AI-Assisted Legacy Audit
We scan the agreed repository scope in hours, not weeks. AI tools analyze 100k+ lines of code, an expert interprets the results, sets priorities, and takes responsibility for every recommendation.
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Why does a traditional
legacy code audit fail?
Hundreds of thousands of lines of code, no documentation, team turnover — a traditional manual review-based audit is too slow, too expensive, and only covers a sample.
Traditional audit
takes weeks or months
An expert reads as much code as the budget allows. With 200k+ lines, a thorough manual review takes 10–14 weeks and tens of thousands of dollars. You wait too long for results.
Covers only a sample,
not the whole codebase
Manual audit covers 20–40% of code. The rest remains unexplored — hidden dependencies, forgotten modules, and outdated libraries stay invisible.
A report without priorities
is just a list of problems
Hundreds of findings without prioritization and ROI are useless for management. You need to know: what to fix first, how much it costs, and what return it will bring.
* Gartner Software Engineering Research, McKinsey Technology
Choose your
level of detail
From a quick 24-hour health check to a full audit with an implementation roadmap. The cost of the Baseline Report counts toward the full audit.
Baseline Report
(24h)
Automated application scan in 24 hours. Top 10 problems, CVE vulnerabilities, and code quality metrics — the ideal starting point.
- Static code analysis (SonarQube)
- CVE scanning and dependency audit
- Health Score (0–100)
- Top 10 problems with priorities
AI-Assisted Audit
(4–5 days)
AI scans the agreed code scope in 2–4h, an expert interprets every significant finding and delivers a plan with priorities and ROI estimates for each recommendation.
- Full coverage of the agreed scope
- Expert interprets every finding
- Priorities with work hours and ROI estimates
- Executive Summary + Technical Deep Dive
- Q&A session (60 min) with expert
Audit + Implementation
Roadmap
Full AI audit with a ready implementation roadmap — sprint plan, estimates, defined dependencies. Your team knows exactly what to do and in what order.
- Everything from the AI-Assisted Audit
- Ready sprint plan with estimates
- Defined task dependencies
- Import to JIRA / Linear / GitHub Projects
- Kickoff with the development team
How does the AI Legacy Audit work?
AI scans, expert interprets —
full report in 4–5 business days.
Repository access
We receive read-only access to the code. We sign an NDA. No need to involve your team in subsequent steps.
AI scan (2–4h)
AI tools analyze source files in the agreed scope: code quality, dependencies, anti-patterns, cyclomatic complexity, and CVEs.
Expert interpretation
An expert verifies every significant AI finding, separates symptoms from root causes, and prioritizes based on business impact and remediation cost.
Report + Q&A session
We deliver a PDF + Markdown report with executive summary and technical deep dive. We conduct a 60-minute Q&A session with your team.
Why is an AI audit
better than traditional?
Full agreed scope instead of a sample
AI reviews the repository scope agreed before the audit, not a random sample. Hidden dependencies, forgotten modules, and outdated libraries are easier to spot and prioritize.
Expert interprets, doesn't guess
AI identifies anomalies — an expert decides what's actually a problem. Every recommendation is verified by a human with experience in production systems.
Priorities with concrete ROI
Every recommendation has an estimated remediation cost (hours), time-to-fix, and potential ROI. Management gets numbers to make decisions.
Faster and cheaper than traditional
4–5 days vs. 10–14 weeks. Full agreed scope instead of a random sample — at a lower cost. Get the results you need to act, not a document that sits on a shelf.
Real projects, measurable outcomes
Before talking about methods, we show the outcome: source-code control restored, critical operations shortened, and deployments standardized.
time (45 → 10 min)
Modernizing a mature postal-logistics monolith
Migration to a supported stack (Java 17, Spring Boot 3), optimization of critical operations, and a single CI/CD process for the whole modular system.
and handed to the client
Cravtt Property — regaining control of the codebase
We recovered and organized the client's source code, documented the project, and delivered a technical and business audit with a clear recommendation.
Want to know what to fix — in 5 days, not 5 weeks?
AI scans the agreed code scope, an expert interprets the results and delivers a concrete action plan with priorities and estimated ROI.
Book a diagnostic call