Challenge: A chemical manufacturer needed to fine tune the settings of the pipeline for each new material batch, with optimization taking 3 days while new batches arrived every 4-5 days.

Key Insights:
- An excellent simulation existed but was underutilized due to 30-120 minute run times
- Manual safety checks added a half-day delay
- Process experts were operating on intuition rather than systematic optimization

‍Implementation:
Developed an AI-powered simulation replica that ran in <0.1 seconds with 98% accuracy
Systematized 100+ safety checks into an automated verification system that experts could quickly approve
Created optimization algorithms that consistently outperformed manual tuning

Results:
€10M Annual Savings per plant through optimized production
‍99% Reduction in parameter optimization time (3 days → 20 minutes)
‍Zero Safety Incidents during the transition to AI-assisted parameter settings                    

Challenge: A specialty glass manufacturer had tested 340 formulations over 18 months with minimal progress. Their best formula achieved only 5 out of 12 mandatory properties.
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Key Insights:
- Risk aversion had led to incremental experimentation that would never reach the required properties
- 20% of experiments were duplicates or near-duplicates.
- Scientists spent 10+ hours weekly on experiment design rather than execution.
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‍Implementation:
- Created a novel visualization tool revealing that alternative substrate paths had higher potential
- Implemented active learning algorithms to systematically explore the parameter space
- Automated experiment tracking to eliminate duplication
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Results:
Market-Ready Formula in 90 Days: Achieved all 12 mandatory properties
80% Reduction in experiment design time
Transformed R&D Strategy toward more ambitious material exploration

Challenge: A multinational polymer manufacturer needed to develop a fire-resistant polymer blend, with 30,000 potential candidates but capacity for only 3 experiments weekly.

Key Insights: While direct property prediction wasn't possible with available data, auxiliary correlations could be leveragedThe lead scientist spent 10+ hours weekly formatting data and running basic analysisPrevious approach focused on a promising but fundamentally limited chemical family

Implementation:
Developed multi-property prediction models using auxiliary data, improving accuracy from 10% to 85-95%
Automated data formatting and analysis, reducing scientist workload by 95% (5 hours per week -> 10 minutes)
Created visualization tools to compare candidate pathways systematically

Results:
2-Year Development Time Savings by redirecting efforts away from a fundamentally limited approach
4X Higher Predictive Accuracy for critical material properties
25% More Experiments Run through increased scientist productivity