RAG Customer Chatbot
A support assistant that answers from your documents, with citations
- Type
- Retrieval-augmented assistant
- Deployment
- Tailored per business
- Grounding
- Your own documents
- Industry
- Cross-industry
The challenge
Support teams answer the same questions repeatedly from information that already exists in manuals, policies and product docs. A generic chatbot bolted onto that problem invents answers — which is worse than no chatbot, because a confident wrong answer about a refund policy or a dosage costs more than a slow reply.
Our approach
We build the assistant around retrieval rather than raw generation. Your documents are chunked and embedded, the model answers only from what it retrieves, and every response carries a citation back to the source passage. When retrieval confidence is low, the bot escalates to a human instead of guessing.
What it does
Grounded in your content
Answers come from your ingested manuals, policies, product docs and FAQs — not from the model's general knowledge.
Citations on every answer
Each response links back to the source passage so staff and customers can verify what they were told.
Confidence-based handoff
Below a retrieval-confidence threshold the assistant routes to a human rather than producing a plausible guess.
Evaluation harness
A regression suite of real questions runs before any prompt or model change, so accuracy is measured rather than assumed.
Tailored per business
Tone, escalation rules, restricted topics and ingestion sources are configured to the specific operation.
What it changes
- Repetitive questions handled without a human in the loop
- Answers traceable to a source, so mistakes are diagnosable
- Accuracy tracked against a fixed evaluation set over time
Built with
- Next.js
- Python
- OpenAI API
- Vector database
- PostgreSQL
Want something like this built for you?
Tell us what you're running today and we'll scope it — starting with a fixed-price discovery.