Nexus RAG Assistant
Grounded answers every response cites the internal document it came from.
- Client
- Confidential
- Year
- 2025
- Category
- AI
- Role
- Design + Build
- Timeline
- 4–6 weeks
The problem
Staff answered the same product, pricing and policy questions from memory and scattered PDFs. Answers drifted, and a wrong one about tax or credit terms is expensive.
What we built
A RAG pipeline over the client’s own documents: FAISS vector retrieval, cosine-similarity ranked chunks, and an LLM that only answers from what it retrieved, with the source file and chunk shown next to every response. Runs on-premise on an NVIDIA A100 so nothing leaves the building, with an operator view for architecture, security and latency.
The result
One assistant, one source of truth. Answers are consistent, traceable to a document, and fast enough to use mid-conversation.
- source file + chunk shown for every answer
- citedsource file + chunk shown for every answer
- runs locally on an NVIDIA A100, so no data leaves
- on-premruns locally on an NVIDIA A100, so no data leaves
- typical retrieval + generation latency
- ~350 mstypical retrieval + generation latency
Built with
- Python
- LangChain
- FAISS
- LLM
- FastAPI
- NVIDIA A100
We deliver what we commit.
Tell us what you're trying to build.
We'll come back within 24 hours with honest feedback on scope, timeline and cost, whether or not we turn out to be the right fit.