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Thesis Machine Learning

Deep learning thematic rotation indexes for ETF issuers

Raised to date

$150K

across 1 Form D filing

Investors
1
Last filed
Apr 6, 2026

About

Thesis Machine Learning builds deep-learning-driven index methodology for thematic index providers, custom-index firms, and ETF issuers. Its Thematic Intelligence Infrastructure analyzes a provider's existing thematic indices and builds a rules-based composite index that rotates toward themes with more favorable relative risk, using proprietary Deep Learning Risk Scores. The resulting index can be licensed into an ETF or other product structure while the provider keeps its brand, governance, and underlying benchmark IP.

An 'index-of-indices' deep learning layer that sits on top of a provider's existing thematic benchmarks instead of replacing them, aimed at reducing AUM leakage when a single theme cools.

Funding

  • OtherFiled Apr 6, 2026Form D
    $150K/ no target

Product

Thesis Thematic Rotation Indices

Index methodology licensing

Composite thematic rotation indices built on top of a partner's existing thematic index lineup, using deep learning models to score, rank, and rotate across themes.

  • Deep Learning Risk Scores for systematic rotation across themes
  • Composite rotation index layered above provider-owned benchmarks
  • Rules-based, explainable methodology that fits existing index governance and licensing

Built for

Thematic index providers, custom-index firms, and ETF issuers

People on file