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Our solution is bringing results_

  • SentiSquare_ E-mail dispatcher saved 12 FTE.

    Customers are getting swifter replies.

    Additional value: Insightful meta-data for complaints department.

  • Albert

    NPS feedback analysis tells Albert what customers want.

    Business Intelligence department saved time needed to sort through feedbacks.

    Precise categorization of open-answer verbatims.

  • T-Mobile

    SentiSquare helps identify lucrative churn candidates.

    Precise prioritization of 1,250,000 text messages yearly.

    Insight into ‘moments of truth’ and root causes.

  • O2 Slovakia

    Identifying negative feedback.

    Relaying precise data about customer complaints.

    O2 gets a clear idea of what customers want improved.

  • Konica Minolta

    Cooperation in software development.

    Recognition of important structural and meaning-carrying elements within text.

    Solution deployed in 10 languages.

  • Česká Spořitelna

    Online chat analysis: automatic clustering of conversations.

    Solved problem of ‘black box’ chat room that produces very little data.

    ČS gets insight on emerging topics that customers bring up.

  • Residomo

    Multichannel analysis of customer interactions: Calls, emails, tickets.

    Unstructured customer queries into actionable insight.

    Discovering hidden causal patterns across touchpoints to drive process optimization.

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Based on artificial intelligence and machine learning, our tools process large amounts of textual data no matter what the language.

We use deep semantics to discover and extract topics and issues hidden in textual content. This gives users a clear understanding and helps companies to enhance productivity and improve their processes.

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