Pragma Group builds econometric and AI-driven commercial intelligence systems for pharmaceutical organizations — pricing, demand forecasting, shortage detection, and AI governance. Built from production deployments, not academic templates.
Four integrated disciplines built from production deployments in global pharmaceutical markets — not from academic templates.
Hospital and institutional elasticity modeling. Nonlinear demand response estimation. Revenue-maximizing price band design with cross-market reference pricing constraints. Confidence intervals, not point estimates.
Hybrid econometric and ML systems that combine causal structure with pattern detection. Calibration tracking via Brier scores. Automated alert thresholds and human-in-the-loop recalibration. Forecasts that degrade gracefully.
Automated competitor shortage detection across FDA and EMA databases. Revenue opportunity scoring by therapeutic overlap, geographic coverage, and production capacity. Elasticity-based substitution modeling for capture rate estimation.
Override accountability frameworks. Calibration monitoring systems. Decision audit trails aligned with EU AI Act requirements. Institutional AI governance design that makes commercial AI systems auditable, not just functional.
Pfizer US was processing $4.8B in annual orders with $1.2B unfulfilled — and paying $30M in contractual fines. The root cause: a demand forecasting architecture that systematically underpredicted demand surges. Eduardo's model reduced forecast error from 44.36% to 36.30%, cutting forecast-attributed backorders from $128M to $51M. Incremental recovery: $77M in a single 12-month deployment.
Custom boosting ensemble · bootstrapped aggregation · custom loss function · shelf-life constraints · 8-week deployment to production.
Engagements start with a focused diagnostic on real data. No multi-month discovery phases. Measurable economic signal within weeks. No vendor lock-in.
Signal validation on real portfolio data. Use-case prioritization. Risk mapping. Readiness assessment. Proof of economic value before broader commitment.
Live model on real product portfolio. Accuracy benchmarks. Revenue impact estimate. Documented learnings. Feasibility report with go/no-go recommendation.
Multi-geography deployment. Automated pipelines. Executive dashboards. Team training. Practices embedded in operational workflow, not just in a consultant's notebook.
Full audit trail. Override tracking. Calibration monitoring. EU AI Act compliance documentation. Institutional AI culture, not perpetual dependency on external advisors.
All engagements include knowledge transfer and open architectures. Actionable roadmaps that work with your existing stack. You own the IP. The goal is institutional capability — not recurring advisory dependency.
Online and in-company programmes built from production deployments. Methodology-first. Designed for pricing, forecasting, and commercial excellence teams in pharmaceutical organizations.
Hospital and institutional elasticity modeling. Cross-price estimation. Revenue band design. The quantitative toolkit for pricing professionals in regulated markets.
Hybrid econometric and ML forecasting systems. Calibration tracking. Scenario simulation. From ARIMA foundations to ensemble methods and uncertainty quantification.
Systematic monitoring frameworks. Revenue opportunity scoring. Substitution modeling. Turning competitor supply disruption into quantified commercial opportunity.
Override accountability. Calibration monitoring. EU AI Act compliance for high-risk commercial AI systems. For the leaders who sign off on algorithmic decisions.
Data-driven HR decision systems. Predictive attrition modeling. Algorithmic hiring and adverse impact analysis. For HR and commercial excellence leaders.
Strategic overview for C-suite and VP-level professionals. What AI can and cannot do in commercial pharma. Governance obligations. How to commission and evaluate technical work.
Kairós is a sovereign European AI governance platform — a compliance infrastructure that sits between your organization and the AI systems you operate. For pharmaceutical companies deploying AI in pricing, forecasting, and HR decisions, EU AI Act high-risk obligations apply from August 2026.
Eduardo Valencia has spent over fifteen years at the intersection of quantitative economics, machine learning, and pharmaceutical commercial intelligence. His work spans demand forecasting, pricing architecture, shortage detection, and AI governance — across markets in Europe, North America, and Asia-Pacific.
At Pfizer US, his forecasting model reduced backorder-attributed losses by $77M in a single deployment. His Master's research at UNED Applied Economics applied game theory and ML to predict bid prices in Denmark's generic drug procurement auctions — a system architecture covering data crawling, preprocessing, model training, and live validation.
He is co-founder and Chief AI Officer of Kairós, a sovereign European AI governance platform. Author of the Thinking AI Series — eight volumes on AI strategy, governance, and accountability. His full profile and publications are at eduardovalencia.com.
Engagements begin with a focused viability review on real data. If you're exploring advisory, in-company training, or course access, use this form.
Advisory engagements, in-company programmes, and course licensing for pharmaceutical commercial teams. All enquiries handled directly by Eduardo Valencia.
eduardo@pragmagroup.ai LinkedIn · Eduardo Valencia eduardovalencia.comLos programas de Pragma Group están en inglés y orientados a equipos comerciales farmacéuticos globales. Para formación ejecutiva en IA en español — Shadow AI, People Analytics, EU AI Act, prompting avanzado — visita Merickson IA, la plataforma de referencia para profesionales hispanohablantes.
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