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Autter enforces your rules, patterns, and conventions — and learns from your team over time. Define standards in plain English and get consistent, codebase-aware reviews across every repository, on every pull request.

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Enforce standards in plain English

Write rules the way you’d explain them to a colleague. No regex, no YAML, no configuration overhead. Autter understands what you mean and applies your rules across every repo. Here are examples of rules teams enforce with Autter today:
RuleCategory
Detect security vulnerabilitiesSecurity
No direct process.env access — environment variables must be accessed through the config moduleSecurity
Require error boundaries on all async UI componentsReliability
You can scope rules to specific repositories. A rule that applies to 5 repos can be configured once and maintained in one place.

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Team learning

Autter watches how your team reviews code and picks up patterns, preferences, and institutional knowledge over time. As your team approves and rejects changes, Autter builds a model of what “good” looks like in your codebase. Examples of what Autter learns from your team’s review activity:
  • Include demo videos for UI changes — learned from reviewers consistently requesting recordings before approving frontend PRs
  • Always include tests for payment flows — learned from senior engineers blocking payment-related PRs without test coverage
  • Inconsistent invalidation implementation between team and non-team schedules — learned from multiple review threads flagging the same architectural inconsistency
These patterns are applied automatically to future PRs — so the standard your best reviewer holds is the standard every PR is held to.

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Onboards from senior developer comments

Autter reads your senior developers’ PR comment history and learns from it directly. When a new hire opens their first PR, they get the same quality of feedback they’d get from your most experienced team member — not a generic linter. This means:
  • New developers learn your actual conventions from day one, not from documentation that may be outdated
  • Senior engineers spend less time repeating the same feedback
  • The review bar stays consistent regardless of who reviews a PR
Autter adapts feedback verbosity based on a developer’s history with the codebase. New contributors get detailed explanations with examples; experienced contributors get concise flags.

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Catching AI-generated code issues

AI coding assistants generate between 30% and 60% of code in a typical PR today. This code compiles, passes tests, and reads cleanly — but can fail in ways that only surface under production load. Autter operates at the merge layer to catch issues traditional CI misses: 
// Autter flags this pattern automatically
async function getTeamMembers(teamIds: string[]) {
  // N+1 query — will execute one DB call per team ID
  // Autter suggests: use db.teams.findMany({ where: { id: { in: teamIds } } })
  const members = [];
  for (const id of teamIds) {
    const team = await db.teams.findUnique({ where: { id } });
    members.push(...team.members);
  }
  return members;
}
Autter has seen your codebase use findMany with in clauses in dozens of other places. It knows this loop generates one query per teamId and flags it — not because loops are bad, but because this specific pattern in this specific codebase is a performance regression.

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Convention drift detection

Every codebase has unwritten rules. Autter learns them from your merge history and surfaces violations automatically:
What Autter detectsExample
Deprecated API usageAI used legacy.createUser() instead of auth.register()
Naming convention violationscamelCase in a module that uses snake_case throughout
Import path deviationsDirect import from @internal/db instead of your team’s @app/data facade
Error handling pattern breaksThrowing raw errors where the codebase wraps them in AppError
Test pattern mismatchesUnit test where integration tests are the established standard

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Everything you need for better reviews

Instant feedback

Autter automatically scans opened PRs for bugs, logical errors, and other technical pitfalls so reviewers can focus on the big picture.

Codebase awareness

Every piece of feedback makes sense in the context of your codebase. No generic suggestions — only relevant insights.

Custom rules

Define and enforce custom patterns with AI prompts and regex. Your standards, consistently applied across every PR.

PR summaries

Get a TL;DR with a summary of changes, a walkthrough, and an architectural diagram for every pull request.

Team learning

Autter builds institutional knowledge from your team’s review patterns and applies it consistently to every PR.

Documentation aware

Reviews consider your internal docs, README files, and architecture decision records for full context.