Selected work - AI systems that earned their keep.

A few of the things I've shipped, with the engineering decisions that mattered. Some engagements are public. Others I can describe in a call. Where a study reads short, the work was real and the details are under NDA. See the engagement shapes if you’re looking for similar work, or read how an engagement runs.

Case studies

ClearProp (own product)

Next.js, mobile, Postgres, solo full-stack

Client
ClearProp (own product)
Sector
General aviation / consumer
Service
Next.js, mobile, Postgres, solo full-stack
Headline metric
Internal proving ground for ESARC patterns; daily-driver logbook for the author

A pilot logbook I actually want to use

Operational outcome

ClearProp is what I build when I build for myself. It is a digital pilot logbook for general aviation pilots, designed around the boring parts of the job: getting your hours in correctly, tracking currency, and producing the export your medical examiner or chief flight instructor actually wants.

It's a side project today and a product I plan to commercialize. It also doubles as ESARC's internal proving ground for new patterns before they show up on client work.

Amazon

Java, Python, AWS (Lambda, DynamoDB, ECS), in-house ML

Client
Amazon
Sector
E-commerce / Logistics
Service
Java, Python, AWS (Lambda, DynamoDB, ECS), in-house ML
Headline metric
1M+ requests/day, $100K+ annualized savings, 50% latency cut

A million requests a day and a shipping bill that got smaller

Operational outcome

I spent three years and change at Amazon as a Software Development Engineer working on services in the shipping cost path. The systems were Java, the scale was high, and the work was a mix of "make this faster" and "make this cheaper."

The three pieces that show up below are the ones that mattered most: a data-map redesign that cut latency and cost together, a price-prediction pipeline in Python, and a legacy-to-Native-AWS migration that meaningfully shortened cart times for customers.

Tell us what you’re trying to ship.

A principal engineer reads every inbound. We reply same day on weekdays, with an honest read of whether we’re the right team for the work.