Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso nataon'i Lidiia Suslova

Ny endrika Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso nataon'i Lidiia Suslova dia mpandresy sarobidy amin'ny 2023 A' Design Award Varahina amin'ny Sokajin'ny Fifanintsanana Famolavolana Serasera, Fifandraisana ary Traikefan'ny Mpampiasa.

VarahinaSerasera· 2023

Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso

nataon'i Lidiia Suslova

Mpanjifa: Ender Turing

Mpandresy A' Design Award Varahina amin'ny Sokajy Serasera

22,531

Mpandresy Loka Voamarina

Hanamarina ny Sata Loka ho an'ny #149673

Tsidiho ny Tranonkala Jereo ao amin'ny A' Design Award Fanambarana ho an'ny Mpanao Gazety Marika Loka

Loko

Ny Endrika

Momba ny Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso

Ny Ender Turing dia sehatra rindrambaiko mifototra amin'ny "cloud" ho an'ny fitantanana kalitao ao amin'ny ivontoerana antso. Ny dashboard mizava-potoana dia ahafahan'ny mpanaramaso sy ny mpitantana manara-maso ny fahombiazana miaraka amin'ny fanavaozana mivantana. Ny fandrefesana aseho dia ahitana ny habetsahan'ny antso, ny fotoana antonony ampiasaina, ny fanadiovana antso voalohany, ny isan'ny fahafaham-po ny mpanjifa ary ny fahafahan'ny mpiasa. Ny fanombanana mialoha ny antso sy ny resadresaka ataon'ny milina dia mampihena ny fotoana ilaina amin'ny fandinihana kalitao, ary ny sivana malefaka dia ahafahana misafidy santionany kely fa tsy ny resadresaka rehetra. Azo forohina sy ovaina ny "scorecards" tsy misy fanampian'ny injenera. Ny sehatra fifandraisana dia atolotra amin'ny fiteny maherin'ny folo.

Ny Fankasitrahana

Momba ny A' Design Award Varahina

Nandresy ny A' Design Award Varahina ny famolavolana Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso, fankasitrahana malaza be omena ny famolavolana miavaka izay mampiavaka ny fahaiza-mamorona, ny fahazotoana ary ny fahamaotinan-tsaina. Ny mpandresy Varahina dia zavatra vita tanana mahay dia mahay ary mamorona mivantana izay manamafy ny traikefa be dia be, ny sarintsaina ary ny fahaizan-kafoy an'ny mpamolavola azy. Andrasana ho zavatra ankasitrahana be, ankafizina ary tiana, mahay amin'ny asa sady vaovao ny famolavolana Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso. Ity famolavolana nandresy ny A' Design Award Varahina ity dia mitahiry ny fomba fanao tsara indrindra amin'ny zavakanto, ny siansa, ny famolavolana ary ny teknolojia, ary tsara indrindra amin'ny fitandremana ny antsipirihany. Ny famolavolana Ender Turing Rindrambaiko Fandalinana ho an'ny Ivontoerana Antso dia mahafa-po ny filana tsara, mikasika ny faniriana, mahasarika ny fihetseham-po ary misarika ny fahalianana.

Ao Ambadiky ny Endrika

Fitaovana ho an'ny Mpanao Gazety

Fankasitrahana, mpikambana ao amin'ny ekipa ary antsipiriany teknika nomen'ny mpanao endrika.

Toetra Manokana: 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.
Refy & Fonosana: 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.
Marika: 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
Teknolojia Famokarana: 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.
Fanamby Endrika: 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.
Fikarohana: 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.
Aingam-panahy: 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.
Faharetan'ny Tetikasa: 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.
Fizotr'asa: 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.
Patanta: 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