Predictive Model Training & Optimization

Optimize lending rates, collateral factors, and liquidation thresholds with models that dynamically respond to market volatility. Increase protocol TVL utilization by 15-40% while maintaining robust risk parameters.

Deploy predictive AMM curve models that anticipate price movements, adjusting liquidity provision strategies to minimize IL for LPs by up to 30% compared to static models, attracting and retaining high-value liquidity.

Implement ML-driven liquidation engines that predict account health deterioration, enabling proactive actions. Reduce bad debt by over 25% and improve capital preservation during black swan events.

Leverage our pre-built model frameworks and on-chain deployment pipelines. Go from concept to mainnet in 4-6 weeks, not months, with production-ready, auditable model code.

Models optimized for fee generation and user retention directly boost the bottom line. Achieve a 10-25% increase in sustainable protocol fee revenue through smarter economic mechanisms.

Gain an edge with proprietary on-chain and off-chain data pipelines feeding your models. We build and maintain the data infrastructure that powers more accurate predictions than public alternatives.

We analyze your on-chain and off-chain data sources to define the predictive problem, select the optimal ML architecture, and establish success metrics. This phase delivers a detailed technical specification and project roadmap.

Client Value: Eliminates scope creep and ensures the model solves the right business problem from day one.

We build robust data pipelines to extract, clean, and engineer features from raw blockchain data (e.g., transaction graphs, wallet behaviors, protocol interactions). This includes creating a versioned, reproducible training environment.

Client Value: Creates a clean, reliable data foundation that directly impacts model accuracy and long-term maintainability.

We train models using frameworks like PyTorch or TensorFlow, employing hyperparameter optimization and cross-validation techniques specific to temporal on-chain data. Models are rigorously validated against out-of-sample and adversarial scenarios.

Client Value: Delivers a high-performance model optimized for Web3's volatile environment, not just generic accuracy.

We deploy the model via scalable APIs (e.g., FastAPI) or on-chain verifiable inference, implement real-time performance monitoring dashboards, and establish a retraining schedule. This phase includes full documentation and handover.

Client Value: Transitions the model from a prototype to a reliable, monitored production system with a clear path for continuous improvement.