Ender Turing د کال سنټر تحلیلي سافټویر د Lidiia Suslova لخوا د خورا باوقاره 2023 د برونز A' Design Award جایزه په د انټرفیس، تعامل او د کارونکي تجربې ډیزاین سیالۍ کټګوري کې یو ډیر ارزښتمن ګټونکی دی.
برونزانټرفیس· 2023
Ender Turing د کال سنټر تحلیلي سافټویر
د Lidiia Suslova لخوا
پیرودونکی: Ender Turing
په انټرفیس کټګوري کې د د برونز A' Design Award جایزه ګټونکی
تایید شوی د جایزې ګټونکی
د #149673 لپاره د جایزې حالت تایید کړئډیزاین
د Ender Turing د کال سنټر تحلیلي سافټویر په اړه
انډر ټیورنګ یو کلاوډ-بنسټ سافټویر پلیټفارم دی چې د کال سنټر د کیفیت مدیریت لپاره ډیزاین شوی. یو ریښتینی-وخت ډشبورډ ناظرینو او مدیرانو ته اجازه ورکوي چې د ژوندي تازه معلوماتو سره د کارکړنې څارنه وکړي. ښودل شوي شاخصونه د زنګ حجم، د زنګ اداره کولو منځنی وخت، د لومړي زنګ حل، د پیرودونکي رضایت نمرې، او د اجنټ شتون شامل دي. د زنګونو او چیټونو خپلکارې مخکینۍ نمرې کول د کیفیت بیاکتنې لنډوي، او د انعطاف وړ فلترونه دا امکان برابروي چې د هرې خبرې اترې پر ځای یوه کوچنۍ نمونه غوره شي. سکورکارډونه د انجینرۍ ملاتړ پرته جوړ او سمول کیدی شي. دا انٹرفیس په لسو څخه زیاتو ژبو کې وړاندې شوی دی.
پیژندنه
د د برونز A' Design Award جایزه په اړه
Ender Turing د کال سنټر تحلیلي سافټویر د برونز A' Design Award جایزې ګټونکی دی، یوه ډېره ویاړلې پېژندنه چې غوره ډیزاینونو ته ورکول کېږي چې په خلاقیت، هوښیارۍ او زیرکتیا ځانګړي دي. د برونز ګټونکي ډېر ماهر او په حیرانوونکي ډول خلاقي آثار دي چې د خپلو ډیزاینرانو پراخ تجربه، تخیل او چارواکتیا تاییدوي. له Ender Turing د کال سنټر تحلیلي سافټویر څخه تمه کېږي چې یو ډېر ارزښتناک، ستایل شوی او ګران شی وي چې په پام وړ ډول مسلکي او نوښتګر دی. دا د برونز A' Design Award جایزې ګټونکی ډیزاین په هنر، ساینس، ډیزاین او ټیکنالوژۍ کې غوره کړنلارې یو ځای کوي او د جزیاتو ته د پاملرنې له امله غوره دی. Ender Turing د کال سنټر تحلیلي سافټویر اړتیاوې په ښکلي ډول پوره کوي، هیلو ته رسېږي، احساسات ځانته ملګري کوي او لیوالتیا راپاروي.
د ډیزاین تر شا
خبري کټ
اعتبارونه، د ټیم غړي او تخنیکي توضیحات چې د ډیزاینر لخوا وړاندې شوي.
- ځانګړي ځانګړتیاوې
- A real-time dashboard allows supervisors and managers to monitor call center KPIs and agent progress with live updates. It displays metrics such as call volume, average handle time, first call resolution, customer satisfaction scores, and agent availability. This helps identify areas of improvement, detect issues, and optimize call center performance through proactive management and data-driven decision-making, leading to improved efficiency, customer satisfaction, and business outcomes.
- ابعاد او بسته بندي
- The primary function of Ender Turing engine is to help users create and train AI models that can understand and generate human-like language. The platform provides a range of NLP capabilities, including text classification, sentiment analysis, entity recognition, language translation, and text summarization, among others. Users can build custom models using Ender Turing's drag-and-drop interface or integrate pre-trained models into their applications using the platform's API.
- ټاګونه
- Artificial intelligence (AI), Natural language processing (NLP), Machine learning, Text classification, Sentiment analysis, Data processing, Text summarization API, Cloud-based platform, Language modeling, Text mining, Call center, Scoring
- د تولید ټیکنالوژي
- We used a data-driven design approach to gather feedback for our design, which involved observing and interviewing customers of the product. All of our decisions were based on identifying user pain points and areas of frustration. As a result, we developed a product solution that eliminates most pain points for call center supervisors, increases their productivity, reduces stress and frustration, and features an intuitive UX interface covered with more than 10 language options.
- د ډیزاین ننګونه
- Experiments were done to test hypotheses and validate design decisions. We found that our users are non-technical and prefer a more prominent communication style. We avoid using complex logic constructions and heavy interface decisions. Our UX solutions are continuously simplified.
- څیړنه
- As we maintain ongoing communication with our end users, including data center supervisors and stakeholders, we have collected data through a range of research methods, such as interviews, surveys, and observations.
This has allowed us to gain valuable insights into usesr needs, preferences, and behaviors, which will inform the design of effective solutions that meet their specific requirements. - الهام
- Call centers handle up to 1000 calls per agent per day. Measuring and analyzing this data with metrics similar to those used by humans is crucial to improve client service and call center productivity. A productive call center saves businesses revenue and promotes polite and friendly communication with customers, enhancing a business's reputation, improving customer satisfaction, and increasing their overall quality of life.
- د پروژې موده
- The project started in June 2020 in Tallinn, Estonia. The Design for the Project started at January 2022 and still developing and enhancing. Product was nominated as winner at Latitude59 pitching competition in May 2022, Tallinn, Estonia and took 2nd place at Infoshare Startup Contest where been selected among 500 startups from all over the world in October 2022, Gdansk, Poland.
- د عملیاتو بهیر
- Our solution provides a 20-fold increase in speed for scoring calls and chats. This is made possible by automated AI pre-scoring and call center quality assurance. Using flexible filters, you can randomly select only a small number of calls and chats for quality monitoring, rather than monitoring all of them.
Moreover, our system allows for flexible setup of scorecards with no limitations. This means that you will no longer need engineers or Excel sheets to modify or create new scorecards. - پیټنټونه
- 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
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