Research & Applied AI Insights.
Practical thinking on quantitative analytics, data science, machine learning engineering, data platforms, and AI workflow design.
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Forecasting
Anomaly Detection
Graph Analytics
Document AI
How We Think About Research
Practical Rigor
- • Baselines before complexity
- • Evaluation metrics tied to business cost
- • Out-of-sample testing and monitoring plans
- • Clear assumptions and failure modes
- • Reproducible workflows where possible
Delivery Perspective
- • Architecture and adoption considered early
- • Human review for high-stakes decisions
- • Data quality and lineage treated as model inputs
- • Governance appropriate to the use case
- • Documentation built for handoff and ownership
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