BUILDING NEXT-GEN FORECASTING PARADIGM.
AI-driven forecasting for complex markets and chaotic systems.
RESEARCH-DRIVEN AI COMPANY
OUR MISSION
Duon Labs focuses on modeling chaotic systems with scientific precision. We build probabilistic forecasting systems that go beyond point predictions, exposing the full structure of uncertainty in real-world markets.
Our systems power smarter decision-making for traders, platforms, and researchers alike.
OUR TEAM
RESEARCHERS
Experts in probabilistic modeling, scaling laws, and uncertainty quantification.
ENGINEERS
Builders of GPU-optimized infrastructure, simulation kernels, and forecasting APIs.
TRADERS & STRATEGISTS
Practitioners guiding product priorities based on real market constraints and opportunities.
REBUILDING THE STACK FROM FIRST PRINCIPLES
SICK BIDE
A foundational distribution modeling framework that encodes probability directly from binary representations, enabling exact, efficient, and assumption-free inference.
APOGÉE
An open research initiative exploring the scaling laws of financial predictability. We ask: how much of the future can truly be inferred from raw historical data?
VOYONS
Our flagship forecasting model for crypto markets. Probabilistic, scenario-driven, and production-ready, available via API or SDK.
GUIDING PRINCIPLES
TIME IS UNIVERSAL
Every market signal is timestamped. We treat time as the backbone of all predictive structure.
RAW DATA FIRST
We work at the lowest, unfiltered, unaggregated level to extract signal without loss.
REMOVE ALL LIMITS
We question assumptions. We push hardware. We reimagine what's possible in forecasting.
OUR VALUES
-
OPEN SOURCE BY DEFAULT
We publish core technologies (SICK BIDE, Apogée, evaluation tools) and welcome community contributions.
-
RESEARCH-FIRST
Our roadmap is driven by experimentation, not marketing timelines.
-
USER-FOCUSED
We build tools that traders, quants, and devs actually want to use: clean, composable, fast.
JOIN US
The future of forecasting won't be built by guessing. It'll be modeled, simulated, and shared.