Today, individual employees still spend at least 400 hours each year searching for information, resulting in millions of dollars in operational costs and lost business opportunities. While enterprise search engines have reduced that effort with the advent of NLP technologies applied to the text found in documents, we are still in the early stages of AI-driven information discovery. The next stage of this evolution will come from applying deep learning models to enable machines to understand language intent and present more relevant information to employees, known as Natural Language Understanding (NLU). With NLU, the shift from employees finding information to information finding employees will be accelerated, further unlocking productivity and innovation.
Natural language processing is the science behind machine comprehension. If you’re new to the concept or looking for an overview of what it is and how it’s used, then this guide is for you.
Sinequa’s Neural Search provides the most sophisticated engine for discovering enterprise information assets available on the market today.
By combining state-of-the-art deep learning language models with the best NLP and statistical techniques, employees and customers spend less time searching for information and more time developing insights to drive decisions and solutions.
Sinequa’s Neural Search improves the enterprise search experience with the following key features:
Sinequa’s Neural Search augments the performance of the existing best-in-class statistical search so that you can expect better relevance for any existing use case. Neural Search also has the potential to perform well in situations where statistical search hasn’t been used for lack of matching words.
Neural Search shows the most relevant passages for greater contextual insights rather than just matching words.
Using deep learning, Neural Search extracts the most relevant answers from existing content, shortening the time-to-insights.
Neural Search comes ready to go out-of-the-box. All four models are pre-trained to perform well on enterprise content and do not require training on your dataset, now or in the future. Our models incorporate years of research and knowledge and are optimized for the best relevance to enterprise content. Harness the power of natural language AI without the need to build and maintain training datasets, or curate content.
Normally language models are big and unwieldy...which means costly infrastructure or expensive hosting bills. Sinequa’s Neural Search has been designed for performance from the very beginning to scale to enterprise levels and for cost-effective deployment on large datasets, including millions of documents.