This repository is a sanitized portfolio artifact showing how I structure support operations work from intake through resolution, escalation, documentation, and QA follow-through.
It is designed for technical operations, workflow automation, and applied AI roles. The goal is not to simulate a company. The goal is to show operating judgment, documentation discipline, and a practical approach to lightweight AI assistance.
- I can turn messy support issues into repeatable workflows and decision criteria.
- I can connect case handling, KB content, escalation rules, and QA coverage into one system.
- I can design a narrow AI-assistance layer with explicit guardrails and evaluation instead of vague automation claims.
cases/case-01-mfa-loop.mdoperations/triage-decision-guide.mdoperations/escalation-playbook.mdapplied-ai/README.mdartifact-map.md
cases/: sanitized support cases showing intake, triage, resolution, and related artifactsknowledge-base/: agent-facing guidance derived from recurring issuesoperations/: triage and escalation guidance for consistent handlingqa/: regression coverage and bug reports tied to support-critical workflowsapplied-ai/: a small triage-assistant spec, evaluation set, and scoring scriptevidence/: proof artifacts for tool exposure and earned credentialstemplates/: reusable formats behind the published artifacts
The AI layer is intentionally narrow. It covers one support task: deciding when an assistant should answer from known guidance, ask a clarifying question, or escalate to a human. It is grounded in the case pack, KB articles, and escalation rules already in the repo.
- All cases are sanitized and illustrative.
- No employer, customer, or production claims are implied.
- No metrics or business outcomes are invented.
- Platform exposure is claimed only where a proof artifact exists in
evidence/.