SentiSquare creates small language models tailored precisely to customer needs. We train our own transformers to create these small specialized models. A transformer is a special neural network architecture that has revolutionized the field of NLP (Natural Language Processing). The most advanced language models today, such as ChatGPT and Gemini, are based on transformers.
Specialized small language models (SLMs) form the basis of our platform. They are easily adaptable, trainable, and ensure high accuracy. SLMs are designed to process huge volumes of customer interactions and provide effective real-time evaluation. They operate securely on-premises, so your sensitive data always remains under your full control.
Ideal for scenarios with sensitive data, high interaction volumes, and low latency requirements.
Ideal for ad-hoc analysis and fast changing environments, thanks to their universal knowledge.
We combine SLMs for reliable, large-scale analytics with LLMs for flexibility and deeper understanding. Both technologies are at hand in one secure No-Code NLP platform.
SentiSquare delivers omnichannel analytics across the entire SLM lifecycle. The platform enables companies to create and customize their own specialized AI models for analyzing customer interactions across all channels in real time.
The solution is language independent, fast and self-sufficient, with the option of on-premise operation and full control over sensitive data. The platform is also fully integrated with LLMs, which complement SLM in situations requiring ad-hoc analysis or answers to new types of question.
SentiSquare covers the entire AI lifecycle, from data processing and annotation, through models, to monitoring and measurable business benefits such as increased efficiency, sales, and customer satisfaction.
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Gartner predicts that by 2027, organizations will use small, narrowly focused AI models three times more than general-purpose LLMs. SLMs provide faster responses and consume less computing power, reducing operational and service costs.Link to the study
The SentiSquare No-Code NLP platform is ready for multi-tenant deployment. One installation can support multiple clients. You don't have to worry about data security because each client operates within their own dedicated data sandbox.
Our algorithms do not depend on the language. They work efficiently on different language families including languages with rich morphology.
Our security system follows the principle of explicit security. Each standard user requires explicit permissions for every object, ensuring complete control over access. Manage your permissions like a pro.
Do you have a large volume of texts to process? Embrace automation to save time and effort in your contact center operations. Our automated solution works tirelessly, 24/7, delivering consistent results without fluctuations or breaks.
Detailed statistics provide a comprehensive overview of system usage. Monitor the usage of classifiers and track how often they have been called. Stay informed about the performance of your system.
Detailed statistics provide a comprehensive overview of system usage. Monitor the usage of classifiers and track how often they have been called. Stay informed about the performance of your system.
The development of new effective text classifiers using a combination of generative and non-generative AI models is co-financed by the European Union.
Project name: Development of new effective text classifiers using a combination of generative and non-generative AI models
Challenge name: OP TAK 2021–2027, Application – Call II. Development of digital solutions
The aim of the project is to further improve small specialized language models so that their training requires as little manual annotation as possible while maintaining human-like success, very low latency, and minimal hardware requirements. The innovation lies in the use of two types of supervision of small language models at the same time, namely manual annotations and LLM. The key feature for maintaining low latency and minimal hardware requirements is that LLM is only needed during the training phase. It is no longer needed when using AI. The project is receiving financial support from the EU.
Read about real-life experiences with the SentiSquare No-Code NLP solution.
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