AMGEN Case Study — AI-Powered Code Intelligence Platform with RAG
Adople AI partnered with Amgen, one of the world’s leading biotechnology companies, to build an AI-powered code intelligence platform that enables developers to search and understand complex codebases using natural language.
The platform uses Retrieval-Augmented Generation (RAG), vector databases, and multi-agent AI architecture to analyze massive software repositories and generate insights from code. This solution allows developers to quickly locate relevant code snippets, understand legacy systems, and accelerate software development workflows.
Challenge
Amgen maintains extensive software repositories containing thousands of files, multiple programming languages, and complex dependencies across its engineering teams.
Developers frequently struggled to locate relevant code, understand legacy implementations, and retrieve useful snippets quickly. Manual navigation through large repositories slowed development workflows and reduced productivity.
The organization needed a system that could allow engineers to search and analyze code using natural language queries while providing meaningful insights from large codebases.
AI-powered code intelligence platforms transform how developers interact with large codebases. By combining RAG architecture, vector search, and multi-agent reasoning, engineers can explore complex repositories using natural language and retrieve relevant code instantly.
-Adople AI
Solution
Adople AI designed and implemented a comprehensive AI code intelligence platform powered by RAG architecture and multi-agent AI workflows.
The platform ingests code repositories automatically, splits code files into optimized chunks, converts them into vector embeddings, and stores them in a scalable vector database for semantic search.
A multi-agent retrieval pipeline identifies relevant code snippets and generates insights using advanced language models, allowing developers to explore complex repositories quickly and efficiently.
Results
- Faster discovery of relevant code across large repositories
- Improved developer productivity through AI-assisted code understanding
- Semantic code search using vector similarity rather than keywords
- AI-generated explanations and optimized code snippets
- Scalable architecture capable of handling millions of code vectors
Technology Stack
- LLM & Reasoning: DeepSeek Coder, GPT-based models
- RAG Orchestration: LangChain
- Embeddings: Jina AI code embedding models
- Vector Database: Qdrant
- Architecture: Multi-agent AI pipeline
- Backend: Python-based AI processing system
Business Impact
The AI code intelligence platform enables Amgen engineers to interact with complex code repositories using natural language queries.
By automating code discovery and analysis, the system reduces the time developers spend navigating large codebases while improving collaboration across engineering teams and accelerating software innovation.
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