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TokVera
TokVera

Posted on • Originally published at tokvera.org

How to Build a Customer Support AI Router with Trace Visibility

#ai #typescript #opensource #api

Most AI support demos stop at a single prompt. A user asks a question, the model returns a reply, and the tutorial ends there.

That is not how real support systems behave.

A real support workflow has to decide what kind of issue it is, where it should go, whether it needs escalation, what policy context applies, and how the final response should be written. Once you add those steps, you also need a way to inspect why the workflow made each decision.

That is the problem behind ai-support-router-starter, a small open-source Node.js example from Tokvera that shows how to build a realistic customer-support AI workflow with trace visibility.

Why support AI needs routing, not just prompting

A support assistant is not only a writing tool.

It is also a routing system.

If a customer reports unexpected charges, the system should recognize that this is likely a billing issue, route it to the right internal queue, decide whether escalation is needed, and apply the right response guidance before drafting the final reply.

In practice, that usually means you need at least these layers:

  • classification
  • routing
  • policy or knowledge lookup
  • reply drafting
  • operational metadata like SLA, ownership, and escalation

Without that structure, you end up with a nice demo and a fragile system.

What the starter repo does

The starter focuses on a practical workflow shape instead of pretending a single prompt solves support automation.

It:

  • classifies inbound tickets into categories like billing, bug, feature, or general support
  • chooses a queue, owner, priority, and escalation recommendation
  • looks up policy guidance before drafting the reply
  • returns internal next actions along with the customer-facing answer
  • emits Tokvera trace data so the end-to-end request path is inspectable

The goal is not to be a complete helpdesk product.

The goal is to give teams a realistic foundation they can fork and extend.

The API shape

The project exposes a small set of routes for health checks, sample payloads, and workflow execution:

  • GET /health
  • GET /api/demo-ticket
  • GET /api/sample-tickets
  • POST /api/tickets/reply

Example request:

curl -X POST http://localhost:3000/api/tickets/reply \
  -H "Content-Type: application/json" \
  -d '{
    "subject": "Need help understanding extra usage charges",
    "message": "Our finance team saw a larger invoice this week. Can you explain what changed?",
    "plan": "pro",
    "customerName": "Riya",
    "customerEmail": "riya@example.com"
  }'
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And the response is more useful than a plain model completion because it contains workflow output, not just generated text:

{
  "traceId": "trc_123",
  "runId": "run_123",
  "triage": {
    "category": "billing",
    "priority": "medium",
    "queue": "billing-ops",
    "suggestedOwner": "billing",
    "suggestedSlaHours": 8,
    "shouldEscalate": false,
    "tone": "reassuring"
  },
  "nextActions": [
    "Assign to billing",
    "Respond within 8 hours",
    "Review included usage, overages, and invoice change history"
  ],
  "reply": "..."
}
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Workflow architecture

The starter keeps the flow intentionally simple and inspectable:

Inbound ticket
  -> classify_ticket
  -> lookup_policy
  -> draft_reply
  -> return triage + reply + next actions
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That separation matters.

If classification, policy lookup, and drafting all live inside one opaque prompt, you only see the final answer and are left guessing where the system went wrong.

When the workflow is split into distinct steps, debugging becomes much easier.

Why trace visibility matters

Support automation can fail in several different ways:

  • a billing ticket gets routed to engineering
  • an enterprise account does not escalate quickly enough
  • the correct queue is chosen but the wrong policy guidance is used
  • the workflow classifies correctly but drafts the wrong tone or final answer

If all you can see is the final response, root cause analysis becomes slow and fuzzy.

With trace visibility, you can inspect the workflow path that produced the result and see which step actually broke down.

That is where Tokvera fits into the starter.

Instead of only tracking raw model usage, Tokvera helps you inspect the root workflow trace and the individual decisions made along the way.

Running the project locally

One nice detail in this repo is that it defaults to mock mode.

That makes it useful for local evaluation, demos, screenshots, and onboarding even before you wire in live provider traffic.

npm install
cp .env.example .env
npm run dev
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When you want to switch to live traffic, set MOCK_MODE=false and provide:

  • OPENAI_API_KEY
  • TOKVERA_API_KEY

That gives teams a clean path from local development to real tracing.

What to customize next

If you want to take this beyond a starter, the next obvious extensions are:

  • replace the policy lookup stub with a real knowledge base or help center integration
  • add Slack or email escalation hooks for urgent tickets
  • persist ticket state and triage output to a database
  • add provider fallback for reply drafting
  • build a lightweight support review UI on top of the API

Why this repo is useful

A lot of AI tutorials show how to get text back from a model.

Fewer show how to build a workflow that another team can actually operate.

That is what makes ai-support-router-starter valuable.

It treats customer support AI as a decision pipeline rather than a one-shot prompt, and it gives you a traced, extensible foundation for building something real.

If you want to try it, start with the repo:

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