Elsevier's major segment of customers are drug companies across the world, and Drug discovery is a complex process. The cost to develop one new drug is $2.6 Billion, and the approval rate for drugs entering clinical development is less than 12%. The attrition rate for drug candidates that is, the number of candidates you start with for each successful launch can be in the order of 10,000:1.

Scientists rely on knowledgebases related to pharmacology, medicine, chemistry, and biology as well as experimental data like clinical trials, experimental publications, tests performed on similar candidates etc. Some of these are purchased, while some are developed over a period within the company. Scientists spend an incredible amount of their expensive time searching through these knowledge bases. Take for example a simple question "What are the compounds that are similar in structure to benzene, have a boiling point of more than 40 Degrees F, and have no side effects on people with lymphoma". The question requires joining information from chemistry, medicine, and pharmacology. By "Joining," we mean understanding the question as if it's human and bringing information from different domains and joining them to provide a definitive answer.

The customer envisioned "A platform" that can join knowledge from different domains to make it searchable, and the search engine reacts as if its a human by understanding the question, parsing it into a machine-readable query and crawling through the databases, and bringing results along with the accuracy at which the answer is likely to answer customer's questions. That platform is ELSSIE; that's what Knoldus built it for Elseiver.

ELSSIE is a platform that connects information from multiple sources stored in the format of a knowledge graph and maintained by Elsevier's Subject Matter Experts (SMEs). ELSSIE enables users to find and analyze content across traditional data silos to derive new value-driven insights.