Real systems, rebuilt to share
AI you can click,
not AI you read about.
I build AI for the work between the trade and the audited number —
in funds and in physical commodities. NAV and mark-to-market, reconciliation and
month-end close, counterparty management and the receivables ledger, documentary
compliance on cargoes, proprietary research and due diligence, market intelligence
that reads short- and long-term relative strength. The work taught me
both languages of the problem — capital and controls on one side,
Python and LLM agents on the other — and the systems built with them run unattended,
in production, every day. What's here are working rebuilds — cut
down several grades, onto synthetic data and fictional names, so they can be shared.
A workshop, not a storefront
Nothing here is for sale — no pricing, no booking link, no funnel.
I built these systems because I needed them, and they've paid for themselves many times over — in hours, and in errors that never happened. Sharing the working patterns costs me little and might save you a quarter of trial and error. That trade seemed obviously worth making.
What you see is deliberately de-tuned.
The originals run in production with real money, real counterparties and real consequences. Out of respect for privacy and confidentiality, every demo is rebuilt several grades down before it's published: synthetic data, fictional names, generic workflows, and a fraction of the moving parts. What a five-minute click-through can't carry — live data feeds, multi-entity books, the edge cases and watchdogs accumulated through years of production failures — is exactly what the originals are made of. The plumbing, the guardrails and the judgment calls shown here are the real thing. The numbers, and the difficulty, are not.