Work From
The Field.

What we did

Engineered a physics-informed synthetic MRI volume library to address severe scarcity in confirmed training cases for a rare oncological condition.

How

Applied parametric copula modeling to capture inter-slice spatial correlations. Differential privacy budget (ε = 1.2) enforced throughout generation. Wasserstein distance validated below 0.04 before sign-off.

Result

Training corpus scaled from 87 confirmed cases to 50,000 synthetic volumes, enabling the first statistically valid model training run.

What we did

Annotated high-density multispectral drone imagery across 14 distinct crop stress classes for a precision agriculture platform.

How

Built a domain ontology covering stress type, severity stage, and spectral band signature. Model-assisted pre-labeling reduced manual load by 58%. Fleiss' Kappa target ≥ 0.91 required before batch sign-off.

Result

800,000 multispectral frames delivered at a final inter-annotator Kappa of 0.93.

What we did

Operationalized OCR-based layout-analysis extraction and structured labeling across multi-format clinical trial data collection sheets.

How

Deployed a consensus verification protocol with triple-review on ambiguous table regions. Statistical QA gate: minimum F1 ≥ 0.93 and Cohen's Kappa ≥ 0.90 required per category before delivery.

Result

2.1 million pages processed across 47 distinct data point categories. Zero pages required re-extraction post-delivery.

What we did

Designed and integrated a custom predictive ensemble model into the client's on-premise cloud infrastructure for multi-horizon demand forecasting.

How

Structured discovery mapped existing data pipelines, identified distribution shift patterns, and defined forecast horizons. Enterprise-grade model explainability layer added to satisfy internal procurement audit requirements.

Result

40% reduction in manual planning overhead in the first quarter. 680% ROI demonstrated at the 36-month projection horizon.