Project description
Computer-aided support for customers
It’s important for customer-centric companies to provide top-class customer service. This is not always easy. Being able to immediately respond to every email and message from a customer is crucial but impossible. Contact centres of customer-facing companies are overwhelmed by messages from customers. While the volume of incoming communication keeps increasing, so does customers’ expectation for a high-quality and fast response and resolution. In this context, the EU-funded SentiSquareCX proposes an AI-based, self-learning solution for any text. It is applicable to any language without limitations and even in multi-language datasets. The unique qualitative advantage of SentiSquareCX’s engine stems from research in Distributional Semantics. The project’s objective is to scale up and deliver the product to new markets and industries.
Objective
Understanding customer voice is fundamental in building products, services and customer facing processes. Although companies have abundance of data, they still lack insight. According to Gartner, fewer than 10% of companies have a 360° customer view, and only 5% are able to use it to systemically grow their businesses. The reason is that listening to customers has been difficult and technologies available either arduous to use, non-scalable or providing skewed interpretation of the data collected. To meet the needs of data intensive industries and deliver cost-effective NLP, we offer our AI-based, self-learning solution for any text. It is language agnostic, applicable thus to any language without limitations and even in multi-language datasets. Compared to alternatives, where search content is predefined (pre-tagged), our disruptive technology genuinely reflects the actual meaning of text, not being limited by lexicons or biased by content predefinition. The unique qualitative advantage of our engine stems from our research in Distributional Semantics. This approach enables the vectorial representation of word meaning. Every word is associated with a vector which reflects the contextual (distributional) information across a text dataset. Vectorial representation allows us to quantify the similarity between meanings. On this basis, our algorithms are employed to automatically discover hidden patterns. We realize that most data in organizations is about customer engagement. For this reason, we want to offer the portfolio of products covering the entire multichannel 360° view: providing AI-powered analytics from insights, through inbound communication assistance, to contact center process automation to semantic segmentation. Private and public organizations, with large volumes of daily communication or large base of customers are our main target segments, representing substantial business opportunity across industries and geographies.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences data science natural language processing
- social sciences sociology industrial relations automation
- social sciences economics and business economics production economics productivity
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.2.3. - INDUSTRIAL LEADERSHIP - Innovation In SMEs
MAIN PROGRAMME
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H2020-EU.3. - PRIORITY 'Societal challenges
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H2020-EU.2.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
SME-1 - SME instrument phase 1
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-EIC-SMEInst-2018-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
301 00 PLZEN
Czechia
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.