Selected work
Telecom document RAG
Retrieval-augmented answers over thousands of service and topology documents, served by local models.
Problem
Operational knowledge — service records, topology, tickets — is scattered across systems and exports. Engineers waste time hunting for context that already exists somewhere.
Approach
Documents are ingested and chunked into a vector store with a local embedding model. A GPU-served LLM answers questions with citations back to the source material, and a curated knowledge layer keeps the corpus organised. The LLM is never placed on the critical path of ingestion, so retrieval stays available even when generation is down.
Stack
Outcomes
- Natural-language answers grounded in the actual document corpus
- Cited sources, so every answer is verifiable rather than hallucinated
- Search across a large internal corpus without manual indexing
- Fully self-hosted — no internal data leaves the network