I take AI agents from scope to production inside real enterprises. The most recent one applied ~$100M of cash in its first two days — designed, built, and shipped with the analysts who use it. Behind that: thirteen years of making new technology actually land in complex organizations. The model is rarely the bottleneck. The deployment is.
01 — Work
Build stories, not service categories. Every number here is real and checkable; where a client is unnamed, that's by agreement.
A Fortune 500 media & streaming company had a classic AR problem at streaming scale: customers pay large lump sums decoupled from invoicing, and someone has to figure out what the money is for. I designed and built an LLM agent that reconciles those payments against open receivables across three lines of business — ~$100M of cash applied in its first two days.
I was the only technical person on the project: scoping with senior finance leaders, agent and conversation design, tooling, data pipeline, implementation, iteration in production.
A six-month-stalled technical problem, reframed as two sequenced programs — first carving a Fortune 500 US industrials company out of its former parent's multi-country SAP estate, then an S/4HANA conversion. A custom AI diligence stack compressed validation from weeks to 48 hours and closed the $5M engagement: Summa's first Fortune 500 US reference.
The last program I was responsible for at Summa: operating-model integration of seven Latin American subsidiaries for a multinational group — unified governance, harmonized processes, and cross-border reporting under compressed timelines. Client confidential.
Co-founded Gigflow, an EU-Innovation-Fund-backed AI startup (€300K grant) looping automation with LLM reasoning from the moment the OpenAI API opened. Zero to 4,000 monthly signups in five months, $4M valuation in year one, a 12-person team.
Nine years at Accenture across two stints — including designing and building SUMMA, a 17-country shared-services center for a $7B multi-industry holding, which I returned to commercialize years later. ERP programs, post-merger integration, and operating-model design for global leaders in brewing, consumer goods, aviation, and mining. Strategic planning at Coca-Cola FEMSA. B.S. Industrial Engineering, Universidad de los Andes. Working languages: English, Spanish, Portuguese, Polish.
02 — The Lab
Engineers stress-test frontier models with games and physics sims. I test them against P&L problems: real companies, real customers, real money. Each of these is a running experiment I build, operate, and document.
An artisan bakery's entire commercial engine, built end-to-end: the website, payments, SMS, and CRM — plus an agent-supported marketing engine and an operational backbone wired through Airtable. A one-person ERP, run in production.
The current experiment on the bench. Notes and a full build story will land here as it takes shape.
WhatsApp conversation reconstruction for legal evidence: chain-of-custody logging, SHA-256 verification, audio transcription, and court-ready PDF exports. Built because a real case needed it.
A provenance-tracked database of my own career: every claim sourced, contested facts flagged, guardrails against embellishment. It already writes my CVs. Next: an agent you can interrogate instead of reading one — ask my career anything, grounded in the record.
03 — Writing
Next up: the full build story of the cash-application agent.
04 — About
My career happens at the seam between what a technology can do and what an organization will actually adopt. Post-merger integration and AI deployment are the same problem wearing different clothes.
I spent nine years at Accenture making two systems, two teams, and two ways of operating become one that functions on Monday morning — including designing and building a 17-country shared-services center from scratch. Then I co-founded an EU-backed AI startup and shipped multi-agent LLM systems years before enterprises began piloting them — production experience with what these models actually do, where they break, and the distance between a demo and a system that survives contact with real operations.
Most recently I returned to commercialize the shared-services center I once designed — closing its first Fortune 500 US reference with an AI-accelerated diligence process — and built a production LLM agent that applied ~$100M of cash in its first two days.
Now I work as an independent applied-AI operator, and I run the lab: real businesses used as test benches for what frontier models can do when you point them at actual P&L problems.
05 — Contact
Two conversations are welcome here: you want agents deployed inside a live organization — scope to production, adoption included — or you're building a team that does this and want someone who already has. Either way, write.