Launch Intelligence Platform
An AI-enabled decision intelligence solution that connects epidemiology, market, product, and event intelligence, moving launch teams from static forecasts to dynamic, scenario-based decisions.
Challenges We Solve
Pharma launch teams manage growing complexity with fragmented data and static tools. These six pain points lead to forecasting inaccuracy, reactive decisions, and missed peak-share opportunity.
Epidemiology, market, and pipeline data live inseparate sources with no unified view.
Spreadsheet models are hard to scale, standardize, or update as conditions change.
Teams can't quickly test competitor launches, access delays, or guideline shifts.
Competitive entries, access variability, and guideline changes make forecasts volatile.
Forecasting, market research, and CI tools operate in silos, slowing every planning cycle.
Platform Modules
Six modules form an end-to-end launch intelligence platform. They are fully integrated, and each can also run as a standalone module.
Quantifies the patient journey from incidence to treatment, identifying eligible populations and key drop-offs that drive market opportunity.
Provides a validated, multi-source view of epidemiology, market size, and the treatment landscape to ground every forecast in real-world data.
Models uptake, persistence, and switching behavior, benchmarked against analogue products and clinical differentiation.
Tracks competitor launches, guidelines, LOE, and access decisions that dynamically impact forecasts.
Enables interactive what-if analysis by adjusting clinical, access, competition, and pricing drivers to see the impact on share and revenue.
Optimizes market entry using multi-factor scoring across market potential, access, competition, and readiness.
Measurable Outcomes
FAQs
Who is this solution designed for?
Global brand heads, franchise and BU heads, forecasting teams, commercial strategy and insights, competitive intelligence, market access, pricing and reimbursement, and launch excellence teams.
Does it replace our forecasting or analytics teams?
No. It augments them by standardizing data, automating scenario analysis, and supporting decisions, while teams continue to own assumptions, validation, and strategic interpretation.
How reliable are forecasts with so many assumptions?
The platform combines validated data sources, analogue benchmarking, and calibrated models. All assumptions are visible and testable, and users can run multiple scenarios to manage uncertainty.
We already have forecasting models in Excel. Why change?
Excel models are useful but static. The platform builds on them with real-time scenario simulation,cross-market standardization, and clearer visibility into key drivers.
Is it too complex for business users?
No. Intuitive dashboards and guided simulations let users focus on decisions rather than model mechanics, while advanced layers stay available for expert users.
Blogs
Similar Case Studies
Move from Static Forecasts to Launch Intelligence

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