Revolutionising Tender
Bidding Strategies with AI

The company faced mounting pressure in the fiercely competitive generics market, where speed, precision, and data-driven insights define success. Yet, their tender bidding approach remained rooted in traditional practices—relying on manual data collection, fragmented market intelligence, and years of institutional knowledge. Despite their experience, they found themselves increasingly outpaced by rivals leveraging advanced digital tools to undercut prices and win critical bids.

In an effort to modernize their approach, the company experimented with Excel-based models to estimate pricing and simulate bidding scenarios. However, these models quickly revealed their limitations. The sheer complexity of tender dynamics—driven by shifting market prices, competitor behaviors, regional regulations, and fluctuating demand—made accurate forecasting an impossible task. Critical variables were either oversimplified or overlooked entirely, leaving the company vulnerable to guesswork rather than guided strategy.

With shrinking margins and missed opportunities mounting, the company’s leadership recognised that their traditional methods were no longer sustainable. The competitive intensity of the post-LoE (Loss of Exclusivity) landscape demanded more than spreadsheets and intuition—it required cutting-edge technology capable of distilling complex market signals into actionable insights. The leadership understood that driving profitability and sustaining growth would depend on adopting a more sophisticated, AI-powered approach to tender bidding.

Vamstar partnered closely with the company to implement its AI-powered Pricing Co-Pilot, beginning with a focused deployment in the Italian market—one of the most competitive and complex tender landscapes in Europe. This collaboration was more than a technology rollout; it was a deep integration of expertise, data, and innovation to drive smarter, faster tender decisions.

The project kicked off with an intensive data collection and structuring process. Vamstar worked alongside the company’s teams to aggregate and organise historical tender data, ensuring every variable—from pricing trends to competitor behaviors—was captured and analysed. Using advanced machine learning and deep learning models, Vamstar identified key patterns and factors that drive tender outcomes, from pricing elasticity to bid timing and market sentiment.

A core feature of the deployment was the introduction of A/B testing and reinforcement learning—capabilities that turned the Pricing Co-Pilot into a dynamic, self-improving system. By running real-time bidding simulations and learning from actual outcomes, the AI continuously refined its algorithms, improving both accuracy and predictive power with every tender cycle. Over time, this transformed the Pricing Co-Pilot from a forecasting tool into a sophisticated decision support system, enabling the company to bid more competitively and profitably while navigating the complexities of the generics market with confidence.

The implementation of Vamstar’s AI-powered Pricing Co-Pilot marked a transformative shift in the company’s tender strategy, delivering measurable impact and driving competitive advantage. By harnessing the power of machine learning and advanced analytics, the company achieved a remarkable $1.5 million in additional revenue, boosted tender win rates by an impressive 76%, and increased margins by 32%.

The success of this initiative quickly sparked excitement across the company’s European operations. Teams from various regions and departments, inspired by the results, expressed keen interest in adopting the technology for their own markets. The Pricing Co-Pilot not only delivered financial gains but also fostered a data-driven culture, democratizing access to advanced analytics. Team members—regardless of their technical expertise—could now leverage real-time insights to inform bidding strategies and strengthen decision-making processes.

Beyond the immediate results, this project exemplified how AI can redefine competitive strategies in the generics market. Vamstar’s Pricing Co-Pilot transformed tender bidding from a reactive process into a proactive, insight-driven discipline. By continuously analyzing historical data, learning from tender outcomes, and adjusting strategies through reinforcement learning, the solution empowered the company to bid smarter, faster, and more profitably.

This case study highlights the profound potential of AI to drive profitability and growth, even in highly competitive and price-sensitive markets. The company’s success story not only underscores the transformative impact of Vamstar’s technology but also sets the stage for broader adoption, positioning the company to fortify its market leadership and sustain its trajectory of growth and innovation.