Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna ngu-Lidiia Suslova

Uyilo olubizwa ngokuba yi-Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna olwenziwe ngu-Lidiia Suslova ngumwini obekekileyo webhaso elinobukhazikhazi le-2023 Ibhaso Lebhronzi le-A' Design Award Kudidi Lokhuphiswano lwe-Uyilo lwe-Inthafeyi, uNxibelelwano kunye naMava oMsebenzisi.

IbhronziI-Inthafeyi· 2023

Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna

ngu Lidiia Suslova

Umxhasi: Ender Turing

Umwini we-Ibhaso Lebhronzi le-A' Design Award Kudidi Lwe-I-Inthafeyi

22,531

Umwini Webhaso Oqinisekisiweyo

Qinisekisa Isimo Sebhaso se-#149673

Tyelela iSayithi Jonga kwi-A' Design Award Uxwebhu Lweendaba Ibheji Yembasa

Imibala

Uyilo

Malunga ne-Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna

Ubhalo Lwencwadi: Ender Turing yiplatifomu yesoftware esekwe kwifu yolawulo lomgangatho weziko lokufowuna. Ibhodi yekhompyutha yexesha langoku ivumela abaphathi kunye nabalawuli ukuba babeke iliso kwindlela abasebenza ngayo ngoluhlaziyo lwexesha langoku. Imilinganiselo eboniswayo iquka umthamo wamafowuna, ixesha eliphakathi lokuphatha ifowuna, ukusonjululwa kwengxaki ngokufowuna okokuqala, amanqaku okwaneliseka komthengi kunye nokufumaneka kwomsebenzi. Ukuhlolwa kwangaphambili okuzenzekelayo kwamafowuna neencoko kunciphisa ixesha lophononongo lomgangatho, yaye izihluzo ezinokuguqulwa zivumela ukuba kukhethwe isampuli encinci endaweni yeengxoxo zonke. Amakhadi amanqaku anokwenziwa nokuhlengahlengiswa ngaphandle koncedo lobunjineli. Le ndlela yokusebenzisana inikezelwa ngeelwimi ezingaphezulu kwezilishumi.

Ukuqatshelwa

Malunga ne-Ibhaso Lebhronzi le-A' Design Award

Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna ngumphumeleli weIbhaso Lebhronzi le-A' Design Award, ukuqatshelwa okuhlonitshwayo kakhulu okunikwa imiyilo ephawulekayo eyahlukaniswa kukuyila, ubukrelekrele nobukrelekrele. Abaphumeleleyo beBhronzi ziimveliso ezinobuchule obukhulu nezidala ngokumangalisayo ezingqina amava aphangaleleyo, ukucinga nokwazi ukuzenzela kwabayili bazo. Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna iyathenjwa ukuba ibe yimpahla exatyiswa kakhulu, edunyiswayo nethandwayo enobungcali obuphawulekayo nezinto ezintsha. Lo myilo uphumeleleyo weIbhaso Lebhronzi le-A' Design Award ubandakanya iindlela ezilungileyo kakhulu kubugcisa, inzululwazi, uyilo kunye nobuchwephesha kwaye ugqwesile ngenxa yokunikela ingqalelo kwiinkcukacha. Ender Turing Umsebenzi Ophambili: Isoftware Yohlalutyo Lweziko Lokufowuna iyanelisa iimfuno ngokumangalisayo, ichukumisa iinqwenelo, ikhwebela iimvakalelo kwaye itsala umdla.

Emva Koyilo

Ikhithi Yeendaba

Iziqinisekiso, amalungu eqela kunye neenkcukacha zobugcisa ezinikezelwe ngumyili.

Iimpawu Ezizodwa: 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.
Imilinganiselo nePakishini: 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.
Iithegi: 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
Ubuchwepheshe Bokuvelisa: 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.
Umngeni Woyilo: 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.
Uphando: 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.
Inkuthazo: 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.
Ubude Beprojekthi: 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.
Ukuhamba Komsebenzi: 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.
Iipathenti: 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