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Gartner® How to calculate business value and cost for generative AI use cases
Explore expert insights, articles, and thought leadership on scientific data challenges.
Discover our whitepapers, spec sheets, and webinars for in-depth product knowledge.
Explore SciBite’s full suite of solutions to unlock the potential of your data.
Explore SciBite’s full suite of solutions to unlock the potential of your data.
Discover how SciBite’s powerful solutions are supporting scientists and researchers.
Gartner® How to calculate business value and cost for generative AI use cases
Explore expert insights, articles, and thought leadership on scientific data challenges.
Discover our whitepapers, spec sheets, and webinars for in-depth product knowledge.
Explore SciBite’s full suite of solutions to unlock the potential of your data.
SciBite / Knowledge Hub / Resources / RAG-based approaches for life science applications [Webinar]
Hear from Sinequa’s Chief Evangelist and Strategist, Jeff Evernham, VP of Customer Solutions, Sommy Boucansaud alongside SciBite’s Director of Alliances, Sam Shelton as they share how to implement AI in a pragmatic, secure and transparent way for life sciences companies.
The heterogeneity, complexity and fast moving evolution of terminology in the life sciences presents particular challenges. As companies scramble to implement AI, vector based search is an obvious entry point, allowing users to support semantic search without the need for experts to curate ontologies. This allows users to get up and running faster and to evaluate RAG based approaches for LLM implementation.
What is becoming evident, is that using vector-based search for RAG architecture has limitations, particularly in the life sciences and where accuracy, transparency and explainability are important.
In the life sciences particularly, vector search can struggle to distinguish concepts that are very close in vector space such as synonyms and this is where an ontology, curated by subject matter experts really improves search and analytics.
During this webinar, we outline this hybrid approach of combining vector search (Neural Search) with ontology based semantic search and how this provides the best of both approaches.
Sam leads partnerships and alliances at SciBite, working collaboratively with existing partners and developing new partnerships aligned to SciBite’s strategic goals. He has a strong technical background in the life sciences, with a PhD in Protein Biochemistry from the University of Nottingham and post-doctoral training in bioinformatics within the department of Neurosurgery at the University of California San Francisco.
Prior to Joining SciBite he held technical sales and commercial roles at Carl Zeiss and most recently led business development at Repositive, building relationships with contract research organisations, biotech’s and pharma companies, facilitating data exchange and search across multiomic datasets. He has a good grasp of the challenges of dealing with unstructured scientific data, and collaboratively developing practical solutions to overcome these.
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