CASE STUDY
Unraveling the potential of internal R&D data
Industry
Health technology
Solution
CAS Custom Services℠
KEY RESULT
8 hrs
Time for one search before CAS
KEY RESULT
Minutes
Time for the same search after CAS
KEY RESULT
↓
Redundant experiments eliminated
The Challenge
Complexity and missed connections in scientific data
A large health-tech organization had accumulated decades of valuable research, but most of it was effectively inaccessible. Key findings were spread across disconnected legacy systems, stored in unstructured documents with no reliable way to search or retrieve them. Scientists had to already know what they were looking for, then manually sift through files until they found it.
The consequences compounded over time. As experienced scientists retired, their institutional knowledge left with them. Newly hired researchers were unaware of what had already been discovered, leading to duplicated experiments across R&D groups that consumed significant time and budget. Without a systematic way to surface and share the organization's accumulated knowledge, innovation slowed, costs rose, and project timelines stretched.
At the root of the problem was a combination of poor data organization, insufficient search capabilities, and a data model that wasn't built for scientific complexity. Valuable information sat hidden in plain sight: present in the organization's systems, but functionally useless for research, discovery, or decision-making.
The Solution
Access to the full breadth of organization's knowledge
The organization sought a better way to search, retrieve, and apply its stored research. CAS applied the same curation methodology behind CAS SciFinder® to build a custom knowledge management system tailored to the company's internal R&D data. The system enabled full-text search across the organization's document library, connected similar concepts and substances across files, and supported searches based on criteria specific to the company's research focus, such as identifying whether a substance is a natural product.
The solution went well beyond standard keyword search. Information extracted from documents was tagged with key concepts and custom criteria aligned to the company's innovation priorities, enabling researchers to locate not just documents but specific references within them. Refined lexicons, ontologies, and taxonomies allowed the system to match synonyms and even recognize entire classes of substances, capabilities that no off-the-shelf search tool could replicate.
CAS also constructed knowledge graphs from the available data, linking substances, scientific findings, and concepts across the organization's document library. These connections gave researchers visibility into work happening in other parts of the organization, creating new opportunities for collaboration. Internal data was further enriched by connecting it with custom content curated from the CAS Content Collection, helping teams identify and fill gaps in their existing knowledge base.
"Having clear visibility to what data is already available is critically important. You need to be able to harvest the available information to reduce workload and speed up processes."
— Nathan Wilmot, R&D Director, Information Research, Dow
The Outcome
Decades of research finally put to work
The impact was immediate and measurable. Before the system was implemented, a single targeted search through the organization's unstructured files could take a senior scientist up to eight hours. With the scientifically aware search tool CAS built, the same search now takes minutes. That difference translates into freed-up capacity for higher-value work and cost savings from experiments that no longer need to be repeated.
The organization now has a functioning institutional memory that doesn't retire when senior scientists do. R&D teams can build on each other's work, surface relevant prior findings earlier in the research process, and direct resources toward novel questions. This creates a faster, more connected approach to health tech innovation.
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