Engineered intelligence
Now with VEOX you can invent algorithms that otherwise humans can't — continuously engineering better intelligence from your data.
Every organisation knows what its models score. Almost none know what they could have scored. That number has never been anyone's job.
Someone shipped it because the quarter was ending. That date was a calendar decision, not a mathematical one — and the model has sat at that number ever since.
It doesn't appear in a budget. It appears as claims leakage, unplanned downtime, churn you priced as normal, and inventory you wrote off as the cost of doing business.
Not guessed at, not benchmarked against a vendor's demo — searched, measured, and handed back to you as something that runs. That's the entire company.
The category
There's a name for what happens when you stop hand-building models and start searching for them. We've been calling it engineered intelligence: a system that proposes candidate algorithms, scores them against your data, keeps what wins, and doesn't get tired.
The distinction that matters is where the intelligence sits. In every previous generation of tooling, a person decided what to try and the machine executed it faster. Here, the machine decides what to try. A person sets the objective and the budget.
It's a young category and we're not going to pretend otherwise. What we can do is publish the record, including the runs we lose, and let you check it.
One engine, several outputs
Everything below runs on the same search. What changes is the space it searches and what it hands back.
The record so far
A leaderboard that only shows wins isn't a leaderboard. Every run we've scored is in the table on the jAIn page, wins and losses in the same list.
VEOX Research
Both papers are open, ungated, and written for someone who'll check the method. Start with the technical one.
How the search works: candidate generation, the fitness protocol, budget scaling and where the curve flattens. Full per-dataset results across 847 runs, losses included.
The shorter one, for the meeting you have to run. What engineered intelligence changes about where modelling effort belongs and who does it.
Who's building this
VEOX was founded by Jepson Taylor — ex-quant, ex-HireVue, holder of 14 AI patents. He sold his deep-learning company Zeff.ai to DataRobot, then held C-level roles at DataRobot and Dataiku. He has watched two generations of AutoML get sold and knows exactly what this isn't.
The engineers behind him came from Intel, Micron, Cisco, VMware and Adobe. We work with a deliberately small number of design partners at a time, and we're selective about fit — if your problem is one the search won't help with, we'd rather say so on the first call.
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