About dysruptLabs

Welcome to the official Substack for dysruptLabs. This publication provides public insights and analysis from our work in hybrid forecasting intelligence, where we develop validated, early-warning signals and alternative data for sophisticated investors.

Our mission is to provide a decision advantage by revealing when and why market consensus is likely to be wrong.

Who We Are

dysruptLabs is a private intelligence firm serving family offices and institutions. For over 15 years, our team of 12 researchers has pioneered a hybrid forecasting model that merges expert human judgment with machine learning validation.

Our core platform is Almanis, a private prediction market focused on geopolitical and election forecasting. It is powered by a rigorously curated global network of over 900 contributors and operates at scale with 99.99% ("Four 9s") efficiency. This system allows us to generate proprietary data on complex events, a capability that has been honed through government research programs and peer-reviewed academic publications.

We maintain a small digital footprint and operate through direct relationships to ensure the confidentiality of our forecaster network and institutional partners.

Our Capabilities

Our work is centred on generating intelligence that is not available through traditional channels like Bloomberg or Reuters.

  • Collective Intelligence Systems: We run scaled private intelligence systems that generate probabilistic assessments of complex macroeconomic and geopolitical events using DARPA-replicated aggregation methods. The core technology is a highly modified private prediction market.

  • Alternative Data Products: We produce proprietary divergence indicators and probability distributions designed for global macro strategies and systematic trading.

  • Strategic Intelligence R&D: We conduct confidential research into information asymmetries and black-swan detection methodologies.

Why Subscribe?

By subscribing to our Substack, you will gain access to:

  • Public analysis derived from our proprietary forecasting models.

  • Insights into macroeconomic and geopolitical events where market consensus may be mispricing assets.

  • Commentary on the science and application of collective intelligence and judgmental forecasting.

Our Credentials & Independent Validation

Our methodology is not just theoretical; it has been tested and proven in demanding environments.

  • DARPA NGS2 Program: Methodology successfully replicated using out-of-sample datasets.

  • eBioMedicine (The Lancet): Our ML-augmentation framework was peer-reviewed and published.

  • Journal of Financial Markets: Our core forecasting methodology has been published.

  • 15-Year Track Record: We have an extensive operational history across multiple forecasting domains.

  • Family Office & Institutional Trials: Our approach has been informed by trials with major hedge funds and family offices.

Leadership

  • Karl Mattingly, Founder & CEO: 25-year career at ANZ Bank in senior risk and international banking, followed by 17 years developing classified and commercial intelligence systems. MBA from Columbia University.

  • Prof. Anne-Louise Ponsonby, Chief Scientist: Principal Investigator at the University of Melbourne and The Florey Institute, leading our scientific validation and academic collaboration.

  • Stephen Markscheid, Founder & Director: Over 35 years of international experience at Boston Consulting Group and General Electric. MBA from Columbia University, MA from Johns Hopkins SAIS.

Contact & Inquiries

For direct inquiries or to learn more about our services for institutional clients, please contact us.

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Real-time probability forecasts in macroeconomics, geopolitics and corporate performance.

People

Researcher in risk & quantitative methods. International banker, economist, accountant & investor living in Australia, Asia & USA. Finds interesting questions for Almanis by Dysrupt Labs to forecast.
Kelly is a Researcher for Dysrupt Labs. She holds degrees in Economics and Political Science from the Australian National University.