Lidiia Suslova의 Ender Turing 고객 상호작용 분석 소프트웨어

디자이너 Lidiia Suslova의 작품 Ender Turing 고객 상호작용 분석 소프트웨어은(는) 매우 권위 있는 2026 A' Design Award 철상의 소프트웨어 디자인 부문에서 탁월한 수상작입니다.

철소프트웨어· 2026

Ender Turing 고객 상호작용 분석 소프트웨어

디자이너: Lidiia Suslova

클라이언트: Ender Turing

소프트웨어 부문 A' Design Award 철상 수상작

6,731

인증된 수상작

#157760 수상 상태 인증하기

사이트 방문 A' Design Award에서 보기 보도 자료 수상 배지

색상

디자인

Ender Turing 고객 상호작용 분석 소프트웨어 소개

Ender Turing은 고객 서비스 대화를 분석하는 클라우드 기반 소프트웨어 플랫폼입니다. 통화와 채팅은 인공지능에 의해 자동으로 평가되어 품질 관리 업무를 단축시킵니다. 자연어 처리 기술은 텍스트 분류, 감성 분석, 개체명 인식, 번역 및 요약을 지원합니다. 드래그 앤 드롭 인터페이스로 맞춤형 모델을 구축할 수 있으며, 사전 학습된 모델은 다른 애플리케이션에 추가할 수 있습니다. 유연한 필터, 맞춤형 스코어카드 및 데이터 시각화는 관리자가 상호작용을 모니터링하는 데 도움을 줍니다.

수상 인정

A' Design Award 철상 소개

Ender Turing 고객 상호작용 분석 소프트웨어, 이 작품은 A' Design Award 철상 수상작으로, 잘 디자인된 창작물에 기대되는 전문적·산업적 요건을 충족하는 좋은 디자인에 수여되는 권위 있는 인정입니다. 철상 수상작은 창작자의 뛰어난 기술, 높은 전문성, 전문가다운 이해, 창의적 역량을 보여 줍니다. Ender Turing 고객 상호작용 분석 소프트웨어, 이 작품은 높이 평가받고 감탄과 사랑을 받는 창작물로서 실용적이고 혁신적일 것으로 기대됩니다. 이 철상 수상 디자인은 업계 모범 사례를 통합하며 노하우와 재능으로 연결된 유능한 기술적 특성을 갖추고 있습니다. Ender Turing 고객 상호작용 분석 소프트웨어, 이 작품은 철저함으로 존중받으며 삶의 질을 개선하여 세상을 더 나은 곳으로 만들도록 디자인되었습니다.

디자인 비하인드

프레스 킷

디자이너가 제공한 크레딧, 팀원 및 기술 세부 사항.

고유 특성: 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.
치수 및 패키징: 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.
태그: 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
생산 기술: 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.
디자인 과제: 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.
연구: 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.
영감: 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.
프로젝트 기간: 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!
운영 흐름: 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.
특허: 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
이미지 출처: Lidiia Suslova