AI-Powered Automatic Source Code Documentation Using Private LLMs
- AI Development
- LLM Integration
- Java
- JavaScript
- Ollama
- GPU Infrastructure
Overview of Our Client
Our client had an enterprise software system that was predominantly developed using Java and JavaScript languages. With time, the number of source code files grew to several thousand pieces without proper technical documentation.
The lack of standardized documentation complicated the onboarding process, technical maintenance, compliance requests, and long-term support. Since the codebase contained proprietary business logic, the client was unable to utilize external cloud-based AI services and required a fully private solution deployed within a controlled infrastructure.
- Region: Europe
- Industry: Enterprise Software Development
- Timeline: ~2 months
Challenge
Maintaining proper technical documentation for complex software systems typically involves considerable cost and effort when done manually. In this respect, we identified the following challenges to overcome:
- A large number of undocumented source code files (~3,100)
- Significant engineering effort required for manual documentation
- Need for precise understanding of Java and JavaScript business logic
- Strict confidentiality requirements for proprietary source code
- Necessity to reduce AI infrastructure and inference costs
- Need for uniform documentation structure in all repositories
Main Goals
To automate documentation workflows and preserve security and cost efficiency, we defined the following goals:
- Generate technical documentation automatically for Java and JavaScript codebases
- Keep all AI processing fully private and locally hosted
- Minimize infrastructure and inference costs
- Support scalable batch processing for thousands of files
- Improve maintainability and developer onboarding
- Standardize documentation quality and formatting, according to the best practices (Javadoc, JSDoc)
Project Overview
We developed an AI-powered documentation generation platform capable of processing large closed-source repositories and generating structured technical documentation automatically.
The system analyzed Java (SpringBoot) and JavaScript (React) source files, extracted semantic and architectural information, and generated developer-oriented documentation using coding-specialized LLM models.
To ensure full confidentiality, the entire solution operated on private GPU infrastructure using Ollama for local model serving. The processing pipeline was optimized for high-volume inference while keeping operational costs under control.
Solution
The delivered solution introduced a secure and scalable workflow for automatic source code documentation generation using private LLMs.
The platform processed repositories in batches, analyzed classes, methods, dependencies, and business logic, and generated readable documentation suitable for internal knowledge bases and engineering teams.
Core Platform Capabilities
- Automatic documentation generation for Java and JavaScript projects
- Local/private LLM deployment without third-party API dependency
- Batch processing for thousands of source code files
- Structured and consistent technical documentation output
- GPU-accelerated inference optimized for cost efficiency
- Secure processing of proprietary enterprise codebases
Technology Stack
To support secure and scalable documentation generation for private repositories, we used the following AI-focused infrastructure:
Infrastructure
- NVIDIA H100 GPU powered server environment
LLM Runtime
- Ollama (private/local model serving)
AI Models
- Qwen2.5 Coder (source code analysis and documentation generation)
Processing Pipeline
- Automated source code parsing and batch documentation workflows
Related Cases
Core Team
- Solution Architect: Designed private AI infrastructure and documentation workflows
- AI Engineers: Integrated coding-focused LLMs and optimized inference pipelines
- Backend Engineers: Implemented parsing and batch processing services
- DevOps Engineers: Configured GPU infrastructure and local model deployment
- QA Engineers: Validated documentation quality and processing consistency
Results
The AI-powered documentation platform significantly reduced manual effort and improved maintainability within the client’s engineering ecosystem. Specifically, we achieved:
- Automated documentation generation for over 3,100 source code files
- Less engineering time spent on manual technical documentation
- Fully private AI workflow without exposing proprietary code externally
- Cost-efficient inference using local GPU infrastructure
- Improved onboarding and long-term maintainability of enterprise systems