We Build the Tool and the Network for the Health Economy

Any health signal in. 128 numbers out. Your model on the network.
Your research becomes a product.

Two Products. One Mission.

The tool without governance is dangerous — unvalidated health models loose in the world. The governance without the tool is just a committee with nothing to govern. We built both.

Models Built on GLE

Each model was built the same way: health signal in, 128 coefficients out, classifier trained. These are the first. Yours is next.

View full validation details on BAGLE

From Researcher to Founder

You bring the biology and the data. We bring the math. You keep 90% of every classification.

1

Collect Signals

Breathing recordings, biosensor readings, voice samples, molecular data — whatever you study.

2

Encode

Send signals to BAGLE API. Get back 128 numbers per signal. The hard math is done.

3

Train & Validate

Train a classifier on those 128 numbers. ParagonDAO validates accuracy before patients rely on it.

4

Ship & Earn

Your model becomes a screening tool anyone can use. A patient breathes into their phone — your model answers.

Research Behind the Encoder

The General Learning Encoder (GLE) is a foundation model for frequency-domain health intelligence

Breathing as Biometric Identity

96.8% identification accuracy across 97 participants using nasal airflow patterns alone. The same encoder that classifies disease also verifies identity — zero additional power.

GLE: Universal Health Signal Encoder

27.5% better than competition-winning solutions on subject-invariant health prediction. Works on new users immediately — no calibration, no retraining.

The Mission

10% of all network fees fund one thing: preventing loss of life. 988 crisis detection, community health screening, free GLE access for crisis organizations. The tool without the mission is just technology. The mission without the tool is just hope.

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Why Not Just Train All the Models Ourselves?

Latest Update

Why Not Just Train All the Models Ourselves?

The investment community and partners challenge us on why a health AI model verification network like ParagonDAO needs thousands of builders — why not just train every model ourselves and own the space? The answer reveals why the health economy must be a network, and why this architecture delivers more stable, compounding returns than the monopoly play.

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