One person, start to finish.
You know what's broken.
There's nobody to build the fix.
One senior engineer, using AI to build at the speed of a team. We take a single internal workflow — the one running on spreadsheets, email, and somebody chasing people — and put it into production in four weeks. Then we run it.
15 MINUTES · NO DECK · YOU PICK THE TIME
Named references at Gigster · World Gold Council · HeartSciencesSee the work →
Every company has one. Nobody has a team for it.
One workflow that never got a system. It spans two entities, or three roles, or a process somebody promised the board would be fixed. It runs on spreadsheets, email, and a person chasing people. Hiring a team to fix it takes longer than the deadline you already committed to.
The feedback loop
It watches its own work, catches what's wrong, and fixes it before you see it. Not a demo — something that keeps working without you.
The bottleneck
One slow step limits everything else. We find it first — fixing anything else looks like progress but changes nothing.
The fulcrum
A small change in the right place improves everything after it. We put our effort where it helps most, not where it's easiest.
A team's output, without a team's overhead.
Production in four weeks, not two quarters. There is nobody to bring up to speed, and AI does the volume work while we make the decisions.
A fraction of hiring for it, and you stop paying when you stop needing it.
One name on the plan and on the failure. No bench, no handoffs, no rotating account team.
Fixed price, agreed before anything starts. Written targets in the contract. You accept the work before it is final.
Trusted where a mistake costs more than money.
Omar Trejo — The Operator
Builds it, runs it, owns the result.
15+ years building operational intelligence software that Fortune 500 companies and fast-moving startups still run today. For the last twenty months that has meant being the entire product and platform function for a multi-site clinical operations platform — multi-role, integration-heavy, auditable, where a dropped hand-off had a real cost. Not clinical expertise: an operator carrying a system where failure was expensive. A cross-entity operations workflow is a lighter load carried the same way.
See the Escape Velocity Labs portfolio ↗Omar is one of the most skilled engineers I have worked with in 20 years of delivering technology. He led a program to build a custom ML platform for a top-10 global client. The work continuously exceeded expectations and became their foundation.
John LiangVP of Customer SuccessGigsterFirst time with a large budget, and Omar got the brief right away. Came back fast with interim results and nailed the delivery. His ability to turn problems into working systems made him our first call for every ML project after.
Johan PalmbergQuantitative ResearcherWorld Gold CouncilOmar delivered in two weeks what our team estimated would take six months. The scoping session alone saved us from a $200K mistake. He operates at a level you rarely see in this industry.
CONFIDENTIALAI Program ManagerMajor US TV networkWe needed a valuation engine that could price properties within 10% of closing price in dense markets, in seconds instead of days. Omar built it. That system became the reason investors took us seriously.
CONFIDENTIALFounder / CEOPropTech companyWe came to Omar with messy data and a deadline we'd already missed once. He turned it into a working system in days, kept us updated throughout, and it held up in production — reliable in a way this field rarely is.
CONFIDENTIALHead of DataHedge fundOmar did outstanding work designing and building the backend for our cloud-native AI-ECG platform. I recommend him to any organization needing a skilled and reliable engineering partner.
Jason DomaskDirector of Software EngineeringHeartSciencesProof, already running.
A telecom routing platform, ~1TB/day, 12x+ return in year one.
A top-10 global telecom was losing margin on international roaming. Engaged through Gigster, ML LABS built the routing platform that ran on roughly 1TB of traffic data a day and returned more than 12x in its first year.
READ THE CASE →A property valuation engine, within 10% for 90% of properties.
ML LABS built the valuation engine behind a PropTech platform: photos, satellite imagery, and location data turned into a price in seconds, within 10% of the closing price for 90% of properties. Knowing when the number was soft was the harder half.
READ →CASE STUDYTwo security reviews, catching what either alone would miss.
Two reviews ran side by side: an outside firm's penetration test and ML LABS' AI-assisted code review. Together they caught what either alone would have missed, including a login check that could fail with no one noticing.
READ →CASE STUDYA multi-site clinical platform where a dropped hand-off had a real cost.
Multi-site automation breaks when one clinic's design is copied to all of them. ML LABS built the platform HeartSciences runs across its sites: shared rules in the center, room for each site's differences at the edge.
READ →Four weeks to something real. Then we operate it.
Written targets go into the contract before work starts. Design and Build: full refund until you accept. Operate: no lock-in — cancel any time with 30 days' notice.
Tell me which workflow is the worst one.
Fixed scope · written plan · measurable targets