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Expert.ai Announces New Features to Hybrid Natural Language Platform

7 February 2023

New features include expanded on-premise deployment options and enhanced taxonomy management via 3rd-party knowledge sources and integration of standardized libraries.

Expert.ai (EXAI:IM), a leading company in artificial intelligence (AI) for language understanding and language operations, announces today the release of new features for its Natural Language (NL) platform enhancing purpose-built natural language processing (NLP) workflow support. By employing a hybrid approach that combines state of the art in NL techniques – including machine learning and knowledge-based, symbolic AI – the expert.ai Platform gets the most out of unstructured data, like text in documents, applications and tools, to enable organizations create new business models, accelerate time to value and optimize processes.

Natural language text understanding has advanced to among the most widely embedded AI capabilities within organizations.  NLP is no longer considered experimental, but a crucial technology for creating tangible ROI and achieving competitive advantage, and hybrid NL is becoming the de-facto approach to optimize results. According to Forrester[1], “Hybrid AI delivers the best results for NLP Applications… Human knowledge remains essential for many use cases, including the effective use of natural language processing (NLP).” Expert.ai’s hybrid NL Platform analyzes and understands unstructured language data in vertical domains to accelerate business impact. Our experience with hundreds of clients across the insurance, financial services, life science/pharmaceutical, healthcare and publishing and media verticals use NL to create competitive advantage via undervalued language data within their enterprises.

“Organizations increasingly recognize the value of hybrid natural language as they can get to market faster, supporting a broader range of use cases with improved efficiencies and enhanced accuracy,” said Luca Scagliarini, Chief Product Officer at expert.ai.  “Practical and useful AI is no longer about the future, it provides enterprises value now. We continue to expand the platform capabilities to enable our customers and partners to create new processes, capabilities, solutions and offers that grow their businesses and impact their bottom lines”.

Expert.ai Platform Winter 2023 new feature highlights:

Enhanced on-premise deployment options and taxonomy management are among the major updates.

  • Deployment (on premise installation): With the expert.ai platform, organizations can accelerate their AI initiatives while fully managing the performance, security and scalability of their data and infrastructure. The new release also enables the use of Kubernetes (K8s) to store core data on-premise, implement specific security measures or comply with specific regulatory requirements while accessing the latest updates to remain current.
  • Taxonomy: The new platform release offers the possibility of adding 3rd-party external knowledge sources to deliver NL applications to production faster with higher levels of business accuracy. Third-party knowledge sources include Unified Medical Language System (UMLS) like MeSH, ICD9 and ICD10 and specific resources like the ones provided by WAND Inc., the premier source for industry vertical taxonomies, business taxonomies and specialty domain-specific taxonomies.
  • Wand Integration expands expert.ai’s out of the box access to new industry and process taxonomies. Shawna Applequist, Marketing Director, WAND, Inc., commented: “WAND’s curated taxonomies can increase the speed of metadata model development by up to 90% by informing the AI engines with a metadata model that can be imported with just a few clicks.”

Additional features include:

  • APIs: Developers can now interact with expert.ai APIs using visual documentation, making it easy for back-end implementation and client-side consumption. Development teams can now visualize and interact with the API resources using a familiar Swagger interface.
  • Navigation of Knowledge Graphs (KGs): Resulting in customized navigation of knowledge models to quickly identify the strength of related concepts and connections.

[1] Forrester Best Practice Report, Hybrid AI Delivers Best Results For NLP Applications