# Ensue | Your world model's research team

Canonical: https://ensue.dev/

> Coordinated AI research against your repo, your data, and your benchmarks. Verified breakthroughs ship as pull requests your team can review. We only get paid when the number moves.

AI research · for teams building world models

# Your world model’s research team.

Ecological forecasting, remote sensing, species distribution. We took a 25M-parameter model past the 800M original on a public benchmark suite, shipped as pull requests.

+17.3% harmonic mean · 96.8% fewer params

Same accuracy, a third less compute. A configuration search your team doesn’t have time to run, with every rejected config published alongside the wins.

1.53× throughput · −34% GPU cost · zero accuracy loss

Predictive maintenance and sensor models that beat published deep learning on multi-condition data. Trains in minutes, no GPU required.

22–34% better than published methods · NASA C-MAPSS

Diagnostic and imaging models, evaluated the way your science demands: pre-registered, permutation-tested, ablated. Evidence your technical and clinical teams can review.

0.648 → 0.837 AUC · Alzheimer’s OCT cohort (public data)

We only get paid when your benchmark gets beat.

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Automatic results

## Breakthroughs in months days.

BREAKTHROUGH VERIFIED

## Shipped to your repo as a PR.

Try the demo →

Where we work

## breakthroughs in the field.

### Climate, ecology& geospatial

Improve models for a changing physical world, without sacrificing the constraints that make results scientifically useful.

### Medicalimaging

Improve diagnostic and imaging models with evidence your technical and clinical teams can review.

### Scientific &industrial vision

Turn complex sensor and visual data into a model advantage: defect detection, forecasting, uptime.

### Real-worldrobotics

Improve the models that make machines work beyond curated benchmarks.

In progress. Sign up for updates as we make these breakthroughs public.

Working in another technically difficult ML domain? If your model has a measurable objective and meaningful constraints, we can help →

From a customer

Lance Legel

CEO · Ecological Intelligence, Inc.

Visiting Scientist, UC Berkeley · Former NASA

What you get

## Science that scales.

01 · Model science

### A better model than the one you have.

The loop runs thousands of experiments across architecture search, training schedules, and data curation, and converges on the combination a lone researcher would take six months to stumble into.

On one engagement, that meant beating an 800M parameter model with 25M parameters. Science over scaling, verified against the customer’s eval.

25M > 800Mparams, on the customer’s eval

02 · Inference and kernels

### Faster on the hardware you ship on.

A model that’s slow on your hardware isn’t shipped. We search kernels, quantization schemes, and compilation targets, and land the speedup as a PR you can merge the same day.

We’ve done it on Apple silicon and Nvidia GPUs, across six generations of hardware, with the results published.

6.3×on Apple Neural Engine

Who you’re working with

Austin Baggio

CEO · Co-founder

Your first call and your weekly report are with me.

Sai Vegasena

CTO · Co-founder

I design the evals and steer the loop.

Set the eval. Set the GPU budget. We only get paid when the number goes up.