Our team will review your submission and reach out within 48 hours to discuss how Crunch’s research community can tackle your challenge.
Harness the power of
AI Engineers
PhDs
ML Models
AI Tasks Completed
As the cost of common intelligence drops and the knowledge horizon expands, the value of above benchmark intelligence skyrockets. Crunch brings thousands of global scientists and technologists into secure collaboration to solve industry's toughest problems.
Crunch delivers the most valuable asset of the century: an engine to beat benchmarks, i.e., the last mile of knowledge.
Powered by 3.5x more experts than leading AI organizations
Real-world outcomes powered by Crunch
How ADIA Lab partnered with Crunch to crowdsource novel algorithms that detect "Structural Breaks"—invisible changes in market rules—achieving double-digit accuracy improvements.
Broad Institute of Harvard and MIT: Predicting biomarkers for autoimmune disease from standard pathology images.
Tier-1 Global Bank: Crowdsourced machine learning models for ultra-low latency FX pricing and inventory optimization.
Collective meritocratic intelligence for high-stakes environments.
Fair, sybil-resistant , and always frontier-ready.
Proprietary data, confidential compute and auditable results.
A system with proven results
performance improvement vs ADIA Lab Causality Research benchmark
improvement vs Broad Institute of Harvard and MIT computer vision benchmark
trading cost savings at major bank (FX OTC)
Compete on real-world problems from leading institutions like the Eric and Wendy Schmidt Center at the Broad Institute and ADIA Lab.
Ranked among the top-paying ML competitions globally, with up to $100k+ prize pools paid in USDC.
Build your rankings and a verifiable track record solving the world's highest-stakes problems.
Run Crunch
crunch-node init my-crunch --pack realtime
cd my-crunch
# Configure your Crunch
# Now run the node
make deploy
Python Notebook Cell
crunch_tools = crunch.load_notebook()
def train(X_train: pd.DataFrame, y_train: pd.DataFrame) -> None:
... # your magic!
def infer(X_test: pd.DataFrame) -> pd.DataFrame:
... # your magic!
crunch_tools.test()
Explore challenges and rewards
DataCrunch Equity Market Neutral #2
This weekly cross-sectional problem target the expected returns of the 3000 most liquid US equities. DataCrunch uses the quantitative research of the CrunchDAO to manage its systematic market-neutral portfolio.
“Crowdsourcing has a very important role to play in investing. Firms turn investing problems into forecasting problems, then outsource to global researchers.”
Prof. Marcos Lopez de Prado
Global Head of Quantitative R&D Adia Lab
“I'm very excited to see what the participants are going to come up with, because if they come up with useful things, that's going to be very impactful.”
Prof. Guido Imbens
2021 Nobel Prize in Economics Laureate
“Institutional finance hasn’t yet had disruption, but likely will; specifically with respect to the competition for research talent in the years to come.”
Peter Cotton
Co-Founder and Chief Scientific Officer Crunch Lab
Crunch is an open network of 11,000+ AI engineers and 1,200+ PhDs who compete to solve high-stakes problems for leading institutions. Organizations bring their toughest challenges, thousands of independent scientists build machine learning models to beat them, and the best models win. The result is prediction accuracy that consistently outperforms in-house teams and traditional approaches.
Most AI today is becoming a commodity. The same models, the same tools, the same results. That's consensus intelligence. Crunch exists for problems where "good enough" isn't good enough. Thousands of independent researchers working on a single challenge surface signal that no single team would find on their own, delivering up to 40% improvement over existing benchmarks.
An institution defines a challenge and it goes live on the Crunch Network, where thousands of researchers build and submit competing models. All computation runs on confidential infrastructure with proprietary data protection and auditable results. The institution receives top-performing models and the insight they produce, without exposing sensitive data to individual participants.
High-stakes prediction problems across quantitative finance, healthcare, life sciences, energy, and beyond. Proven results include building the world's most accurate causal discovery algorithm with Nobel Laureate Guido Imbens and ADIA Lab, predicting biomarkers for autoimmune disease with the Broad Institute of Harvard and MIT, and delivering crowdsourced ML models for real-time FX pricing at a tier-1 global bank.
Scale and structure. An in-house team gives you a handful of perspectives. Crunch gives you thousands, all competing independently, ranked by actual performance against real data. The infrastructure is onchain, sybil-resistant, and always frontier-ready, staying current without the overhead of recruiting and retaining top ML talent.
Researchers ("crunchers") join the network, pick a challenge, and build models on the Crunch Hub. Competitions rank among the top-paying ML challenges globally, with prize pools up to $100k+ paid in USDC. Performance is tracked on a global leaderboard, building a verifiable track record. You build models that matter and get paid when they perform.
.svg)
