Running ML competitions is hard: you need infrastructure, visibility, trust in scoring, and a way to motivate participation. Most companies lack the reach or technical setup to do this effectively.

Core Benefits

• Reach a large, pre-engaged community of ML engineers
• Transparent scoring and fair competition design
• Prize distribution is handled globally over blockchain wallets

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Key Features

• Very easy to use for ML engineers
• Models can be fully encrypted and private. Nobody will ever see your code.
• No blockchain wallets need to be managed: It uses embedded wallets and gas sponsorship so ML engineers don’t need to handle it.

Organizations want to leverage collective intelligence but face barriers: infrastructure complexity, data security concerns, and difficulty in attracting top ML talent.

Core Benefits

• Tap into a global ML talent pool with minimal overhead
• Retain full control of data and competition rules
• Reduce cost and time compared to in-house solutions

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Key Features

• Deploy competitions directly from your node
• Permissioned access and fine-grained data control
• Automated orchestration of scoring and payouts
• Seamless integration with existing data pipelines
• Audited Contracts keep your money safe
• Nodes integrate into Multisigs, for ease of use and security

Current ML infrastructure struggles to meet sub-millisecond latency and high throughput demands while remaining extensible for new use cases.

Core Benefits

• Predictions and computations in <60 microseconds
• Handles terabytes of daily throughput
• Extensible engine for advanced analytics (e.g., anomaly detection)
• Real-time visualization for transparent monitoring

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Key Features

• Ultra-low latency streaming architecture
• Terabytes of throughput per day 
• Visualisation of Input and prediction streams that scales to this latency and throughput
• Plug-in modules for pattern detection, anomaly detection, and custom logic

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Companies want to combine in-house models with external intelligence, but lack a framework that is both secure and extensible. Running workflows outside their environment raises compliance, data governance, and IP protection concerns.

Consultants want to service companies, but don’t have the means to build out a full scale ML orchestration pipeline and lack specialised models to service customers needs

Core Benefits

• Deploy in your own cloud for maximum control and compliance
• Reuse and standardize data streams via a canonical graph
• Leverage Crunch ecosystem models securely through TEEs
• Seamlessly integrate your in-house models with external models
• Extend with custom modules via open-source, BSL-licensed code

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Key Features

• Ontology — Standardizes and reuses input streams within your cloud workflows.
• Privacy Preserving (TEEs & MPC) — Run encrypted third-party models outside or within your cloud infrastructure. We ship with TEEs and MPC to keep the models and data private.
• Model Composition — Ensemble, chain, and integrate Crunch ecosystem models with your own.
• Workflow Management — Define and automate any type of ML workflow, from simple chains to complex orchestration.
• Open Source + BSL — Extend and adapt the engine while maintaining enterprise-grade protections.