your Policy Pack is enforced at three points: pre-generation in the prompt, post-generation on the developer's machine, and at the pull request, with conformance evidence for your auditors.
How StackRails closes the gap
Every AI coding agent gets the right context from your code, architecture and policies, served through a read-only, vendor-neutral MCP, so AI-written code fits your system from the first draft.
Context for every agent, the core
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, the reason to buy
Your approved patterns, coding conventions, security policies, and technology choices become executable rules, your Policy Pack, enforced by three checks: before the agent writes, on the developer's machine, and at the pull request. Instead of copying markdown instructions and tool-specific rules into every repo, platform teams define one governed Policy Pack that StackRails distributes to Claude Code, Cursor, Copilot, Codex, and any MCP-capable assistant. New developers write code that looks like your best engineers wrote it.
Dependency mapping, an outcome
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 no one mapped. StackRails models the full dependency chain so you see the impact of a change before you make it.
Live architecture documentation, an outcome
Self-written technical documentation is history. Wikis, Confluence pages, ADR backlogs nobody touches, gone. Architecture documentation is generated from the code itself: service maps, ownership, dependency chains, API contracts. Current on every commit because it comes from what's actually running, not someone's six-month-old memory of it. New engineers and AI agents alike query the live state of your systems.
AI architectural reviews, an outcome
Continuous architectural review on every change. The platform surfaces architectural drift, scalability risks, and accumulating technical debt, the structural problems that don't fail a unit test. Architects get an Architectural Drift Report showing where new code deviates from approved patterns, where dependencies are tangling, and where the system is drifting from its target architecture, so the review queue stops being the bottleneck.
Non-conforming code never reaches main.
Governance moves left and stays on. Every change passes three gates on its way from prompt to production.
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
The AI's output is checked at the developer's machine, before code is committed or pushed.
At the pull request
A conformance report and audit; the architect approves before merge.
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
The AI's output is checked at the developer's machine, before code is committed or pushed.
At the pull request
A conformance report and audit; the architect approves before merge.
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.
Without the right context, AI coding agents are like a junior coder with no knowledge of your enterprise. You’ll never get production-level code.
The variable isn't the model. It's the governance layer around it. StackRails maps your code, architecture and policies and enforces your policies at every step, from the prompt to the pull request.
Save tokens. Save money.
We feed every agent only the right context at the right time, never your whole codebase. Fewer tokens per request means lower spend and faster answers.
Read-only. In your environment. Fast to deploy.
StackRails deploys in your own cloud (AWS, Azure, or GCP) under a read-only access role, 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. Read-only access role, your VPC, your KMS. Your source stays in your environment; the only egress is the read-only 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.
Built for complex codebases.
The more services, dependencies and legacy systems you have, the more StackRails helps.
Monolith, microservices, or a hybrid
Whatever your topology, StackRails maps it. The harder your system is to hold in one head, the more value it returns.
Interconnected services and real dependencies
Internal APIs, shared contracts, and dependency chains that span repos and teams.
Wired into legacy systems you can't easily change
Critical paths that route through systems no one wants to touch but everything relies on.
What about our legacy systems?
We ingest the API endpoints to those systems. And any legacy code you own, we ingest the code too.
Who buys it
CEO
Engineering velocity as a business lever. Ship faster, safely, without adding headcount.
Velocity without Architectural Drift.
Governance and audit-ready evidence.
A business case in your own numbers.
Platform champions
One set of guardrails for every tool.
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 read-only, 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 agent like Claude Code 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 write to our code, or train a model on it?
No. StackRails is read-only by design. It never writes to your repos, and 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.
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.