The evolution of enterprise search relevance
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.
Sinequa Neural Search - the next generation of search relevance
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.
The benefits of Sinequa Neural Search include:
- The best relevance, four state-of-the-art deep learning models and hybrid search results retrieval
- Easy to deploy, no models to train
- Fast and efficient, optimized for performance using most advanced cloud GPU processing technology
"Sinequa significantly differentiates itself through its use of deep learning (artificial neural networks), and how it uniquely applies multiple deep learning models to provide more accurate search results,"