AI strategies for R&D require a different kind of partner
Developing effective AI for scientific R&D involves more than just technical expertise. Purpose-built AI solutions require deep subject matter knowledge, advanced technological understanding of algorithms and foundational models, and curated and structured scientific data. With this foundation, your solutions can deliver not just faster predictions, but more accurate, reliable outcomes.
Why AI in science needs better data
67% of R&D leaders are dissatisfied with the speed of AI implementation in their organizations. One of the most common barriers is unreliable or unstructured data.
Scientific organizations face unique challenges when building AI models. Inconsistent inputs can lead to inaccurate predictions, flawed outputs, and wasted investments.
Science-smart AI addresses these risks by integrating high-quality, validated content and technical expertise that reflects the rigor of scientific research.
"It is very challenging for humans to collect accurate data from a vast body of literature and transform it into a structured, AI-ready format… CAS did a great job supporting us."
How CAS powers science-smart AI strategies that deliver
CAS offers more than a century of scientific curation expertise to help you avoid the risks that come with unstructured or unreliable data.
Our expertly curated, domain-specific content and technical expertise in building and enabling AI models power reliable, high-impact results whether you are training a model, integrating new data sources, or developing new agentic workflows, or building a Retrieval-Augmented Generation (RAG) system.
Accelerate discovery: Enable faster, more accurate predictions with clean, structured data.
Reduce risk: Prevent hallucinations with validated, domain-specific training sets.
Build confidence in your AI strategy: Empower your teams with insights they can trust.
Don’t just adopt AI; make it a trusted engine for innovation.
Train smarter, scale faster
The success of any AI system depends on how well trained it is. Poor-quality data leads to underperforming models, wasted resources, and lost trust.
We help you avoid those risks with expertly curated training datasets and custom curation services tailored to your scientific goals.
- Training datasets. Large-scale, clean, domain-specific datasets to help you train models that perform.
- Custom curation. Structuring and preparing your internal data to ensure it is AI-ready and fully leveraged.
- Ongoing support. API delivery and continuous updates keep your systems current, evolving with your needs.
Whether you are building a model from scratch or refining an existing one, we ensure your input data is accurate, consistent, and ready to deliver results.
Enablement across industries
AI is not one-size-fits-all. CAS delivers tailored solutions that align with your industry’s unique data landscape and innovation goals.
- Pharmaceuticals: Accelerate drug discovery, repurposing, manufacturing, and regulatory compliance.
- Biotechnology: Improve predictive modeling and streamline research workflows.
- Chemicals: Enhance formulation development and process optimization.
We also support agriculture, cosmetics, government entities, and other sectors, helping organizations drive innovation, improve sustainability, and modernize their data infrastructure.
Across industries, the pressure to innovate faster, reduce risk, and make smarter decisions is growing.
We help you meet these demands with AI-ready data, continuous delivery, and expert support that keeps your systems evolving.
Case study: CAS delivers the data foundation for predictive R&D success
Challenge: A diversified chemical company faced barriers to innovation as it lacked accessible domain knowledge and high-quality, property-specific data to support machine learning in R&D.
Solution: CAS provided custom-curated datasets from scientific literature and patents, enabling accurate AI predictions and accelerating the development of novel electronic devices.
How we help
Why is high-quality data important?
What makes CAS data different?
How does CAS help reduce selection bias in data-driven systems?
Can CAS help digitize and organize legacy data?
What is Retrieval-Augmented Generation (RAG)?
How does CAS support AI training and development?
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