Research & development
Collaborative document retrieval
A document retrieval project spanning processing, vector search, reranking, APIs, and evaluation.
The problem
Useful answers depend on more than storing document chunks. The system had to preserve document structure during processing, find relevant passages, and rank them well enough for downstream use.
My role
I contributed across the project. The contribution was collaborative and does not imply sole implementation of every component.
Approach
The system brought together document processing, vector search, reranking, API access, and evaluation.
Inside the work
Retrieval quality starts before a question reaches a language model. Documents must first be processed into a usable structure; candidate passages then need to be found, ranked, and exposed through an API. This project combined hierarchical document processing, vector search, cross-encoder reranking, and evaluation. I worked across these areas with the team. The case study focuses on the shape of the pipeline and my cross-cutting contribution, without treating the presence of a component in the repository as proof that I built it alone.
What is shared here
No source code, data, product name, organization, or measured outcome is shared.