Biomanufacturing

Palantir Foundry's Biomanufacturing solution is designed for global pharmaceutical manufacturers, emerging biotechs, and clinical manufacturing operations seeking to overcome the challenges of operational and data silos. By creating a connected, comprehensive data foundation, Foundry enables organisations to leverage automated alerting and predictive AI to monitor operational efficiency, optimise yield, and predict deviations across the entire manufacturing lifecycle.

Trusted by top global pharma companies, emerging biotechs, and the FDA, Palantir Foundry integrates legacy and greenfield manufacturing systems into an agile platform capable of supporting the rapid development of novel therapeutic modalities at scale.

Core Capabilities and Accelerated Impact

• Reduce Process Variability: Quality results are made available in near real time as assays are completed — rather than on a quarterly basis — enabling better-informed vendor choices and reducing variability in production yield.
• Rapidly Address Deviations: Deviation investigations are informed by edge data integrated from supply chain and manufacturing systems, resulting in more efficient root cause identification and a reduction in discarded lots.
• End-to-End Process Optimisation: Demand models for cell products and intermediates, alongside supply projections of key raw inputs, enable resource allocation optimisation to meet tight deadlines for clinical trial enrollment.

Platform Features and Technical Capabilities

• Granular digital batch records enabling traceability from raw materials through intermediates and final inventories.
• IoT edge model integration through on-site sensors and environmental monitoring for yield optimisation.
• Out-of-the-box templated modules for efficient configuration and scaling of new manufacturing programs.
• Robust security tools that facilitate collaboration across the organisation as well as with outside parties such as Contract Manufacturing Organisations (CMOs).
• Shared analysis workbooks for collaborative investigation of production deviations.
• Machine learning models capable of spotting deviations earlier and providing enhanced projections of realised yield to inform resource allocation.
• Threshold-based and ML-model-based alerts to ensure troubling batch patterns are caught with sufficient time to intervene.
• Scenario analysis tools allowing supply chain teams to experiment with adjusting raw materials and resource allocation to limit the impact of disruptions.

Case Study: Optimising Batch Production Processes

• Partner: A Top 5 Global Pharma client.
• Challenge: Reduced processed yields in an important product production line. Sensors on each component generated valuable data, but process experts struggled to utilise it at scale. Batch quality data was stored in a separate data warehouse, making combined analysis with process condition data difficult.
• Impact: Seamless integration of batch data and sensor data enabled process experts to correlate historical process conditions, leading to 12% higher yields and $100Ms of additional revenue potential. Process experts were able to test hypotheses through decision simulations in Foundry before making changes to production. Daily monitoring of batch quality enabled near real-time detection of new deviations and provided broader visibility into the process landscape.

Case Study: Yield Optimisation

• Partner: A pharma client.
• Challenge: High variability of biologic product yield for key clinical programs. Manual batch investigations were time-consuming and often inconclusive. Teams had limited ability for collaborative analysis, no mechanism for confirmatory experiments, and unclear visibility into the clinical impact of yield metrics.
• Impact: Historical manufacturing yields were matched with raw material metadata, enabling automated analysis and continued monitoring of unexpected relationships between input materials and production quality. Machine learning models were applied to spot deviations earlier and provide enhanced yield projections. Collaboration was improved through automatic integration of assay results from both in-house teams and CMO partners, creating an end-to-end record of all batches.

Case Study: Resilient Supply Chain

• Partner: A client developing novel cell therapies.
• Challenge: The need for a supply chain that was both flexible and highly controlled to account for increasingly frequent disruptions — including raw material shortages, vendor backlogs, and other shocks — without compromising quality. Teams operated in siloed systems with little visibility into the downstream impact of system changes.
• Impact: Integration of disparate ERP inventory systems, IoT environmental sensors, and CMO assays (in-process and release) resulted in a single source of truth for the progression of a cell product. Automated alerts ensured troubling batch patterns were caught in time to intervene, with supply projections automatically adjusting as conditions evolved. Scenario analysis tools enabled supply chain teams to adapt to changes in clinical demand and limit the impact of disruptions.

Palantir Foundry is field-tested software trusted across the life sciences industry, supporting both large global pharmaceutical manufacturers and emerging biotechs. The platform is designed to integrate with existing manufacturing infrastructure and external partners such as CMOs, providing a secure, scalable, and collaborative environment for clinical manufacturing operations.