We are a team with 5 years of experience integrating AI into development processes. Our solutions are certified and ensure metric transparency. We develop Tech Debt AI — an AI debt management system that makes technical debt measurable, prioritized, and manageable like a regular backlog. Technical debt is a concept in software development that reflects the implied cost of additional rework caused by choosing an easy solution now instead of using a better approach that would take longer. Our clients face a situation: any new feature takes 3–4 times longer than expected, 70% of time is spent understanding legacy code, and management cannot see the reasons for slowdown. The system solves this — performs automated code analysis to detect issues, identifies debt, and generates a repayment plan. Average development budget savings is 40%, operational costs drop by 30%. The system is available from $500/month per repository. For a mid-size team, typical annual savings reach $50,000, delivering a 2:1 ROI in the first quarter.
Get a detailed audit of your repository — we'll show how AI helps reduce the debt metric. Contact us for a no-obligation consultation and we'll evaluate your project.
Problems we solve
Unmeasurable debt. Without numerical metrics, it's hard to convince the team and management to allocate time for refactoring. Our code analysis introduces the debt metric (TD index) — the ratio of total person-hours to the number of files. An index above 3.0 signals a critical state.
Blind prioritization. Developers often pick "interesting" tasks instead of the most critical ones. Our debt prioritization algorithm considers severity, effort, and business impact. Quick wins (vulnerabilities) get top priority — each can be closed in 0.5 hours. AI ranking is 3 times faster than manual audit.
Management resistance. Without specific numbers, leadership does not see the ROI of paying down debt. The system generates Jira tasks with story points, acceptance criteria, and labels — debt becomes part of the regular sprint. Average development budget savings is 40%, operational costs drop by 30%.
How AI evaluates architectural debt
We use a combination of code analysis (static analysis) and LLM for code (Claude Sonnet, GPT-4). First, we find files with high cyclomatic complexity via radon (threshold CC > 10). Then Claude Sonnet analyzes the structure of large files (>500 lines) for God Objects and module boundary violations. The result is a JSON with issue type, severity, and recommendations.
# Fragment: AI analysis of architectural issues response = client.messages.create( model="claude-sonnet-4-5", max_tokens=2048, messages=[{ "role": "user", "content": f"Analyze the list of large files for architectural issues.\n\nFiles (path, line count):\n{json.dumps(large_files, ensure_ascii=False)}\n\nReturn JSON:\n[{{\n \"file\": \"...\",\n \"issue\": \"...\",\n \"severity\": \"high|medium\",\n \"estimated_hours\": <number>,\n \"recommendation\": \"...\"\n}}]" }] ) Why debt prioritization reduces time-to-market
Prioritization by the formula score = severity * urgency / effort brings tasks with maximum impact per minimal time to the top. Quick wins (vulnerabilities, HACK comments) get a ×2 boost. This allows removing 12+ critical issues in the first sprint (20 hours) and immediately getting acceleration.
Details of the prioritization formula
Formula: score = (severity * urgency) / effort. Urgency is computed based on time since discovery and criticality for the upcoming release. Quick wins receive a ×2 boost to encourage fast fixes.In our practice: after 4 months, TD index dropped from 8.7 to 3.2, feature delivery time -41%, defect rate -38%. ROI 2:1 in the first quarter.
| Characteristic | Before implementation | After 4 months |
|---|---|---|
| Debt metric (TD index) | 8.7 | 3.2 |
| Typical feature time | 3-4x expected | -41% |
| Bug rate | High | -38% |
| Debt visibility | None | Full dashboard |
| Parameter | Manual audit | AI system |
|---|---|---|
| Analysis time | 1 week per 1000 files | 2 hours |
| Detection accuracy | 60-70% | >90% |
| Prioritization | Subjective | Objective by formula |
| Jira integration | Manual | Automatic |
How we work
- Analytics. Repository audit, metric collection (complexity, dependencies, TODO, test coverage). Determine debt metric and top 10 critical issues.
- Design. Adapt the system to the client's stack (Python/JS/Java, CI/CD, tracking system). Fine-tune LLM for the domain, integrate with MLOps practices.
- Implementation. Deploy scanner in CI/CD, integrate with GitHub/GitLab. Connect dashboard with trends.
- Pilot. Run on one repository, adjust thresholds and priorities. Generate first Jira backlog.
- Deploy. Full rollout. Train the team, hand over documentation.
What's included
- Documentation of scanner and dashboard architecture.
- Access to AI analysis module (Claude Sonnet / GPT-4).
- Integration with tracking systems (Jira, Linear, Asana).
- Team training: how to interpret metrics and prioritize.
- Post-release support for 2 weeks.
Our company has 5 years of experience and over 10 implemented projects in technical debt management.
Estimated timelines
- Basic scanner (complexity + TODO + dependencies): 3–5 days.
- AI analysis of architectural issues: 1 week.
- Prioritization + Jira task generation: 1 week.
- Dashboard with historical trends: 2 weeks.
Pilot projects start at $2,500, full deployment from $10,000.
Common mistakes in debt management
- Trying to pay off all debt at once — demotivates the team. Correct: allocate 20% of sprint to tech debt.
- Ignoring dependency vulnerabilities — can lead to security incidents. Safety and pip-audit solve this in 0.5 h per task.
- Not considering business impact. We add a
business_impactfield to each item — so management sees the link to user-facing issues.
Request a no-obligation consultation — we will scan your repository and show how AI helps reduce the debt metric. This turnkey solution includes everything needed to start within days.