AI and machine learning.
Production machine-learning systems built and operated for the MyAllies platform, available to client engagements.
Areas of work
Signal models
Tabular models (LightGBM, XGBoost) for predictive signals across equities, options, and macro factors. Walk-forward validated.
Conviction and ranking
Cross-sectional and time-series conviction scores, operated in production.
Anomaly detection
Detection of pricing errors, missing data, corporate-action issues, and suspicious flow.
LLM workflows
Earnings-call summarisation, filing extraction, news classification, entity resolution. Processing throughput exceeds 40,000 documents per day.
Vision and document AI
Document extraction, image-based filings, chart interpretation using open-source vision models adapted to financial workflows.
Feature engineering
Domain-specific features — earnings surprise, insider clustering, supply-chain shocks, options skew — engineered for financial use cases.
Operating philosophy
Every model in production is instrumented for drift, error calibration, and economic impact. Kill switches are mandatory before client-facing deployment.
Where a problem lacks adequate data, a clean target, or sufficient economic signal, we will say so.
Open-source and frontier models are selected per workload according to cost, latency, and accuracy requirements.
ML engagement enquiry.
Describe the model or workflow under consideration. We will respond with feasibility and scope.
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