AI-powered technical due diligence for software teams

AI Repo Analyzer that audits software repositories for architecture, security, complexity, testing, and technical debt—turning hours of manual review into an evidence-backed report in minutes.

Comment

Inspiration

Software is becoming easier to build, but understanding an unfamiliar codebase is still expensive and time-consuming.

When a company evaluates a startup, hires engineers, takes over a vendor codebase, or inherits an internal system, senior developers may need to spend hours manually studying files, dependencies, architecture, security risks, tests, and technical debt.

We wanted to build a tool that makes this technical due diligence faster, more systematic, and easier to audit.

That led us to build AI Repo Analyzer — an autonomous software-auditing platform that combines deterministic code analysis with AI reasoning and verification.

What it does

AI Repo Analyzer takes a GitHub repository and produces an evidence-backed technical assessment.

The system can analyze:

  • Programming languages and project structure
  • Dependencies and frameworks
  • Architecture and entry points
  • Cyclomatic complexity and maintainability
  • Security risks and suspicious patterns
  • Test execution results
  • Technical debt and engineering risks
  • AI-generated recommendations
  • Architecture visualizations
  • A downloadable technical due-diligence report

The important part is that AI is not trusted blindly.

Deterministic analysis engines inspect the actual repository, while verification steps check AI-generated findings against source-code evidence. This helps reduce unsupported claims and makes the final report more useful for technical decision-making.

A typical workflow is:

GitHub Repository → Repository Ingestion → Static Analysis → Security & Complexity Analysis → Sandbox Testing → AI Analysis → Verification → Technical Report

How we built it

We built AI Repo Analyzer as a multi-agent software analysis system with a web interface and backend API.

Analysis layer

The repository is cloned into a temporary workspace and analyzed using deterministic tooling.

We inspect:

  • File structure
  • Programming languages
  • Dependencies and manifests
  • Imports and entry points
  • Architecture relationships
  • Complexity and maintainability
  • Security-related patterns
  • Tests and execution results

AI layer

An LLM is used to enrich the deterministic findings with higher-level explanations, summaries, recommendations, and risk analysis.

The architecture uses a pluggable model-provider approach so the system can work with different LLM backends.

Verification layer

Generated findings are checked against repository evidence before being included in the final assessment.

This is especially important for code-analysis systems because a fluent AI response is not enough when the output is being used for engineering or business decisions.

Application layer

The backend exposes APIs for repository analysis, while the interface provides an interactive experience for starting an analysis and viewing results.

We also added real-time execution updates, generated architecture diagrams, report generation, Docker support, automated testing, and CI integration.

Challenges we ran into

The biggest challenge was making AI useful without allowing it to become the source of truth.

A normal LLM can produce convincing explanations while inventing file paths, code references, or technical findings. That is unacceptable for technical due diligence.

We therefore had to separate deterministic analysis from AI interpretation and introduce verification around the generated findings.

Another challenge was handling repositories with very different structures, languages, dependency systems, and test configurations.

We also had to think about safe execution, temporary workspaces, containerization, runtime configuration, and making the system reproducible for other developers.

Accomplishments that we're proud of

We are proud that the project is not just an LLM wrapper.

It combines several engineering disciplines into one working pipeline:

  • Static program analysis
  • Security analysis
  • Complexity analysis
  • Sandboxed test execution
  • Multi-agent orchestration
  • LLM reasoning
  • Evidence verification
  • Architecture graph generation
  • Real-time application updates
  • Automated report generation
  • Dockerized deployment
  • Automated tests and CI

We also built the system so that the analysis results can be consumed as a practical technical report rather than only as a chat response.

What we learned

We learned that building an AI product is often less about generating text and more about building the systems around the model.

Reliable AI applications need:

evidence → deterministic tooling → model reasoning → verification → user-facing output

We also learned that scalability and trust have to be considered from the beginning.

A useful technical product needs reproducible analysis, controlled execution, clear failure handling, and outputs that a user can actually act on.

What's next for AI Repo Analyzer

We see AI Repo Analyzer evolving from a repository auditing tool into a broader technical intelligence platform.

Future versions could include:

  • Continuous repository monitoring
  • Pull-request risk analysis
  • CI/CD integration
  • Security and compliance policies
  • Team and organization dashboards
  • Historical technical-debt tracking
  • Automated remediation suggestions
  • API access for engineering platforms
  • Enterprise authentication and access controls

The long-term vision is to make software due diligence something that can happen continuously rather than only when someone has to manually inspect a codebase.

Built With

Share this project:

Updates

Submission history