AI Customer Support Assistant
A concept for grounded support replies: retrieve approved text, cite it, and escalate instead of inventing policy.
Project overview
The project explores AI customer support as retrieval plus a reply, not as a personality. Knowledge is a mock set. No live helpdesk is attached.
Problem being explored
Support teams need fast answers that still match the written policy.
Example user scenario
A visitor asks: “What is the refund window for plan B?” The system should search approved policy, quote the passage, show a confidence hint, and offer a human if the match is weak.
Proposed workflow
- USER QUESTION
- KNOWLEDGE SEARCH
- RAG
- ANSWER
- SOURCES
- CONFIDENCE
- ESCALATION if needed
Architecture explanation
Customer question → knowledge search → RAG → answer → sources → confidence → escalate when necessary. Optional later: CRM or helpdesk ticket with the transcript.
Key features
- Answers constrained to approved knowledge
- Source citations next to the reply
- A confidence signal you can threshold
- Escalation / human handoff path
- Room for helpdesk or CRM integration in production
Demo interface
The Try Demo control is a front-end sketch. Replies are scripted illustrations, not a live RAG call.
FAQ
How can this concept be adapted for a real business?
This page is a DEMO / CONCEPT: it shows interface, architecture, and the support workflow. It is not a client deployment or a live helpdesk. A production system can be adapted to your policies, APIs, authentication, monitoring, and human queues.
Can it connect to our helpdesk or CRM?
A production implementation can create or update tickets through the helpdesk or CRM API, including transcript and sources, once credentials and field mapping are defined. Risky actions can wait for an operator. That connection is not live on this page.
Why show a confidence signal?
The score is a threshold you tune so a weak match does not look like a firm policy answer. Low similarity can offer a human handoff instead of a guessed reply. It is a product rule, not a legal probability.
Can the assistant open a ticket?
Opening a ticket can be implemented as an explicit tool with authentication and logging. The demo illustrates the idea and does not create tickets. In a working system the operator still sees the full context.
Limitations
No live ticketing system. Knowledge is a mock set. Not a client deployment.
What production would require
Approved documents, evaluation questions, access control, and a human queue.