Software Quality
& Security
powered by Explainable AI

We combine measurable indicators with expert interpretation to build a defensible picture of software quality and security. The evaluation covers code, architecture, dependencies, development practices, and relevant risks, translating findings into clear priorities and an improvement roadmap that teams can track over time.

We analyse source-code, architectural, process, and historical evidence to reveal where Technical Debt is concentrated, why it emerged, and how it may affect future change. The outcome is a prioritised view that helps teams balance remediation effort, business value, and delivery risk.

We help teams treat security as an engineering quality from the beginning—not as a final compliance step. This includes threat-aware requirements, architectural analysis, secure development practices, and verification activities embedded throughout delivery.

Analysis techniques identify suspicious code and dependency risks, then support engineers in understanding root causes and evaluating possible repairs. Human review remains central so proposed changes can be assessed against system context and operational constraints.

Our methods make recommendations traceable to the evidence and reasoning behind them. Engineers can inspect explanations, contribute contextual knowledge, and use that feedback to improve both the model and the decision-making process.

We design AI-enabled solutions around real engineering workflows, from knowledge extraction and code analysis to decision support. Emphasis is placed on explainability, evaluation, human oversight, and integration with the tools development teams already use.