Build at the speed of AI.Trust it like your best engineer.
Generating code is cheap now. Reviewing it is the expensive part, and it lands on your seniors. Canon makes every AI coding tool build like your best engineer, enforces it as it writes, and proves what it followed. So you can trust what ships.
In-editor · Pre-merge · Pre-ship checks · Runs inside your environment
Canon learns your standards as your team codes, from reverts, incidents, and review corrections, then applies them the instant an agent writes. Your developers change nothing and just get better output.
Generating code is cheap. Reviewing it is expensive. And it lands on your seniors.
Your AI writes a real share of your code now. The bottleneck isn't writing anymore. It's trusting and reviewing what it ships.
Agents can generate ten times more, but only the people who know your standards can check it. That's your most senior engineers, and every hour they spend re-reading generated diffs is an hour off the roadmap. You got speed on the writing and gave it back on the reviewing.
The same correction gets made in review, again and again, because the reasoning behind it lives in one engineer's head or a PR thread nobody re-reads. Every agent, on every tool, relearns it the hard way. Or never does.
Cursor solves it one way, Copilot another, Claude Code a third. Each is fine alone. Together your codebase turns into five dialects, and the one your team agreed on isn't always the one that ships. Rules files help until they rot, and no one can tell whether the model even read them.
Without a way to know which standards a change followed, every line is guilty until proven innocent. That's the babysitting tax, and it grows every month with the volume your agents produce.
'Our AI ships more code every week. How much of it can we actually trust without a senior re-reading every line?' Right now, that question has no clean answer.
The better your AI gets, the more this matters.
Coding agents get faster and more autonomous every month, writing more of your code with less of you watching. That's the upside, but only if you can trust what they ship. Canon is the layer that makes trust scale with capability: the more your tools can do, the more you can safely hand them. It's the rare thing that gets more necessary as the models improve, not less.
of teams use AI coding tools, but only 30% can fully govern what those tools write.
more likely to report major efficiency gains: teams with full governance over their AI-generated code.
One living standard. Every tool held to it.
Steering and enforcing aren't two systems. They're one standard, applied with the right force: a nudge when that's enough, a course-correction when it drifts, a block when it must be.
It learns how your team builds, on its own.
Canon starts with the rules you set, your security and architectural non-negotiables, so it works on day one. From there it keeps learning on its own, from how your team actually codes, your review corrections, and your resolved incidents. No one writes documentation, no one maintains a rules file. The reasoning in your seniors' heads becomes context every agent can use, at no extra cost to anyone.
Steer it before it goes wrong.
Every agent gets the relevant standards as context before it generates, so it builds like your senior engineer, not the median of the internet. It runs inside the tools your developers already use, with no new step, plugin, or process to adopt. They feel nothing different and just get better output.
Enforce and prove.
Canon meets each action with the right force: guidance as the agent writes, a course-correction when it drifts, a hard block at the merge gate when it must. Then it logs what each agent did and which standard it followed, across every tool.
Inform when it can. Enforce when it must. Prove it always.
IN-EDITOR · PRE-MERGE · PRE-SHIP CHECKS
The PR arrives already checked against your standards.
Reviewers today do two jobs mashed together: mechanical conformance checking, and genuine judgment. The first is tedious, only seniors can do it, and it's drowning the second.
Canon does the conformance sweep and shows its work, right in the PR. 14 standards met, 2 flagged, with the reasoning and provenance attached. Your reviewer trusts the passes the way they trust a passing test suite, goes straight to the flags, and spends their brain on the judgment call only a human can make.
Trust is earned, not demanded: hard blocks come only from deterministic, human-approved rules, semantic checks surface for a human instead of auto-approving, and the override rate is tracked so you can watch it fall.
A rules file is a suggestion. Canon is a closed loop.
CLAUDE.md, skills, and cursor rules are useful, and Canon works alongside them. But they can't remove the review bottleneck, because they can't give your reviewer proof.
You hope the model read it.
A rules file is context you attach and cross your fingers over.
- ×No record of whether any rule was actually followed
- ×Goes stale the moment your team's practice moves on
- ×One file per tool, drifting apart, maintained by hand
- ×Your reviewer still has to re-check every line
You know what it followed.
One standard, applied at write time, verified at the gate, proven in the PR.
- ✓Every change checked against the standard, result shown in the PR
- ✓Self-updating: learns from your reverts, incidents, and corrections
- ✓One standard held across every tool your team uses
- ✓Your reviewer checks the flagged parts, not every line
That proof is the whole point. It's what lets a reviewer stop re-checking everything, and that's what removes the bottleneck. A rules file can never give you that.
The reasoning that lives in one engineer's head, working for every agent.
When a senior figures out why something has to be done a certain way, the architecture call, the error path they hit, the "we tried that and it broke," that knowledge normally dies in their head or in a PR thread no one re-reads. So every agent relearns it the hard way, or never does.
Canon captures that reasoning from the work your team already does and gives it to every agent on your codebase. The hard-won knowledge of your best engineers stops being trapped in one person and starts building all your code. No agent re-solves a solved problem. No agent repeats a known mistake.
A senior corrects a teammate: payment retries must go through the idempotency layer, or we double-charge. We learned that the hard way.
After it recurs, it becomes a proposed standard with the reasoning attached. You approve it in seconds.
A different engineer's Cursor agent writes a refund retry and gets it right the first time, because Canon gave it the rule, and the reason, before it wrote a line.
No one re-explained the decision. No one caught it in review. The standard just held.
Steer everywhere. Block where we can. Prove always.
No tool can control every AI coding assistant equally. We don't pretend otherwise. We tell you exactly what Canon can and can't enforce, tool by tool.
Steer everywhere. Nothing lands unchecked.
Every agent gets the relevant standards before it writes. Whatever tool produced the code, every change passes the merge gate, re-checked against your standards. The floor under everything.
Block where we can.
On Cursor, Codex, and Claude Code, Canon sits in the loop and blocks a violation before it's written. On Copilot and other closed tools, no one can block in-editor, so Canon steers via MCP and backstops at the merge gate.
Built for teams whose AI writes real code across more than one tool.
- ✓You run more than one AI coding tool (Cursor, Claude Code, Copilot) across the team.
- ✓AI writes a real share of your merged code, and review load is piling up on your senior engineers.
- ✓A senior keeps making the same review corrections, and they don't stick.
- ✓You want to hand your agents more, but can't yet trust what they ship without reviewing all of it.
· Solo devs or single-tool teams
· No AI coding tools in use yet
· Teams with no shared conventions to enforce
Join the waitlist
Be among the first teams to keep every AI coding tool building the way your team actually works.
Stop checking everything your AI writes. Start trusting it.
See Canon learn your standards from your own repo, enforce them across every tool, and prove what each agent did, so you can trust your AI with more.