Lidiia Suslova ᱨᱮᱱᱟᱜ Ender Turing ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱫᱚᱦᱲᱟ ᱨᱚᱲ ᱵᱤᱪᱟᱹᱨ ᱥᱚᱯᱷᱴᱣᱮᱨ

Lidiia Suslova ᱨᱮᱱᱟᱜ Ender Turing ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱫᱚᱦᱲᱟ ᱨᱚᱲ ᱵᱤᱪᱟᱹᱨ ᱥᱚᱯᱷᱴᱣᱮᱨ ᱫᱚ ᱥᱚᱯᱷᱴᱣᱮᱭᱟᱨ ᱯᱨᱚᱛᱤᱡᱚᱜᱤᱛᱟ ᱵᱷᱟᱜ ᱨᱮ 2026 A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱨᱮᱱᱟᱜ ᱟᱹᱰᱤ ᱢᱟᱱ ᱟᱠᱟᱱ ᱡᱤᱛᱠᱟᱨ ᱠᱟᱱᱟ᱾

ᱞᱚᱦᱟᱥᱚᱯᱷᱴᱣᱮᱭᱟᱨ· 2026

Ender Turing ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱫᱚᱦᱲᱟ ᱨᱚᱲ ᱵᱤᱪᱟᱹᱨ ᱥᱚᱯᱷᱴᱣᱮᱨ

Lidiia Suslova ᱞᱮᱠᱟᱛᱮ

ᱜᱟᱦᱟᱠ: Ender Turing

ᱥᱚᱯᱷᱴᱣᱮᱭᱟᱨ ᱵᱷᱟᱜ ᱨᱮ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱡᱤᱛᱠᱟᱨ

6,731

ᱡᱟᱸᱪ ᱟᱠᱟᱱ ᱮᱣᱟᱨᱰ ᱡᱤᱛᱠᱟᱨ

#157760 ᱞᱟᱜᱤᱫ ᱮᱣᱟᱨᱰ ᱦᱟᱞᱚᱛ ᱡᱟᱸᱪ

ᱥᱟᱭᱤᱴ ᱧᱮᱞ ᱢᱮ A' Design Award ᱨᱮ ᱧᱮᱞ ᱢᱮ ᱯᱨᱮᱥ ᱨᱤᱞᱤᱡ ᱯᱩᱨᱥᱠᱟᱨ ᱵᱮᱡ

ᱨᱚᱝ

ᱰᱤᱡᱟᱭᱤᱱ

Ender Turing ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱫᱚᱦᱲᱟ ᱨᱚᱲ ᱵᱤᱪᱟᱹᱨ ᱥᱚᱯᱷᱴᱣᱮᱨ ᱵᱟᱵᱚᱛ ᱛᱮ

