Bronze Interface, Interaction and User Experience Design · 2023
Ender Turing SaaS
- A' Design Award 2023 Bronze Winner in Interface, Interaction and User Experience Design
- Awarded April 15, 2023
- CLIENT: Ender Turing
- Estimated Number of Design Views: 21,925
- Verification code 9EA81A555E · Verify this award
About Ender Turing SaaS
Ender Turing is a cloud-based software platform for call center quality management. A real-time dashboard allows supervisors and managers to monitor performance with live updates. The displayed metrics include call volume, average handle time, first call resolution, customer satisfaction scores and agent availability. Automated pre-scoring of calls and chats shortens quality reviews, and flexible filters allow a small sample to be selected instead of every conversation. Scorecards can be created and edited without engineering support. The interface is offered in more than ten languages.
Press Kit
- Unique Properties
- 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.
- Dimensions & Packaging
- 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.
- Tags
- 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
- Production Technology
- 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.
- Design Challenge
- 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.
- Project Duration
- 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.
- Operation Flow
- 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. - Research
- 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. - Inspiration
- 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.
- Patents
- 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
Colors of Ender Turing SaaS
Design Interview about Ender Turing SaaS
The core principle guiding the design of Ender Turing is to create an intuitive and efficient user experience that leverages AI to enhance call center operations. Inspired by the potential of AI to transform customer interactions, the design focuses on clarity, accessibility, and user empowerment. By integrating advanced analytics seamlessly into the user interface, we aim to provide users with actionable insights without overwhelming them.
The primary focus was to ensure that complex AI-driven analytics are presented in a user-friendly manner. The goal was to enable call center managers and agents to easily access and interpret data to improve performance and customer satisfaction. Achieving a balance between sophisticated functionality and simplicity was paramount.
Future plans include iterative enhancements based on user feedback, integrating more advanced AI capabilities, and expanding the platform's adaptability to various industries beyond call centers. Continuous improvement will ensure the design remains at the forefront of user needs and technological advancements.
The initial design phase spanned approximately six months, encompassing user research, prototyping, and usability testing. This period allowed for thorough exploration and refinement to ensure the design met user expectations and business objectives.
The design was driven by the recognition of a gap in the market for AI-powered tools that enhance call center efficiency and customer satisfaction. While the project aligned with organizational goals, it was also fueled by a personal passion for leveraging technology to solve real-world problems.
The design is proprietary to Ender Turing and is actively used within our platform. There are currently no plans to sell or lease the design rights; instead, we focus on continuously improving and offering it as a core component of our services.
The increasing demand for efficient customer service solutions and the advancements in AI technology highlighted an opportunity to innovate in the call center industry. This convergence motivated the creation of a design that harnesses AI to deliver tangible benefits to users.
The design was influenced by industry best practices and the work of pioneers in user-centered design. Collaborative discussions with cross-functional teams also provided diverse perspectives that enriched the design process.
The primary users are call center managers and agents seeking to improve operational efficiency and customer satisfaction through AI-driven insights. The design caters to organizations aiming to leverage technology for enhanced customer interactions. Currently we are focused on providing more value to dusiness owners and gain business insights from analysing call center data.
The seamless integration of advanced AI analytics into an intuitive user interface distinguishes this design. Unlike other platforms that may present data in a complex manner, Ender Turing focuses on clarity and actionable insights, ensuring users can easily interpret and act upon the information provided.
The name "Ender Turing" pays homage to Alan Turing, a pioneer in computer science, symbolizing the integration of advanced technology and intelligence. "Ender" signifies the aim to provide end-to-end solutions, reflecting the comprehensive nature of the platform.
The design process utilized tools such as Sketch for wireframing and prototyping, InVision for collaborative feedback, and Adobe Illustrator for detailed graphic design elements. These tools facilitated efficient iteration and collaboration throughout the project.
The most unique aspect is the real-time feedback mechanism that allows supervisors and teammates to highlight specific moments or behaviors within conversations. This fosters a culture of continuous improvement and collaboration within call center teams.
