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Chelsie Hodgkiss, AI Operator
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Chelsie Hodgkiss

Operations Systems Builder · AI & Automation

I take messy operations that depend on spreadsheets, tribal knowledge, manual handoffs and people remembering how everything works, and turn them into systems that are easier to run. I usually start before the build: figuring out what the process actually is, where the source of truth lives, which assumptions need to become rules, and where human judgment needs to stay. Then I use AI and automation to close the gap between the operation we need and the system that runs it. I’m an operator, not a traditional developer. That’s useful when the hard part isn’t writing code — it’s figuring out what the code should actually be allowed to do.

Availability
Limited availability
Location
Utah, United States
Languages
English
Timezone
America/Denver
BuildFix or finishAutomation
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What Chelsie has shipped.

Proof of Work leads this profile: the context, the contribution and the real work behind each project.

GTM Operations & Planning System

Build· 2026

GTM Operations & Planning System

Turned a GTM operation that relied on spreadsheets and tribal knowledge into a working system with explicit rules, planning workflows, monitoring, and human approval gates.

The context
Account groups, capacity, ownership, opportunity creation, QC, and planning lived across spreadsheets, CRM data, and people's heads. Different definitions were being used for the same concepts, lists could change after work started, and answering basic questions often meant finding the person who knew how the process worked.
What was shipped
I turned that operating logic into an internal application with separate workflows for running the operation, planning capacity, building account groups, investigating problems, and administration. It uses frozen rosters, explicit source-of-truth rules, staleness and mismatch detection, logged changes, and human approval before anything writes back to the system of record.
Operator contribution
I owned the system end to end: identifying the operational problem, defining the business rules and data ownership, deciding where automation was safe and where human judgment needed to stay, designing the workflows, directing the AI-assisted build, testing the output against real operating cases, and continuing to refine the system when production use exposed assumptions we hadn't made explicit. What changed: questions that used to mean finding the person who knew now mean opening a screen, and when upstream data breaks we find out from a flag instead of a bad decision three weeks later. The real test came when a person central to the old way of working was out for an extended stretch — and the operation kept running. Under the old process, that absence was the outage.
Claude CodeCodexMakeAPI integrations

Skills and tools

Claude CodeCodexMakeAPI integrations

Managed skills support OpsSeek filters. Other tools describe the Operator’s workflow.

Working fit

Capacity
8 hours / week
Pricing is shared only when the Operator chooses to publish it.

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