I lead engineering teams that ship AI and modernized platforms, and I stay in the code.

Today that is a 7-engineer team at Antech, the veterinary diagnostics business inside Mars. On agentic workflows we raised sprint throughput 4.2x, replaced a 20-year SOAP/Java monolith with .NET 10 microservices on Azure, and moved a 2 PB imaging archive to Azure. In spring 2026 I took a commercial real estate lender's AI program from zero to production in two months.

Under 24h Production incident MTTR at Antech, down from weeks

30 to 80 hrs/wk Staff time returned at the lender, measured at observed adoption

Email me Résumé (PDF) Flagship case study

Where the work ran

Mars 2024 to present
Vellum Capital 2018 to present
2022 to 2024
2020 to 2021
Foundation for Economic Education 2015 to 2020
2011 to 2015

What I solve

Three problems I take on, each linked to the work behind it.

Team leadership

Lead a hands-on team that ships

I lead a 7-engineer team at Antech and write code with it. Before that I ran engineering as CTO at Royalty Exchange and Director of Technology at FEE, with hiring, reviews, budget and roadmap, and managed 24 engineers at Celsius.

Platform modernization

Replace a legacy platform while it carries production load

At Antech my team decomposed a SOAP/Java monolith into .NET 10 microservices on Azure, and I led the move of a 2 PB imaging archive to Azure.

AI in production

Put agents into production under change control

My team built the PIMS Support Tool, which analyzes API health reports on Azure OpenAI / Azure AI Foundry. At a regulated lender I built the AI function and had it in production in two months.

The work

Case study · Mars veterinary diagnostics

Platform modernization and agentic engineering at Antech

My 7-engineer team at Antech replaced a SOAP/Java monolith with .NET 10 microservices on Azure, built the PIMS Support Tool on Azure OpenAI, and moved a 2 PB imaging archive to Azure. Production MTTR went from weeks to under 24 hours.

Screenshot of cheatsheets.davidveksler.com — cheatsheets.davidveksler.com

Case study · CRE bridge lending

Founding the AI function at a regulated lender

Two services were live in production at handoff, including a daily investor-enrichment pipeline, on a governed platform where nine departments could publish reviewed, drafts-only skills (agents propose, humans send). Two months, one engineer, on contract.

Architecture diagram of the lender platform: governance, skill marketplace, agentic client, observability, over read-only systems of record — diagram

CRE lending · production service

An enrichment service that researches like an analyst and never sends

A service that ran daily in production: research a live investor cohort from public sources, score its own research, draft the outreach, and write it to the CRM for a named human to send. The system sent nothing, by design.

Pipeline diagram: public sources, quality gate, depth-scored research, draft, then CRM, rep tool, and a named human who sends. A crossed-out line marks the send path that does not exist. — diagram

Case study · Regulated fintech / DeFi

Crypto custody, and recovering $4M+ in stranded creditor assets from 5M+ vaults

AI platform work paired with a decade of institutional custody and DeFi architecture: CCSS Level 3 custody at Vellum, wrapped-token launches, and $4M+ recovered for creditors during the Celsius bankruptcy.

Screenshot of vellum.capital — vellum.capital

Case study · Public, one-person agentic system

A governed agentic pipeline you can audit end to end

A collection of 160+ interactive reference pages, built and maintained by one person plus AI agents, held to one written spec and shipped through a human merge gate. The pipeline is the thing worth looking at: it makes the next page cheap and consistent, and every commit is browsable in public.

Case study · Public agentic system

A civic data reference built in a day, and kept current by agents

Built inside a day from a paragraph of intent, then kept current by six pull-request-only agents behind a human merge gate. Every published fact carries its source, the date it was checked, and who verified it.

Screenshot of coloradofirearmswatch.org — coloradofirearmswatch.org

Case study · Public evidence reference

A graded, sourced reference on who pays for AI

A public reference site that grades who actually bears the cost of the AI infrastructure buildout: electricity, water, grid equipment, and memory. Every number carries a primary source and two dates, published and last checked, and one person plus AI agents keeps it current.

Screenshot of whopaysforai.org — whopaysforai.org

How I run programs

An AI program keeps the trust of its risk and audit people by calling things what they are. A feature is done when it runs in production under change control with a named owner, and everything before that point is a draft. Programs die when their status reports drift from that standard, and the drift always starts with a demo somebody called finished.

Sources Systems of record Production, tickets, documents. Read-only.
Agent Drafts Proposes the change and cites what it read.
Named human Approves the write Every irreversible action passes a person.
Production Under change control A named owner, an audit trail back to step one.

How a feature reaches done. The same shape in every case study above: agents draft, a person approves, production stays under change control.

Working on something like this?

Send me the job description, or two lines on the problem and what it costs you today. Short, specific messages get the fastest reply.

david@davidveksler.com Résumé (PDF) Where I fit best

Last updated 2026-11-14 · Changelog · How this site is built · Press · Projects and writing