Intake
Webhook, support ticket, CRM note, call transcript, or form submission.
Pick a scenario or paste a real operational request.
Structured result with owner, risk, draft response, and QA checklist.
Human-in-the-loop controls for customer-facing, data-sensitive, or low-confidence outputs.
Every AI workflow needs owner, status, guardrail, metric, and target before rollout.
| Workflow | Team | Status | Guardrail | Metric | Target |
|---|
The interview point is not the toy UI. It is the operating pattern you can ship across teams.
Webhook, support ticket, CRM note, call transcript, or form submission.
LLM or rules identify category, severity, owner, confidence, and missing context.
Human review for customer-facing, data-sensitive, or low-confidence work.
Draft response, route owner, create task, update CRM, or send to Product digest.
Track adoption, QA pass rate, override rate, time saved, and failure patterns.
How this maps to the tools in the job description.
| Layer | Likely Tool | Role In System |
|---|---|---|
| Workflow orchestration | n8n / Make / Zapier | Webhook intake, branching, API calls, retries, scheduled digests. |
| Review queue | Retool | Approve, reject, edit, and escalate AI outputs before system writes. |
| Knowledge and playbooks | Notion | Workflow registry, prompt library, SOPs, owner docs, office-hours notes. |
| Data layer | SQL / BigQuery / Snowflake | Workflow runs, QA reviews, adoption, time saved, and audit history. |
| AI layer | ChatGPT / Claude / API | Classification, summarization, draft responses, feedback clustering, RAG answers. |
| Delivery | CRM / Support / Slack / Email | Put output where teams already work instead of forcing copy-paste. |
"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."
"This does not auto-send customer-facing responses. Anything involving PMS integrations, market data quality, strategic accounts, or low confidence goes through review."
"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."