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While consumers have rapidly adopted ChatGPT and other generative AI models, business users are faced with a unique challenge:
On its own, GenAI is known for fabricating data to satisfy a prompt. These “hallucinations” result in inaccurate and unreliable outputs. When integrated with a world-class intelligent search engine, however, GenAI draws from governed data to provide trustworthy, contextualized results.
Sinequa Assistant represents more than 20 years of industry-leading innovation across natural language processing (NLP) and deep learning combined with modern GenAI capabilities.
A cutting-edge AI search experience designed to revolutionize the way your workforce discovers information, validates insights, develops new products and ideas, and delivers impact.
Leveraging multiple layers of deep learning analysis, Sinequa Assistant approaches knowledge discovery with humanlike understanding so you can count on high-resolution relevance every time.
Reliable information starts at the source. Sinequa sources answers from your governed enterprise data so you can count on contextualized analysis and trustworthy insights.
Sinequa Assistant works harder so you can work smarter. Outsource the grunt work of content summarization and fact-finding to your new virtual assistant so you can spend more time doing what you do best.
AI-powered search doesn’t replace your workforce–-it augments their contributions. Expediting manual processes and time-to-insights empowers better decision-making, faster innovation, and stronger results.
It’s like having a personal assistant who:
Leveraging 20+ years of innovation across deep learning and natural language processing, Sinequa leads the industry in fine-tuned relevance across complex data sets.
Integrate with whichever generative AI model best suits your needs–even an internal one–as long as it can connect via API.
Sinequa seamlessly connects to all your content sources so you can access the entirety of your enterprise's knowledge--across all content types--from a single search bar.
Sinequa uses the permissions of your existing systems, so employees only see what they’re authorized to see.
Sinequa’s intelligent search platform 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.
Sinequa’s enterprise search platform is designed for critical, intensive, and large-scale use. With no limits on document volume or user count, the platform is built to evolve alongside your needs.
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.
Alan Pelz-Sharpe, Founder, Deep Analysis
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:
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.