Methodology
Every system I build — whether it's tracking a commodity market, validating a dataset, or monitoring a geospatial hazard — follows the same underlying pipeline. The domain changes; the methodology doesn't.
Ingestion pipelines normalize and validate structured data — transaction records, exchange feeds, price charts, weather forecasts — alongside unstructured sources such as news articles, filings, and imagery. Formats, timezones, and units are standardized at this stage, giving every downstream process a single, consistent schema to work from.
Statistical and rule-based checks run against the normalized data, screening for outliers, missing values, threshold breaches, and pattern deviations. This layer isolates the data that carries genuine signal, separating material events from routine noise before deeper analysis begins.
Where the domain supports it, purpose-built models — Isolation Forest for anomaly detection, time-series and classification models for forecasting — are trained on historical patterns to flag emerging risks proactively, rather than surfacing issues only after they have already occurred.
Every finding is scored against domain-specific criteria — failure rate, statistical significance, market impact, or conviction — and ranked accordingly, so the most consequential issues surface first instead of sitting buried in an undifferentiated list.
A large language model layer synthesizes the ranked evidence into a structured narrative, translating technical findings into clear, decision-ready language written for the stakeholder who needs to act on it, not the analyst who produced it.
Findings are delivered through an interactive dashboard and a downloadable executive report, giving stakeholders both the ability to explore the underlying data directly and a concise summary they can act on immediately.
Applied
Runs the full pipeline end to end on live silver market data: ingesting price, macro and news data, ranking evidence by conviction, and generating a daily AI-written morning brief.
Applies the same pipeline to data quality: statistical and ML checks detect issues across large datasets, ranked by failure rate, then explained in plain language via an LLM reporting layer.
Extends the pipeline to geospatial data: automated collection of weather and hazard data, anomaly and risk detection, and interactive visualisation of operational risk.
Coming Soon