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Modernization · Technology company

AI-Assisted Codebase Audit

AI-assisted static analysis, dependency mapping, and senior architecture review to surface maintainability, performance, and security risks.

MODERNIZATION
100%
files analysed
142
issues prioritised
3 weeks
audit duration end-to-end
The challenge

What the client was facing

A scale-up's CTO needed an honest answer to a hard question: is our codebase ready for the next 3 years, or is technical debt about to slow us down?

What we built

The solution

  • AI-assisted static analysis across the full repo: dependencies, hotspots, smells
  • Senior architects reviewed every AI finding to remove false positives
  • Prioritised backlog: security, performance, maintainability, scalability
  • Effort + business-impact estimates for each item
In production

What it looks like

Illustrative screens — actual client UI, branding, and data redacted under NDA.

AI-Assisted Codebase Audit 1 2 3 4 5 6 7 8class ClaimsExtractor: def __init__(self, llm, schema): self.llm = llm self.schema = schema def extract(self, document): prompt = self.build_prompt(document) raw = self.llm.complete(prompt) return self.schema.validate(raw)AI suggestion ▸
AI-Assisted Codebase Audit — analyticsOverview82%Accuracy3.2kItems12Today4.7ScoreTrend
AI-first delivery angle

Why AI-first mattered here

AI processed thousands of files in hours instead of weeks. The senior review step filtered noise. The deliverable was an actionable backlog, not a 200-page report nobody reads.

Technologies

Stack

TypeScript SonarQube Claude Semgrep GitHub Actions

Have a similar problem to solve?

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