ᱮᱸᱰᱮᱨ ᱴᱩᱨᱤᱝ ᱫᱚ ᱢᱤᱫᱴᱟᱹᱝ ᱠᱞᱟᱩᱰ ᱨᱮ ᱵᱮᱱᱟᱣ ᱥᱚᱯᱷᱴᱣᱮᱨ ᱯᱞᱮᱴᱯᱷᱚᱨᱢ ᱠᱟᱱᱟ ᱡᱟᱦᱟᱸ ᱫᱚ ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱥᱮᱣᱟ ᱜᱟᱞᱢᱟᱨᱟᱣ ᱨᱮᱭᱟᱜ ᱵᱤᱪᱟᱹᱨ ᱞᱟᱹᱜᱤᱫ ᱵᱮᱵᱷᱟᱨᱚᱜ ᱠᱟᱱᱟ ᱾ ᱠᱚᱞ ᱟᱨ ᱪᱮᱴ ᱠᱚ ᱟᱨᱴᱤᱯᱷᱤᱥᱤᱭᱟᱞ ᱤᱱᱴᱮᱞᱤᱡᱮᱸᱥ ᱦᱚᱛᱮᱛᱮ ᱟᱡᱛᱮ ᱱᱚᱢᱵᱚᱨ ᱮᱢᱚᱜᱼᱟ, ᱡᱟᱦᱟᱸ ᱛᱮ ᱜᱩᱱᱟᱱ ᱧᱮᱞᱡᱚᱝ ᱠᱟᱹᱢᱤ ᱠᱚ ᱠᱚᱢᱚᱜᱼᱟ ᱾ ᱱᱮᱪᱩᱨᱟᱞ ᱞᱮᱝᱜᱩᱭᱮᱡ ᱯᱨᱚᱥᱮᱥᱤᱝ ᱫᱚ ᱚᱞ ᱦᱟᱹᱴᱤᱧ, ᱢᱚᱱᱮ ᱵᱷᱟᱵᱽᱱᱟ ᱵᱤᱪᱟᱹᱨ, ᱮᱱᱴᱤᱴᱤ ᱩᱨᱩᱢ, ᱛᱚᱨᱡᱚᱢᱟ ᱟᱨ ᱠᱟᱴᱷᱟᱢᱳᱲᱟ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢᱚᱜᱼᱟ ᱾ ᱰᱨᱮᱜ ᱟᱨ ᱰᱨᱚᱯ ᱤᱸᱴᱚᱨᱯᱷᱮᱥ ᱛᱮ ᱠᱚᱥᱴᱚᱢ ᱢᱚᱰᱮᱞ ᱠᱚ ᱵᱮᱱᱟᱣ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ, ᱟᱨ ᱢᱟᱲᱟᱝ ᱠᱷᱚᱱ ᱛᱚᱭᱟᱨ ᱢᱚᱰᱮᱞ ᱠᱚ ᱮᱴᱟᱜ ᱮᱯᱞᱤᱠᱮᱥᱚᱱ ᱨᱮ ᱥᱮᱞᱮᱫ ᱫᱟᱲᱮᱭᱟᱜᱼᱟ ᱾ ᱩᱥᱟᱹᱨᱟᱱ ᱯᱷᱤᱞᱴᱚᱨ, ᱠᱚᱥᱴᱚᱢ ᱥᱠᱚᱨᱠᱟᱨᱰ ᱟᱨ ᱰᱟᱴᱟ ᱪᱤᱛᱟᱹᱨ ᱛᱮ ᱧᱮᱞᱩᱛᱤᱭᱟᱹ ᱠᱚ ᱜᱟᱞᱢᱟᱨᱟᱣ ᱧᱮᱞᱡᱚᱝ ᱨᱮ ᱜᱚᱲᱚ ᱠᱚ ᱧᱟᱢᱟ ᱾

ᱢᱟᱱᱟᱱ

A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱵᱟᱵᱚᱛ ᱛᱮ

Ender Turing ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱫᱚᱦᱲᱟ ᱨᱚᱲ ᱵᱤᱪᱟᱹᱨ ᱥᱚᱯᱷᱴᱣᱮᱨ ᱫᱚ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ Ender Turing ᱜᱟᱦᱟᱠᱤᱭᱟᱹ ᱫᱚᱦᱲᱟ ᱨᱚᱲ ᱵᱤᱪᱟᱹᱨ ᱥᱚᱯᱷᱴᱣᱮᱨ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾

ᱰᱤᱡᱟᱭᱤᱱ ᱯᱤᱪᱷᱟ ᱨᱮ

ᱯᱨᱮᱥ ᱠᱤᱴ

ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱧᱮᱞ ᱠᱷᱚᱱ ᱠᱨᱮᱰᱤᱴ, ᱴᱤᱢ ᱥᱫᱚᱥᱭ ᱟᱨ ᱴᱮᱠᱱᱤᱠᱮᱞ ᱵᱤᱵᱚᱨᱚᱬ ᱮᱢ ᱟᱠᱟᱱᱟ᱾

ᱟᱞᱜᱟ ᱜᱩᱱ ᱠᱚ: Ender Turing offers unique customer experience solutions, enhancing revenue and loyalty. Real-time analytics on employee-customer interactions empower informed decision-making.

Our AI platform automates QA routines, uncovers business insights, and boosts agent performance through continuous coaching.
ᱢᱟᱯ ᱟᱨ ᱯᱮᱠᱮᱡᱤᱝ: The core purpose of the Ender Turing engine is to facilitate users in crafting and refining AI models capable of comprehending and generating human-like language. This platform offers a comprehensive suite of Natural Language Processing (NLP) capabilities, encompassing tasks such as text classification, sentiment analysis, entity recognition, language translation, and text summarization, among others. Users have the flexibility to construct tailored models through Ender Turing's intuitive drag-and-drop interface, or seamlessly integrate pre-trained models into their applications via the platform's API.
ᱴᱮᱜ ᱠᱚ: Artificial intelligence, AI, Natural language processing, NLP, Machine learning, ML, Text classification, Sentiment analysis, Data processing, Text summarization, API, Cloud-based platform, Language modeling, Text mining, Call center, Analysis, Scoring
ᱛᱮᱭᱟᱨ ᱛᱮᱠᱱᱚᱞᱚᱡᱤ: Utilizing a data-driven design methodology, we collected feedback by directly observing and interviewing our product's customers. Our decisions were guided by the identification of user pain points and areas of frustration. The outcome is a product solution that effectively addresses the concerns of business. Leveraging insights gained, our solution drives revenue growth by enabling informed decision-making and strategic planning, enhancing business productivity, reducing stress, and offering an intuitive user experience. Moreover, our interface supports over 10 languages, ensuring accessibility and usability across diverse user bases.
ᱰᱤᱡᱟᱭᱱ ᱪᱮᱞᱮᱧ: Experiments were conducted to assess hypotheses and validate design decisions. Our findings revealed that our user base consists largely of non-technical individuals who favor a more prominent communication style. Consequently, we refrain from employing complex logic constructions and cumbersome interface designs. Instead, our UX solutions undergo continual refinement to ensure simplicity and ease of use.
ᱜᱚᱵᱮᱥᱚᱱᱟ: Engaging with end users, including data center supervisors and stakeholders, we collected data via interviews, surveys, observations, and competitive analysis. Our key finding is the potential of call center data analysis to boost revenue through invaluable insights for informed decision-making. This approach unveils user and business needs, guiding tailored solutions.
ᱯᱨᱮᱨᱚᱱᱟ: Market Demand: Recognizing the growing need for effective CX solutions in various industries.

