Fatbunny Digital
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Applied AI use case / customer-support triage

Route each support request with the context needed to act.

Intent, urgency, approved answers and structured ticket creation designed as one service workflow.

The assistant identifies the service intent, gathers useful context, answers inside the approved boundary or creates a structured case for the correct team.

Delivery
service
Scope
Human-owned
Control
Measured outcome

Business workflow

Correctly routed case
  1. 01Support request
  2. 02Intent and urgency
  3. 03Approved knowledge
  4. 04Resolution boundary
  5. 05Service owner
  6. 06Help-desk case
  7. 07Useful service response

The operating problem

Support queues become expensive when incomplete requests reach the wrong team and customers must repeat information before anyone can act.

System mechanism

Customer-support triage workflow

A real operating path showing the information, decisions, systems and accountable people required to produce a useful result.

Customer-support triage: Support request to Intent and urgency to Approved knowledge to Resolution boundary to Service owner to Help-desk case to Useful service response.

  1. 01
    Business inputSupport request

    The question and channel enter with the minimum available context.

  2. 02
    Controlled workflowIntent and urgency

    The workflow identifies the service path and priority signals.

  3. 03
    Approved contextApproved knowledge

    Relevant policies and service guidance ground any direct answer.

  4. 04
    Decision pointResolution boundary

    The system decides whether it can answer or must create a case.

  5. 05
    Human gateService owner

    Exceptions transfer with the question, evidence and attempted steps.

  6. 06
    Business systemHelp-desk case

    The request is structured, prioritised and routed to the correct queue.

  7. 07
    Business valueUseful service response

    The customer receives an answer or accountable handoff without restarting.

Fatbunny responsibility

Fatbunny maps the current workflow, information authority, decision owners, integration boundary, review states and measurable release criteria before implementing the automation.

Capabilities

  1. 01

    Current-workflow and exception mapping

  2. 02

    Retrieval and source-authority design

  3. 03

    Bounded tools and system integration

  4. 04

    Human review and outcome measurement

Delivery decisions

  1. 01

    Define the business result before selecting a model

  2. 02

    Keep deterministic rules outside probabilistic interpretation

  3. 03

    Return missing evidence and consequential action to an owner

  4. 04

    Measure the completed workflow rather than model activity

Expected outputs

  1. 01

    An agreed workflow and operating boundary

  2. 02

    A working scenario connected to approved information and systems

  3. 03

    Evaluation evidence, ownership controls and a measured next backlog

Honest limits

  1. !

    Automation cannot repair missing source information or unclear decision ownership.

  2. !

    Sensitive data and consequential actions require explicit permissions and review.

  3. !

    A convincing demonstration is not treated as production evidence.

Start with the real work

Support queues become expensive when incomplete requests reach the wrong team and customers must repeat information before anyone can act.

The first release should remove a specific coordination constraint without hiding missing information or transferring accountability to a model.

Connect information, decisions and systems

The workflow may involve Support channels, Knowledge base, Help desk, Customer record. Each connection receives a narrow purpose, known failure behavior and a responsible owner.

Measure the business result

Useful evidence includes correctly routed case, faster first response, more useful escalation. Model confidence and conversation volume are not substitutes for those outcomes.

Direct answers

Common questions

What does customer-support triage automate?

The assistant identifies the service intent, gathers useful context, answers inside the approved boundary or creates a structured case for the correct team.

Does the AI make every decision?

No. Known rules remain deterministic, and uncertain or consequential work returns to the accountable person shown in the workflow.

Can this connect to existing business systems?

Yes, when those systems provide suitable APIs, exports, CLI operations or MCP tools with appropriately scoped access.