Omnilogic Labs is two deep domain experts — Mike and Juan — who have spent careers building, repairing, and teaching the systems other companies depend on. Our depth spans AI, finance, organizational behavior, insurance, fraud, developer tools, logistics, networks, and computer hardware. For bigger jobs we assemble a vetted network around that core. We are deliberately small, deliberately deep, and accountable end to end. Not a body shop.
Builds AI systems for businesses — the kind that keep running long after the demo. Spent five years as a software architect on Uber's autonomous-vehicle program, building the engineering culture and safety-critical practice for 500+ engineers. Earlier, engineering lead on a #1 Food & Drink iOS app that reached roughly 10M users and was acquired by Yelp. Research roots in human-robot interaction and low-level systems.
"I ship systems, not strategy decks. One of those can break — which means one of those is real."
AI and enterprise transformation. Led global fraud-detection machine learning at payments scale, with data pipelines serving tens of thousands of corporate clients, and built large-scale risk data platforms. Earlier, strategic business development across Latin America and the founder of an algorithmic-trading platform. Pioneered our preview-to-engagement model.
Working prototype on real data in a week. Production in thirty days. The ROI question shifts from "if" to "how fast."
When a problem needs more than the two of us, we bring in vetted specialists we've worked with for years — UX, hardware, robotics, and design among them. We can also help you staff a team of your own: we know who's good, and we've made the hiring mistakes so you don't have to.
We build a working prototype against your real data, so you can judge the value before committing to a build — not from a slide, from something that runs.
From validated prototype to a production-ready system with predictable outcomes. Outcome-based milestones, not hourly meters.
We own the result, not a ticket queue. And we leave your team able to run what we built — that's the Teach half of the lab.
Build, Advise, and Teach are not three service lines bolted together. Build and Advise each map to a founder who has done the work at scale; Teach is the discipline both carry — the clean handoff that leaves your team able to run what we built. The expanded record below is selected, not exhaustive: the engagements that shaped how we operate.
20+ years shipping software, from safety-critical autonomous-vehicle infrastructure to consumer apps at scale. Owned software architecture, CI/CD, code review, and quality practice for a 500+ engineer safety-critical codebase. Earlier, engineering lead on a #1 Food & Drink app to ~10M monthly active users and a Yelp acquisition. Research roots in humanoid robotics, human-robot interaction, and cross-platform instruction translation.
"I ship systems, not strategy decks. One of those can break in production — which means one of those is real."
Two decades across payments, consumer electronics, and software worldwide. Led global business intelligence and ML fraud-detection at payments scale, with pipelines serving tens of thousands of clients, and built a billion-record risk data lake with real-time alerting. Earlier, ran a ~$50M/yr Latin American business unit and centralized CRM across 24 countries. MBA, International Business; MS, Telecommunications.
"A working prototype on your real data in a week. The ROI question stops being whether — and becomes how fast."
Most projects don't fail on the engineering. They fail in the gap between what leadership means and what gets built — and in the reverse gap, where engineering reality never makes it back into a decision anyone can act on.
The single skill underneath Build, Advise, and Teach is translation: turning executive intent into working systems, and turning what the systems reveal into choices a board can sign off on. It is why the same two people can scope a build, sit across from your risk committee, and train your team — the medium changes, the act of translation does not.
On focused work, the founders implement directly. When a build demands more depth or more hands, we orchestrate a curated network of vetted specialists we have worked with for years — assembled to fit the problem rather than padded to fill a roster. You get the same people accountable for the result, end to end.
Specialists we have shipped with before — UX, hardware, robotics, and design among them — not strangers pulled from a bench. We have made the hiring mistakes already so you don't have to.
The team grows only when the build demands it and shrinks back when it doesn't. No seat-count to justify, no bodies to keep busy — the cost tracks the work.
If you'd rather build the capability in-house, we help you hire it. We know who's good, and a clean handoff means your people run what we built — that's the Teach half of the lab.
