Optimize your tender pricing with AI-powered recommendations. Our machine learning models analyze historical contract awards and market dynamics to predict optimal bid prices. The Polaris AI Pricing Engine helps you find the sweet spot between competitive pricing and profitability.
Request a DemoOur AI analyzes the relationship between bid price and win probability. For example, for a cardiovascular generic tender:
The system plots historical award data as a scatter chart showing the relationship between price and win probability, with an ML prediction curve identifying the optimal bid point.
Our AI analyzes years of historical contract award data to understand pricing patterns and success factors across markets and product categories.
Machine learning algorithms predict win probability at different price points to find the optimal balance between competitiveness and profitability.
Get actionable pricing recommendations with clear rationale and supporting data. Every recommendation comes with the evidence behind it.
Comprehensive factors driving pricing recommendations:
Our customers see up to 44% improvement in tender win rates by pricing more competitively and strategically. AI helps you avoid both over-pricing (losing bids) and under-pricing (leaving money on the table).
Find the sweet spot between competitive pricing and healthy margins. Do not leave money on the table by bidding too low, and do not lose bids by pricing too high.
Reduce pricing analysis time from days to minutes with instant AI-powered recommendations. Clear supporting rationale and scenario analysis tools help your team decide quickly.
Make pricing decisions backed by data, not gut feel. Transparent methodology, historical validation, and confidence intervals give you full visibility into the reasoning behind every recommendation.
See how AI-powered pricing guidance can improve your win rates and profitability.
Book a DemoWhen a blockbuster drug loses patent protection, generic entry can drive prices down by 80-95% within 2-3 years. Our AI models are specifically calibrated for post-LoE pricing dynamics, analyzing historical price erosion curves across molecules and markets. We help you determine the right entry price, anticipate competitor pricing moves, and model how prices will evolve over the contract period. Whether you are first-to-file or entering a crowded market, our models optimize your pricing for each specific LoE scenario.
Biosimilar tenders have unique pricing dynamics — typically higher price floors than small-molecule generics, different evaluation criteria (often with greater weight on quality and supply security), and fewer competitors per tender. Our pricing models account for biosimilar-specific factors including originator pricing, interchangeability status, therapeutic area dynamics, and hospital pharmacist switching preferences.
Test different pricing strategies before submitting your bid. Our scenario simulation engine lets you model multiple what-if scenarios to understand the impact on win probability and expected revenue:
Pricing strategies that work in Germany may fail in Italy. Each European market has unique pricing dynamics driven by procurement structures, reference pricing systems, and competitive intensity. Our platform provides country-specific pricing benchmarks and recommendations for all major European tender markets:
We also track international reference pricing (IRP) implications, helping you understand how your bid price in one country might impact your price in another through reference pricing cascades. This is critical for molecules sold across multiple European markets.
Many pharmaceutical companies lose significant revenue through systematic over-discounting in tenders. Without data-driven pricing guidance, commercial teams often bid lower than necessary, leaving money on the table. Our AI identifies these patterns and recommends optimal prices that win tenders without sacrificing margins unnecessarily. Clients typically recover 5-15% of previously leaked revenue within the first year.