Causal AI for Chemical & Pharmaceuticals
Building a sustainable future
The chemical and pharmaceutical industry stand to gain significant advantages by leveraging Causal AI. This novel approach enables businesses in these sectors to make more accurate predictions, optimize processes, and accelerate innovation.
In drug discovery, causal AI can identify causal factors driving complex biological processes, enabling faster identification of potential treatments. In chemical manufacturing, it can optimize production processes by uncovering the root causes of inefficiencies or defects, reducing waste, and improving yield.
By harnessing causal AI, these industries can gain a competitive edge, drive innovation, and achieve more efficient and cost-effective operations.
Golden Batch
Quality Optimization
Data Governance
Mapping & Data Quality
Work place safety
Asset performance monitoring
Product master
R&D
Golden batch quality optimization
A Fortune 500 Biologics major implemented a Causal AI based root cause analysis system to optimize batch variations in bio-reactor’s yield, quality, and to minimize batch rejections using TRAIN.
The AI agent utilizes causal knowledge to continuously monitor batches against the “golden batch” profile. It provides guidance on input feed rate and specific process adjustments referencing the “Golden Batch”.
This implementation resulted in minimizing variations in batch cycle time and replicating the golden batch to enhance product quality. The easy to simulate process twin helps in understanding the complex interactions within production processes allowing for real-time simulation and analysis.
Total economic benefit
Faster
Root cause analysis
Reduced
Batch quality variations
Faster
Scenario simulations
Enterprise Data Governance
A Fortune 500 chemical major implemented a Causal AI based metadata discovery, mapping, classification and grouping solution to expand the coverage of enterprise data governance.
The traditional manual approach required data stewards spending 1000’s of hours interviewing the data users to understand the context and usage of the data. This process is extremely time confusing and requires intricate knowledge of the domain.
The AI agent automates metadata discovery, data definition enrichment, mapping and classification based on learnt causal models.
Total economic benefit
Improved
Data definitions & quality
Faster
Mapping & Classification
Improved
Lineage
Contact us to schedule a demo
Learn more about how Causal AI can empower your teams to
- Minimize golden batch quality variations.
- Harmonize technical and business metadata to build a business glossary to improve data quality.
