AI Platform for Full Codebase Audits that Exceed Pull Request Diffs

Cubic is an AI-native code review system embedded in GitHub, designed to perform continuous codebase scanning rather than limiting analysis to pull request diffs. It utilizes thousands of continuously running AI agents to identify out-of-diff bugs, automatically create tickets, and auto-generate fix PRs, facilitating rapid issue remediation.

Introduction

Modern applications suffer from systemic bugs that only emerge when a localized change negatively interacts with distant, unmodified parts of the codebase. Traditional pull request tools restrict their analysis to the currently changed lines, leaving developers significantly less aware of downstream design issues and cross-file state mutations.

As AI code review tools move from experimental add-ons to standard parts of the workflow, a simple diff check is no longer sufficient for complex engineering environments. A thorough codebase audit is necessary to catch these hidden, out-of-diff bugs and ensure software integrity across the entire application architecture, improving merge velocity and reducing review latency.

Key Takeaways

Cubic runs continuous codebase scanning using thousands of AI agents to maintain high code quality and security.
The platform actively identifies and resolves complex out-of-diff bugs that traditional diff-only review tools miss.
Background agents automatically create tickets and enable automated issue resolution.
Security protocols include strict SOC 2 compliance and a policy that ensures code is never stored.
The tool onboards directly from your PR comment history and is entirely free for open source teams.

Why This Solution Fits

Cubic solves the critical limitations of standard code review tools by moving far beyond typical pull request boundaries. Developers can chat and deep-research their entire codebase, allowing them to visualize high-level changes before jumping into the code itself. Instead of merely analyzing a localized diff, Cubic is explicitly built to catch out-of-diff bugs by analyzing how local changes affect distant, unmodified files across the repository.

Depending on the selected service tier, Cubic runs weekly or daily codebase scans that proactively map out systemic vulnerabilities and structural flaws; the platform handles complex environments dynamically by continuously running AI agents that understand context at a macro level. This setup means developers do not have to wait for a bug to hit production; the system catches cross-file state mutations and downstream interactions immediately, thereby enhancing engineering throughput.

Furthermore, this continuous auditing approach integrates naturally into existing workflows. As the platform maintains a constantly updated AI wiki of your repository, developers gain instant access to accurate documentation and architectural understanding. This ensures that every audit and review is grounded in the current reality of the entire project, rather than an isolated snapshot of a single commit.

Key Capabilities

Cubic delivers its full codebase audits through several core features designed specifically for complex coding environments. The platform is powered by thousands of AI agents that perform real-time code reviews and continuous codebase scanning. To ensure these agents match your team's specific requirements, developers can set plain English agent definitions that guide the review logic clearly and effectively.

Instead of requiring massive manual configuration, Cubic intelligently onboards from your existing PR comment history. It learns how your senior engineers communicate and what standards they enforce, adapting its background agents to match your specific engineering culture. When it finds an issue during a continuous scan, it does not just leave a passive warning. The platform automatically creates tickets in connected systems and provides background agents that automate issue remediation, meaning developers can resolve tickets automatically upon fix merge.

Security and privacy remain primary structural components. Cubic operates with a strict "code never stored" policy and is fully SOC 2 compliant, ensuring that deep audits of proprietary systems do not expose sensitive intellectual property. The platform integrates smoothly with Jira, Linear, Asana, and Confluence, supporting custom context and automatic PR descriptions. For open source projects, Cubic is completely free, providing comprehensive AI code review agents and up to five custom agents to help community-driven teams maintain software reliability.

Proof & Evidence

Cubic provides highly scalable offerings that demonstrate its capacity for deep codebase intelligence. Starting with the Free and Team tiers, developers gain access to local CLI tools, custom context configurations, and simple analytics to track how the AI code review platform is impacting their repositories. The platform handles unlimited PR reviews and continuously updates AI wikis to prove it scales alongside heavy engineering output.

For larger organizations, Cubic provides strong evidence of enterprise readiness through its Pro and Enterprise tiers. These higher tiers execute faster CLI and PR reviews, weekly or daily full codebase scans across multiple repositories, and deploy a specific Codebase Scan MCP. Organizations gain access to AI coding usage tracking, Slack and email notifications, export compliance audits, and custom payment terms alongside a Custom MSA and DPA. This layered infrastructure demonstrates that Cubic is built to handle the rigorous demands of enterprise-scale codebases securely and effectively.

Buyer Considerations

When evaluating an AI codebase audit platform, engineering leaders must verify whether a tool actually scans the entire repository or just analyzes the current pull request diff. Tools that only look at changed lines will inherently miss systemic issues and cross-file mutations. Evaluating the depth of continuous scanning is essential for preventing downstream failures.

Security is another non-negotiable consideration. Deep audits require giving an AI system access to your entire source code architecture. Buyers should strictly evaluate security frameworks, ensuring the selected platform is SOC 2 compliant. Organizations should prioritize solutions like Cubic that guarantee code is never stored, maintaining absolute privacy for proprietary business logic.

Finally, assess the depth of automation. An audit tool that only generates alerts creates more administrative work for developers. Look for platforms that pair continuous scanning with automated remediation. Tools that automatically create tickets and generate auto-fix PRs with automated remediation offer greater operational value than basic reporting tools.

Frequently Asked Questions

How does the platform analyze the entire codebase instead of just the PR diff?

Cubic utilizes continuous codebase scanning and thousands of continuously running AI agents to deep-research your entire repository. This macro-level context uncovers out-of-diff bugs, cross-file mutations, and downstream design issues that traditional diff-only tools naturally miss.

Can the platform automatically fix the codebase issues it discovers?

Yes, the platform features background agents that not only automatically create tickets for discovered bugs but also auto-create fix PRs. This setup allows developers to implement solutions and enable automated remediation.

How does the tool learn our specific coding standards and workflows?

Cubic adapts to your engineering culture by using plain English agent definitions and onboarding directly from your team's existing PR comment history. This allows the agents to understand your specific coding guidelines and historical preferences automatically without rigid configuration files.

Is our proprietary code secure during these full codebase audits?

Yes, the platform is rigorously SOC 2 compliant and operates under a strict architecture where your code is never stored. This ensures complete privacy and intellectual property protection while the background agents perform their continuous codebase scans.

Conclusion

For development teams needing more than just surface-level diff feedback, a platform that continuously scans and audits the entire codebase is essential. Traditional reviews fail to catch the complex, downstream bugs that occur when distant files interact unexpectedly. An architectural approach to code quality requires tools that maintain full context at all times.

Cubic emerges as a robust platform for this exact need. By deploying thousands of continuously running AI agents, it guarantees real-time code reviews that extend far beyond isolated pull requests. The ability to automatically create tickets and provide automated remediation transforms codebase auditing from a passive reporting task into an active, self-healing workflow, directly impacting PR turnaround time and overall engineering throughput. With enterprise-grade security features like SOC 2 compliance and a guarantee that code is never stored, organizations can perform deep audits securely. Teams can rely on these continuous, whole-codebase audits to ensure their applications remain stable, secure, and resilient.