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Thesis Machine Learning

3 viewers · 30d

Total raised

$150K

+$150K this year

1 filing since 2026 · latest Other filed

Cumulative raised
LAST ROUND
Other · $150K
ROUNDS
1
INVESTORS
1
FOUNDED
2026
HQ
Georgetown, TX
SECTOR
Fintech
EMPLOYEES
N/A
30D VIEWERS
3

AI overview

Updated

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.

What sets it apart

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 history

1 round
OtherApr 1, 2026
Form D
+$150K$150K total

Latest SEC filings

via EDGAR · CIK 0002127213
Form D · Apr 1, 2026View on EDGAR

Products

1 tracked

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
  • Adaptive constituent selection and weighting at the composite level