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01 / 07Positioning & copy2026

Aeon Labs

Machine learning, end to end.

Aeon ML takes a dataset from upload to a trained model. An advisory scores its readiness and suggests the task, a preprocessing agent writes a plan you accept or edit, feature engineering reports the impact of every new column, and an algorithm advisor and an AI reading of each run say what to try next.

End-to-end ML platform
Positioning & copy
Data to deployed model

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Results

Data to deployed model

R² on AmesHousing: 0.93
Data quality after prep: 99

Inside the launch

Sign up: build ML pipelines at warp speed

Sign in, over a wireframe of neural architecture

Projects in a workspace

ML Advisory: the suggested task and data quality

Multivariate analysis, with a preprocessing plan

The preprocessing agent writes a minimal plan

A thorough plan, with its blocking issues

Encoding and scaling steps, ready to accept

Plan saved as a preprocessing config

Missing values, a strategy per column

Cleanup: columns the plan chose to drop

The preprocessing report: before and after

Strategies set from the plan, then applied

The diff: new columns and the target distribution

Feature engineering, planned by its own agent

Feature impact and the health of new features

Dataset lineage, from original to features

The algorithm advisor ranks what to train

AI interpretation of a cross-validated run

Palette

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