AI Ops Lead Workflow Demo

A working prototype for workflow discovery, support triage, human review, QA guardrails, and operating metrics.

Tickets0
Review Queue0
Auto Candidates0
Avg Confidence0%
Time Saved0h

Ticket Intake

Pick a scenario or paste a real operational request.

Triage Output

Structured result with owner, risk, draft response, and QA checklist.

Ready

Review Queue

Human-in-the-loop controls for customer-facing, data-sensitive, or low-confidence outputs.

Workflow Registry

Every AI workflow needs owner, status, guardrail, metric, and target before rollout.

WorkflowTeamStatusGuardrailMetricTarget

Build Architecture

The interview point is not the toy UI. It is the operating pattern you can ship across teams.

1

Intake

Webhook, support ticket, CRM note, call transcript, or form submission.

2

Classify

LLM or rules identify category, severity, owner, confidence, and missing context.

3

Guardrail

Human review for customer-facing, data-sensitive, or low-confidence work.

4

Act

Draft response, route owner, create task, update CRM, or send to Product digest.

5

Measure

Track adoption, QA pass rate, override rate, time saved, and failure patterns.

Production Version

How this maps to the tools in the job description.

LayerLikely ToolRole In System
Workflow orchestrationn8n / Make / ZapierWebhook intake, branching, API calls, retries, scheduled digests.
Review queueRetoolApprove, reject, edit, and escalate AI outputs before system writes.
Knowledge and playbooksNotionWorkflow registry, prompt library, SOPs, owner docs, office-hours notes.
Data layerSQL / BigQuery / SnowflakeWorkflow runs, QA reviews, adoption, time saved, and audit history.
AI layerChatGPT / Claude / APIClassification, summarization, draft responses, feedback clustering, RAG answers.
DeliveryCRM / Support / Slack / EmailPut output where teams already work instead of forcing copy-paste.

Opening

"I built a small AI Ops prototype around support triage because it shows the real job: messy intake, routing, human review, QA, and measurable leverage."

Show The Live Demo

  • Pick the Yardi sync failure scenario.
  • Run triage.
  • Point out category, severity, owner, confidence, review gate, draft response, and QA checklist.
  • Approve or escalate the queue card.

Explain The Judgment

"This does not auto-send customer-facing responses. Anything involving PMS integrations, market data quality, strategic accounts, or low confidence goes through review."

Close

"The repeatable pattern is workflow map, output contract, guardrails, small prototype, usage metrics, QA, then team enablement. That is how I would scale AI adoption safely."