Digital asset management · AI content operations · Los Angeles

I make huge content libraries findable, rights‑safe and fast.

Digital asset management and AI content operations at Walmart Creative Studios. Before that, eight years on film sets. Always building something.

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Alt text pipeline

assets in the DAM I help run: 2M+
alt text success on walmart.com, manual vs. my AI pipeline: 70→99.1%
workdays a year saved by automating alt text: ~200
film & TV credits on IMDb: 55

Selected work

All 8 projects →

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now

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Case study · AI · Accessibility

AI alt text at retail scale

Partnering with Adobe on Brand Aware Metadata to put LLM-written alt text into production across walmart.com.

2025–2026AIMetadata

System · Automation · Rights

Rights that travel with the file

An Airtable intake that captures usage rights at delivery and keeps them attached to the assets, built for 20+ producer teams.

2025–2026AutomationOperationsMetadata

Taxonomy · Metadata

One vocabulary, 25+ teams

Shared metadata taxonomies adopted across 3D, video, photography, graphics, AI-generated and stock-imagery teams on a 2M+ asset DAM.

2025–2026MetadataOperations

Side project · Taxonomy · Web

Pigfolio

A pigment-level catalog of 2,250+ artists' oil paints, normalized by Colour Index code so painters can compare brands honestly.

2026MetadataBuild

Dept	Grip · IATSE 52
Crew	30–40
Truck	5-ton · ~$1.2M kit
Call	06:00 · wrap: late
Credits	55 on IMDb

Operations · Film & TV

Eight years as the second-in-command

Best boy grip, dolly grip and key grip on New York film and TV sets: crews, budgets, and a truck full of equipment.

2013–2021Operations

Headshot: in pre-production

How I work

I do my best work as the second-in-command: I design the workflow, build the tools, keep the details straight and tell the technical truth early, so the person making the call has what they need. On film sets that job is literally called best boy.

More about me →

Direct and precise. I’d rather name the real problem on day one.
Everything reversible. If a bulk change can’t be undone, it isn’t ready.
Rights at the start. Metadata captured at intake is cheaper than metadata hunted down later.
AI with a checker. Models generate, a loop verifies, people handle the exceptions.

Off the clock: oil paint

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Have a library that needs taming?

I’m always happy to talk DAM, metadata and practical AI that holds up in production.

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