Accelerating In-House Development with AI: Preventing Quality Black Boxes in Code Audits

Accelerating In-House Development with AI

In the rapidly evolving landscape of technology, the integration of generative AI, such as Cursor and GitHub Copilot, into code development has transformed the approach to in-house software solutions. However, with this shift comes the critical challenge of ensuring the quality and security of AI-generated code. Ludo Technologies, based in Kanagawa, Japan, aims to tackle this issue head-on with the launch of its beta version of the code auditing platform, pyscn Enterprise, which will be offered free of charge to a select group of ten companies.

The AI Code Quality Conundrum

As generative AI tools become increasingly common in code development, many organizations are finding it challenging to maintain robust quality management systems. A survey conducted by SonarSource in January 2026 revealed that a staggering 96% of developers do not trust the accuracy of AI-generated code. Moreover, only 48% of developers conduct verification before committing code, despite the fact that AI tools already generate or assist in 42% of code.

This growing Verification Gap poses a significant risk that companies often remain unaware of. AI-generated code may be unverified, leading to potential pitfalls in production environments. Furthermore, an evaluation by BaxBench indicates that 41% to 62% of AI-generated code may harbor security vulnerabilities. Werner Vogels, Amazon's CTO, has been vocal about the dangers of accepting AI-generated code without scrutiny, characterizing it as a form of gambling rather than true software engineering.

With development cycles accelerating, very few organizations can accurately convey the current state of their code quality to upper management, leaving them susceptible to unrecognized risks.

Introducing pyscn Enterprise

The pyscn Enterprise platform is designed to furnish companies with an effective tool for managing the quality of AI-generated code. By combining the widely-used static analysis engine, pyscn, with advanced AI agents, the platform facilitates continuous visibility into the overall health of codebases. It ensures that executives can readily explain the state of their code quality at any given moment.

Key Features:

1. Dashboard - This feature allows for the visualization and tracking of code quality over time. Users can easily assess Health Scores for repositories, along with breakdowns of risks by importance and weekly changes, providing immediate clarity on improvement or deterioration, even without technical expertise.
2. Weekly Code Audit Reports - The system conducts an automatic scan of the entire codebase weekly to identify issues such as circular references, code clones, dead code, complexity metrics, and architectural violations. Results are generated as GitHub Issues for efficient tracking.
3. Automated Pull Request Reviews - To maintain the quality standards outlined in previous audits, pyscn Enterprise provides automated reviews via static analysis and AI for pull requests.
4. Implementation Support - During the beta phase, Ludo Technologies will offer assistance in incorporating, configuring, and operating the system.

Analysis with pyscn

An example analysis conducted with pyscn on a sample repository demonstrates the tool's efficacy in providing an overview of code quality through Health Scores. Weekly code audit reports compile executive summaries with risk assessments and suggested improvement actions, while a management portal allows for comparison and monitoring of multiple repositories.

The Limitations of Traditional Approaches

Currently, many organizations leave the task of code quality management to individual developers, lacking a comprehensive understanding of the codebase's condition. This often leads to an inability to respond to inquiries regarding the status of the organization's code effectively. pyscn Enterprise aims to rectify this blind spot by quantifying these metrics systematically.

Beta Version Overview

- Target Audience: Companies operating Python projects (no industry restrictions).
- Number of Offerings: Ten companies will be selected.
- Cost: Free of charge.
- Conditions: Participants must agree to a brief survey regarding usage.
- Selection Criteria: Must be operating a Python project with a development team of at least two members.