One stack. From raw data to live signal.

Petabyte-scale ingestion, normalization, and feature pipelines across structured market data and unstructured global signals.

• Tick-level market events and limit order book microstructure.
• Decades of regulatory filings, earnings call transcripts, real-time news wires.
• Alternative datasets: supply chain logs, geospatial imagery, sentiment streams.
• Feature stores designed for low-latency online serving and offline pretraining.

Distributed pretraining and fine-tuning infrastructure for proprietary financial foundation models.

• Multi-billion-parameter time-series transformers.
• Sequence models trained directly on limit order book messages.
• Multi-dimensional encoders bridging structured and unstructured modalities.
• Continuous training across cloud-native GPU clusters scaled across regions.

GPU-accelerated backtesting and synthetic-market generation for stress testing.

• 1,000+ simulations per week across decades of historical regimes.
• Counterfactual scenario generation for regime-shift robustness.
• High-fidelity execution and latency modeling.

Ultra-low-latency model serving for live signal generation and execution.

• Continuous out-of-sample evaluation against live markets.
• Model-drift detection and automated rollback policies.
• Co-located serving for venue-aware execution paths.

An internal fleet of AI agents that compress research cycles from months to days.

• Hypothesis generation and feature engineering automation.
• Code authoring for data pipelines and experiment scaffolding.
• Operational task automation across the research org.