Elsevier Introduces SciBite Chat, a transformative AI-Powered semantic search tool for Life Sciences R&D

SciBite Chat combines the strength of semantic search for accurate and traceable information retrieval with Large Language Models (LLMs) to interpret natural language questions and generate answers.

Built atop SciBite Search, has the potential to transform the search experience, providing explainable results while understanding and summarizing human language effortlessly. It offers data-first, semantic analytics software for those who want to innovate and get more from their data. Its semantic infrastructure answers business-critical questions in real-time by releasing the value and full potential of unstructured data.

It uses ontology-backed semantics with Retrieval Augmented Generation (RAG) architecture to improve search results. Unlike conventional search tools, SciBite Chat’s natural language query and iterative chat features allow users to have a conversation with their data. Trust and traceability are at the core of the user experience, with the search results showing the verbatim evidence, as well as the underlying SciBite Search query language used to identify the relevant source documents. These features ensure that search results are both explainable and reproducible, which are essential factors for life sciences research.

SciBite Chat is built on three pillars of data-driven insights—accuracy, transparency, and flexibility. Accuracy is enhanced by SciBite's deep expertise in life sciences, while transparency is guaranteed through human explainability incorporated into every step of the user journey. Flexibility in incorporating internal terminologies, data ingestion, and deployment options ensures that organizations can easily integrate SciBite Chat into their work/workflow.

Read the full press release here .