Turning Watch expertise into a reusable diagnostic system.
A multi-document knowledge architecture for complicated activation and lifecycle decisions.
What needed to work.
Wearable activation spans order entry, provisioning, customer records, billing, and device lifecycle events. Scattered documentation and informal SME help make it easy to apply the wrong flow or interpret an expected delay as a failure.
The ownership I brought.
- Designed and shipped the multi-document RAG diagnostic system and its master knowledge structure.
- Structured scenario-specific guidance for wearable activation and lifecycle troubleshooting.
- Organized canonical flow definitions and signal timing so diagnosis could be anchored in the correct lifecycle context.
- Connected the knowledge work to hands-on readiness and validation across CRM and billing systems.
Four focused domains.
One connected diagnostic model.
The structure follows the questions people need to answer. The master RAG connects the domains while preserving scenario-specific context.
Customer-owned devices · Cox-sold devices · Exchange · Relinking · Related activation paths
Decisions that shaped the work.
Separate knowledge by the decision it supports.
Terminology resolves what a term means. Canonical flows establish expected behavior. Signal timing explains what can legitimately arrive later. Troubleshooting guides the next diagnostic step. A master layer keeps those domains connected.
Preserve scenario differences.
Customer-owned devices, retail orders, exchanges, and relinking do not automatically share the same expected sequence. The knowledge structure preserves the relevant path instead of flattening everything into a generic activation answer.
Make uncertainty useful.
Signal and lag interpretation matters because a missing observation is not automatically a defect. The system structures what to inspect and how to reason about the state before escalating.
What changed.
Delivered reusable decision support for activation and lifecycle troubleshooting, bringing scenario knowledge and diagnostic guidance into a connected structure. Earlier career materials describe more consistent triage and less dependence on ad-hoc SME support.
My AI work starts with domain truth, knowledge structure, and a useful operational decision.
Source & outcome context
Career résumé and supporting work records describe the knowledge structure and qualitative operational outcome. The illustration shows the organizing approach; it does not expose source documents, operational rules, prompts, or system access.