Unleash the power of enterprise GenAI to drive results

Neural Search is a new approach to retrieving information based on the application of advanced machine learning for Natural Language Understanding (NLU). Specifically, it refers to the use of deep neural nets (there’s the “‘neural”), a specific form of machine learning, to aid in information retrieval. These machine learning techniques search for results with a greater appreciation for the meaning of phrases and their context, instead of searching for the presence of specific words or word forms.

Bidirectional Encoder Representations from Transformers (BERT) is a machine learning framework for NLP developed by Google with a large transformer language model at its core. With TensorFlow BERT as a foundation, Sinequa has built and pre-trained four Deep Neural Network (DNN) models using transfer learning on custom data sets for enterprise content for its Neural Search capabilities.

Neural Search will improve the search result relevance for any use case where the intelligent search is already being used. Text that provides context – full sentences and long phrases – stands to show the most improvement. Examples of places where neural search augments employee abilities may include:

• Research and development, in areas such as drug discovery and product engineering
• Direct answers for customer service, compliance or risk management
• Employee onboarding
• Predictive analytics and chatbots/virtual assistants

Unlike any other search solution available on the market today, Sinequa’s Neural Search uses four models to improve relevance: Meaning Encoder, Passage Ranker, Query Generator, Answer Finder.