Continuous AI
AI-powered code quality that scales with your pipeline
What is Continuous AI?
Continuous AI is a source-controlled quality platform that runs custom checks on every pull request—without the noise of generic AI reviews. Unlike tools that bombard developers with unsolicited suggestions, Continuous AI only enforces the rules your team defines, written as plain markdown files stored right in your repository. The setup is refreshingly straightforward. You create check definitions in a `.continuous/` directory, and they become native GitHub status checks with actionable inline fix suggestions. From coding standards and security rules to accessibility compliance and API rate-limiting validation, the platform executes your team's exact specifications on every PR. There's no black-box AI guessing—just consistent, transparent enforcement of the standards you already use. Continuous AI also ships with AI agents that can be created and run on demand, plus integrations with Slack, Sentry, Snyk, and Gmail to extend automation beyond code review. For teams that need governance, the platform offers centralized agent management, per-seat access controls, and enterprise-grade security features including SAML/OIDC SSO and Bring-Your-Own-Key (BYOK) support. Pricing is flexible—start with pay-as-you-go token billing at $3 per million tokens, upgrade to the Team plan at $20 per seat per month for centralized management, or go custom for enterprise needs. If you're a GitHub-powered engineering team tired of noisy AI reviews and want automated, customizable quality gates that actually scale with your velocity, Continuous AI is worth a serious look.
How to Use Continuous AI
Continuous AI makes it dead simple to automate your team's code quality standards directly inside GitHub. Here's how to get your first custom check up and running in just a few minutes.
Install the GitHub App
Head to the Continuous AI dashboard and install the GitHub App on your target repositories. You'll need admin access to grant the necessary pull request and status check permissions so the platform can read PRs and post results.
Define your first check in markdown
Create a `.continuous/` folder in your repository root and add a markdown file describing the rule. For example, a check that enforces TypeScript strict mode can be written in plain English with expected behavior and fix instructions that the AI will follow.
Configure check behavior and scope
Specify whether the check should block merging on failure or simply warn. You can also target specific branches or file patterns to keep checks focused and efficient for your team's workflow.
Trigger a pull request and review results
Open a new PR and watch Continuous AI automatically execute your check. Results appear as GitHub status checks with inline annotations showing exactly where code deviates from your standards, along with suggested fixes.
Iterate and expand your rule library
Review the results, tweak your markdown rules based on feedback, and add more checks for security, accessibility, or performance. Your rules evolve with your codebase, and the platform scales with your team.
Continuous AI Core Features
Continuous AI Use Cases
- 1Enforce coding standards and style guidelines automatically on every pull request, ensuring consistent code quality across your entire engineering team without manual review bottlenecks.
- 2Run security vulnerability scans as part of your CI pipeline using integrations like Snyk, catching issues before they reach production and reducing security debt over time.
- 3Validate API rate-limiting middleware configurations automatically, preventing deployment of endpoints that could lead to abuse or performance degradation in production.
- 4Ensure accessibility compliance for front-end code by running automated checks that flag WCAG violations and suggest fixes directly in the pull request workflow.
- 5Centralize governance of AI agents across teams, controlling which agents developers can use while maintaining enterprise compliance with SSO and BYOK requirements.
Pros and Cons of Continuous AI
Pros
- Scales automatically with your development velocity—checks run on every PR without slowing down the pipeline, from small teams to enterprise organizations shipping hundreds of pull requests daily.
- Zero noise from irrelevant AI suggestions—you define exactly what rules to enforce, so developers only see feedback that matters to your team's specific standards.
- Actionable inline fix suggestions embedded directly in GitHub PRs reduce context switching and help developers fix issues without leaving their workflow.
- Flexible pricing accommodates every stage of growth, from pay-as-you-go token billing to enterprise contracts with custom SLAs, SSO, and BYOK support.
✕ Cons
- Teams must invest time in authoring and maintaining markdown-based check definitions, which requires technical fluency and ongoing iteration as standards evolve.
- Currently limited to GitHub integration with no support for GitLab, Bitbucket, or other version control platforms mentioned in the documentation.
- Enterprise features like custom SSO and BYOK are locked behind custom pricing with no transparent cost details, making budget planning challenging for procurement teams.
Continuous AI vs Top Alternatives
| Feature | CodeRabbit | GitHub Copilot Code Review | SonarQube |
|---|---|---|---|
| Check definition method | Pre-built AI review rules | AI-generated suggestions | Static analysis rules (customizable) |
| Inline code fixes | Yes, inline comments | Yes, inline suggestions | No (report-based) |
| Starting price | $12 per user/month | $10 per user/month (Copilot) | Free (Community Edition) |
| Enterprise security | SSO in Enterprise plan | Included in GitHub Enterprise | Available in paid tiers |
Continuous AI Pricing
Starter
- Pay-as-you-go token billing
- AI agents creation and run
- Integration connectors
- Purchase credits for frontier models
Team
- $10 in credits per seat included
- Private agent sharing
- Centralized agent management
- Agent access control
- Gmail/GitHub SSO login
Company
- Custom SSO (SAML/OIDC)
- Bring-Your-Own-Key (BYOK)
- Contractual commitment options
- Custom invoicing
- Custom SLA support
Continuous AI FAQ
What is Continuous AI?+
How does Continuous AI pricing work?+
Does Continuous AI support GitLab or Bitbucket?+
Can I use my own API keys with Continuous AI?+
What kind of checks can I create with Continuous AI?+
Is Continuous AI open source?+
How does Continuous AI differ from generic AI code review tools?+
Continuous AI Review — Editor's Score
Who Should Use Continuous AI?
Engineering teams on GitHub who want to automate code quality enforcement without relying on generic AI review tools. Best suited for teams that already have defined coding standards and want to scale their review process without adding headcount.
Continuous AI delivers exactly what high-velocity engineering teams need: automated, customizable code quality checks that don't add noise. Its markdown-based check system is elegant for developers who know what they want but has a learning curve for teams new to defining standards programmatically. If you're a GitHub shop looking to enforce rules without slowing down, this is a solid investment.
- Markdown-based checks version-controlled in your repository
- Native GitHub status checks with inline fix suggestions
- Pay-as-you-go pricing with no hidden minimums
- Enterprise-grade SSO and BYOK for regulated environments
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