Internal Service Advisor Copilot
Python · Claude Messages API · knowledge-grounded
The problem
Dealership service desks run on tribal knowledge. An advisor — especially a new hire — constantly needs answers that live in a senior colleague's head or a folder of PDFs: is this under warranty? what's the standard quote range? what do I do with a recurring fault? when must I escalate? That's slow, inconsistent between staff, and risky when policy is stated wrong (promising warranty coverage that doesn't apply).
The approach
A Claude assistant grounded in the dealership's own materials — a small knowledge base of Markdown documents (warranty policy, pricing ranges, a common-issues playbook, escalation rules, comms tone). For each question it:
- answers only from the knowledge base — never invents policy or prices;
- cites the source file(s) it used, so the answer is auditable;
- escalates when the topic isn't covered, or when the rules require a human (safety, complaints, warranty disputes, comebacks, refunds);
- runs in advisor mode (concise, mid-task) or onboarding mode (step-by-step teaching + a check-your-understanding question).
Architecture
The knowledge base is plain Markdown here so the demo runs with zero setup. In production the grounding step is the only thing that changes — point it at a real CRM/ticketing knowledge base, a Notion/Confluence space, or indexed PDFs. The prompt and guardrails stay identical.
The guardrail (why it's trustworthy)
For a tool staff rely on, a confident wrong answer is worse than an honest "I don't know".
The system prompt's first rule is grounding: answer only from supplied documents; if the
answer isn't there, return in_kb: false, escalate, and say so.
Sample runs
"Bumper-to-bumper is 3 years/100,000 km; powertrain 5 years/160,000 km. Brake pads are NOT covered — they're a listed wear-and-tear exclusion."
"That isn't covered in our current materials — please escalate to a senior advisor." (No invented policy. This is the guardrail working.)
A step-by-step teaching transcript (stop driving · don't open a hot radiator · arrange a tow · escalate) — then: "Check: why arrange a tow instead of telling them to drive in carefully?"
Why it maps to the role
This is the internal-operations and training side of the job: an AI workflow for staff (not customers) that gives fast, consistent, cited answers, refuses to fabricate policy, routes the high-stakes cases to a human, and doubles as a self-serve onboarding trainer.