AI-Powered Code Migration Between Languages

AI-Powered Code Migration Between Languages

We often receive legacy projects where Python backend needs to be migrated to TypeScript, Java to Kotlin. Rewriting manually is expensive and time-consuming: a 15,000-line project takes 3–4 months, with budgets running into tens of thousands of dollars. Transpilers like Babel or j2objc produce unreadable code that ignores idioms: snake_case remains snake_case, and Pydantic models turn into bulky classes. We built an LLM-based system that doesn't just translate syntax but adapts architecture: it replaces libraries with idiomatic equivalents, converts types, and validates the result. In this article, we share the architecture and a real case of migrating a notification service from Python FastAPI to TypeScript. With over 30 successful migrations and 5 years in the field, we guarantee results.

Why AI Migration Is More Efficient Than Manual Refactoring?

Manual code migration is a task that takes weeks and months. AI migration cuts time by 5–7x for projects up to 15,000 lines. The resulting code is idiomatic: snake_case becomes camelCase, Pydantic models become Zod schemas, and async calls become native Promises. Our experience shows that 70–85% of lines are migrated without edits; the remaining 15% is complex business logic that we refine manually. Budget savings reach 50–70% compared to manual refactoring. For a typical 15K-line project, manual migration costs $30k-$60k, while AI migration costs $10k-$20k.

How Is the AI Migration Architecture Designed?

The naive approach—feeding the LLM the entire file and asking for a translation—works only for files up to 200–300 lines. For real codebases, we use a four-component architecture:

• Dependency Analyzer — builds a dependency graph between modules and determines the migration order. Uses AST to analyze imports.
• Chunk Splitter — splits files into independent chunks (classes, functions, modules) that can be migrated and tested in isolation.
• Context Manager — passes already migrated dependencies to the LLM so that new files use correct imports.
• Validator — compiles and tests the migrated code; on errors, triggers auto-fix.

A key element is the Glossary: a dictionary mapping source language libraries to target language equivalents. For example, Pydantic → Zod, SQLAlchemy → Prisma, FastAPI → Express+Hono.

# source: Python, target: TypeScript
pydantic.BaseModel: zod.ZodObject
sqlalchemy.orm.Session: prisma.PrismaClient
fastapi.FastAPI: express.Application

How We Migrate Your Project: Stages

1. Analysis and glossary preparation — we study the source codebase, build a dependency graph, configure library mappings. Takes 1–2 days.
2. Migration of isolated modules — first we migrate models and utilities, then services and routes. Each file is compiled and tested separately.
3. Integration and auto-fix — we merge migrated modules, run a full build. On compilation errors, the system automatically fixes them via LLM.
4. Testing — we run unit tests (Jest/Vitest) and integration tests. If coverage drops more than 10%, we manually refine tests.
5. Delivery — we provide the code, glossary documentation, coverage report, and instructions for re-migration.

Practical Case: Python Microservice → TypeScript

Context: A startup migrated a notification service (Python FastAPI, 3200 lines) to TypeScript to unify the stack (the frontend team knew only JS/TS).

Scope: 28 files, 12 Pydantic models, 34 API endpoints, 180 unit tests.

Process (2 weeks):

• Week 1: glossary setup, migration of models and utilities (automatic), manual refinement of 3 complex files with business logic.
• Week 2: migration of routes, test adaptation (Jest), integration testing.

Results:

• 85% of code migrated automatically without manual edits.
• 15% required refinement (complex logic with Python-specific idioms).
• TypeScript compilation errors: 47 → 0 (after 2 LLM fix iterations).
• Test coverage of the migrated service: 71% (was 74% in Python — minimal loss).
• Unexpected bonus: during migration, AI identified 3 spots with potential race conditions in Python code, which were fixed in the TypeScript version.

We expected automation only for simple files, but the system handled 85% of the code—this saved us 3 weeks of manual work. — noted the startup engineer.

What Is Included in the Migration Result?

We deliver a complete package:

• Migrated code in the target language (entire codebase).
• Glossary documentation (library mappings).
• Documentation for the migrated code.
• Configured compilation and testing pipeline.
• Test coverage report.
• Access to the migration tool.
• Training session for your developers.
• One month of post-migration support.
• Instructions for re-migration upon updates.

Each migrated file is compiled with the strict flag. On errors, auto-fix via LLM is triggered. After all files are migrated, unit tests (Jest) and integration tests are run. If coverage drops more than 10%, we manually refine tests. The entire process is repeated until full success.

How Long Does Migration Take?

Contact us for a consultation on codebase migration. Get an analysis of your project in 1–2 days and learn the exact time and budget savings. Request a consultation — we will analyze your project and propose the optimal approach.

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.