AI System for Managing Technical Debt in Development

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.

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.

How we work

1. Analytics. Repository audit, metric collection (complexity, dependencies, TODO, test coverage). Determine debt metric and top 10 critical issues.
2. 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.
3. Implementation. Deploy scanner in CI/CD, integrate with GitHub/GitLab. Connect dashboard with trends.
4. Pilot. Run on one repository, adjust thresholds and priorities. Generate first Jira backlog.
5. 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_impact field 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.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.