Selected work
A sample of engagements across industries — each one following the same arc: a measurable problem, a validated model, a deployed solution.
Challenge: Thousands of shipping documents processed by hand each month — slow, expensive, error-prone.
Approach: An OCR + NLP pipeline that extracts, classifies, and validates document fields, with human review only on low-confidence cases.
[Placeholder — real metric here, e.g. "90% of manual entry eliminated"]
Challenge: Inventory decisions driven by intuition, producing both stockouts and overstock.
Approach: Time-series forecasting models trained on sales history, seasonality, and promotions — validated out-of-sample before a single order changed.
[Placeholder — real metric here, e.g. "Forecast error reduced by X%"]
Challenge: Separating genuine predictive signal from noise in high-frequency market data.
Approach: Bayesian classification and regime detection models, stress-tested with out-of-sample validation and honest significance testing.
[Placeholder — real metric here]
The pattern behind the projects
We only take on work where success can be defined as a number before we start.
Out-of-sample testing, significance checks, and honest reporting — including negative results.
Integration into your real workflow, with documentation and handover your team can own.
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