Overview
This anonymized project explores internal document retrieval and question answering with local models and vector search.
The problem area
Useful organizational knowledge is often distributed across documents that are difficult to search consistently. Retrieval-augmented generation offers one way to connect natural-language questions to relevant source material while keeping retrieval visible as part of the answer process.
This entry intentionally avoids claims about deployment, performance, datasets, or business outcomes until those details can be shared accurately.