AI Service Operations for a Multi-Location SME

01 · Problem

The operating constraint

A growing services business manages inbound requests across email, messaging, and CRM queues. Manual classification and knowledge lookup delay response, while managers lack consistent visibility into exceptions and service quality.

02 · Solution

The control-plane design

A lightweight AI service layer classifies each request, retrieves approved operating knowledge, prepares a cited draft, and routes low-confidence or sensitive cases to a named owner. The CRM remains the system of record.

03 · Architecture

Signal-to-action flow

1. Ingest requests from approved channels
2. Remove unsupported or sensitive fields
3. Classify intent and retrieve approved knowledge
4. Draft a cited response with confidence
5. Route exceptions to a human and record outcomes

Mermaid.js source

graph LR
  A[Email + CRM] --> B[Request Classifier]
  B --> C[Approved Knowledge]
  C --> D[Response Draft]
  D --> E{Confidence + Policy}
  E --> F[Human Review]
  E --> G[Approved Reply]
  F --> H[Outcome Log]
  G --> H

04 · Impact

Measure the decision, not the demo.

Baseline operational cost, decision latency, reliability, override behavior, business lift, and risk events before deployment. Report exceptions and uncertainty alongside headline outcomes.