Efficiency: Seeking ways to streamline processes within contact centers, QA automation, real-time analysis and revelation business insights.

Customer Satisfaction: Understanding the importance of delivering exceptional customer service to foster loyalty and drive revenue growth.

AI Technology: Leveraging advancements in AI and ML to develop innovative solutions that can adapt and evolve over time.
ᱯᱨᱚᱡᱮᱠᱴ ᱥᱚᱢᱚᱭ: The project commenced its journey in June 2020 in Tallinn, Estonia. Design efforts for the project began in January 2022 and are currently ongoing, with continuous development and enhancement. Notably, the product achieved recognition as the winner at the Latitude59 pitching competition in May 2022, held in Tallinn, Estonia. Additionally, it secured the 2nd place position at the Infoshare Startup Contest in October 2022, hosted in Gdansk, Poland, amidst stiff competition from 500 startups worldwide. Moreover, our team achieved the distinction of Best Seed startup at the Valencia Digital Summit conference in October 2023!
ᱠᱟᱹᱢᱤ ᱦᱚᱨ: Our solution offers a 20-fold speed increase in scoring calls and chats, powered by automated AI pre-scoring and quality assurance. It also extracts valuable business insights, allowing users to efficiently monitor interactions with flexible filters. With seamless scorecard customization and robust data visualization tools, informed decision-making is empowered. These capabilities optimize operations and enhance customer experiences, contributing to revenue growth.
ᱯᱮᱴᱮᱱᱴ ᱠᱚ: Scientific publications: 1. Transferability Evaluation of Speech Emotion Recognition Between Different Languages January 2022 DOI:10.1007/978-3-031-04812-8_35 In book: Advances in Computer Science for Engineering and Education (pp.413-426) Ievgen Iosifov Olena Iosifova Oleh Romanovskyi Vladimir Sokolov 2. Automated Pipeline for Training Dataset Creation from Unlabeled Audios for Automatic Speech Recognition July 2021 DOI:10.1007/978-3-030-80472-5_3 In book: Advances in Computer Science for Engineering and Education IV (pp.25-36) Oleh Romanovskyi Ievgen Iosifov Olena Iosifova Vladimir Sokolov 3. Analysis of Automatic Speech Recognition Methods January 2021 Conference: Cybersecurity Providing in Information and Telecommunication SystemsAt: Kyiv, Ukraine Olena Iosifova Ievgen Iosifov Vladimir Sokolov Oleh Romanovskyi Igor Sukaylo 4. Sentence Segmentation from Unformatted Text using Language Modeling and Sequence Labeling Approaches October 2020 DOI:10.1109/PICST51311.2020.9468084 Conference: 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T) Ievgen Iosifov Olena Iosifova Vladimir Sokolov 5. Methods and components of natural language processing August 2020 Adaptive automatic control systems 1(36):93-113 DOI:10.20535/1560-8956.36.2020.209780 Olena Iosifova Ievgen Iosifov Oleksandr Rolik 6. Techniques Comparison for Natural Language Processing June 2020 DOI:10.5281/zenodo.3895814 Conference: 2nd International Workshop on Modern Machine Learning Technologies and Data ScienceAt: Lviv-Shatsk, Ukraine Olena Iosifova Ievgen Iosifov Oleksandr Rolik Vladimir Sokolov
ᱪᱤᱛᱟᱹᱨ ᱥᱟᱹᱦᱟ: Lidiia Suslova