Amgen Case Study: AI Code Intelligence System with RAG for Developer Productivity
Adople AI partnered with Amgen, a leading U.S.-based biotechnology company, to build a code intelligence system that enables developers to search and understand large codebases using natural language.
The platform uses Retrieval-Augmented Generation (RAG), vector search, and multi-agent pipelines to convert complex code repositories into structured, queryable knowledge for engineering teams.
Challenge
Large enterprise codebases are difficult to navigate due to scale, complexity, and lack of structured knowledge layers.
Amgen engineers worked across multiple repositories, languages, and legacy systems, making it time-consuming to locate relevant code and understand existing implementations.
The challenge was to design a system that could transform unstructured code into searchable data and enable natural language interaction for faster development workflows.
The problem is not accessing code, but understanding it. AI systems that structure codebases into searchable knowledge layers enable faster discovery, reuse, and development.
- Adople AI
Solution
Adople AI designed a code intelligence pipeline that ingests repositories, structures code into embeddings, and enables semantic retrieval through RAG architecture.
The system converts code files into vector representations and stores them in a scalable database, allowing developers to query repositories using natural language.
A multi-agent retrieval layer identifies relevant code, generates explanations, and provides contextual insights to improve developer productivity.
Results
- Faster discovery of relevant code across large repositories
- Improved developer productivity through AI-assisted search
- Semantic code retrieval using vector-based search
- AI-generated explanations for complex code logic
- Scalable system for large enterprise codebases
Technology Stack
- LLM-based code understanding and reasoning
- RAG architecture for code retrieval
- Embedding models for semantic search
- Vector database for scalable indexing
- Multi-agent pipeline for query orchestration
Business Impact
Business Impact
The platform enables developers to interact with large codebases more efficiently using natural language queries.
By structuring code into searchable knowledge, the system reduces development time, improves collaboration, and accelerates engineering workflows across teams.
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