ENDERTURING.COM
ENDERTURING.COM
The design process was highly collaborative, involving professionals with expertise in AI development, data analytics, and user experience research. This multidisciplinary approach ensured the design was both technically robust and user-centric.
Technology plays a central role, with AI algorithms analyzing voice and digital conversations to provide actionable insights. The design ensures these complex processes are presented to users in an accessible and meaningful way.
Yes, the design is heavily influenced by user data and analytical research. We conducted user interviews, usability testing, and analyzed interaction data to inform design decisions, ensuring the platform meets user needs effectively.
One significant challenge was presenting complex AI-driven analytics in a way that is easily understandable to users without technical backgrounds. Balancing functionality with simplicity required iterative testing and refinement.
Recognizing the innovative aspects of the design and its potential impact on the industry, we felt it was appropriate to submit it to gain broader recognition and feedback from the international design community.
The design process reinforced the importance of user-centered design principles and the value of cross-disciplinary collaboration.
Grand Interview about Ender Turing SaaS
It all started with recognizing the gaps in traditional call centers — like inefficient processes and missed opportunities to improve customer satisfaction and sales conversion rates. We focused on building practical AI tools like real-time speech recognition and agent assist to make life easier for agents and deliver better outcomes for customers. Working closely with users and refining the solution along the way helped us create something that truly works. Winning the Bronze A' Design Award is a great acknowledgment of that effort.
The decision to focus on specific metrics came from conversations with call center supervisors and other management roles who shared their biggest challenges — like tracking agent performance, spotting trends early, and ensuring customer satisfaction in real time. We identified the most common pain points and aligned the dashboard with actionable data, like customer sentiment, average handling time, agent adherence, etc. The goal was to give supervisors the insights they need without overwhelming them.
We tackled this challenge by combining advanced natural language processing models with language-specific customizations. Each language required careful tuning to account for nuances like tone, context, and cultural differences. Collaboration with native speakers and leveraging multilingual datasets ensured we captured sentiment accurately. Continuous testing and feedback loops helped refine the models, ensuring consistency in analysis and scoring across all 24 languages.
The self-coaching prescriptions were designed by focusing on actionable insights tailored to individual agents. Using AI-driven analysis of conversations, we identified key areas for improvement, such as communication tone, adherence to scripts/internal procedures, or resolution strategies. The system delivers concise, specific feedback along with the best examples, making it easy for agents to understand and act on.
One of the biggest challenges was creating a scorecard system that was both highly customizable for different use cases and easy to use. Early versions were not ideal, which we discovered through user testing. We simplified the design by introducing templates and easy configurations while ensuring advanced users could still access deep customization options and automations. Regular feedback cycles with supervisors helped fine-tune features like updates, weight adjustments, and accuracy measurement to make the system both powerful and user-friendly.
The 20-fold increase in call scoring speed achieved by Ender Turing SaaS is the result of a combination of advanced technical and design optimizations. By leveraging AI-driven automation through natural language processing (NLP) and machine learning, much of the scoring process is pre-handled, significantly reducing manual workload. This is paired with user-centric interface designs that streamline workflows and minimize friction for reviewers. Additionally, optimized batch processing and cloud-native scalability ensure efficient handling of large volumes of calls with minimal lag. Together, these elements deliver remarkable performance improvements without compromising accuracy or usability.
Ender Turing SaaS’s interface was designed to balance simplicity and depth. For non-technical users, we focused on clean layouts, clear language, and pre-configured options that require minimal setup. User testing with diverse groups helped refine the interface, ensuring it works intuitively for supervisors and analysts alike while still delivering powerful, AI-driven insights seamlessly.
End user insights played a pivotal role in shaping Ender Turing SaaS by driving the development of intuitive interfaces and practical features. Feedback highlighted pain points in workflows, leading to simplified navigation, faster access to key functions, and automation where possible. These conversations ensured the platform addressed real-world needs, balancing ease of use with robust capabilities tailored to users' priorities.
We see Ender Turing SaaS evolving into advanced predictive models that not only highlight issues but also proactively suggest solutions before they escalate. On the agent side, we’re focusing on AI-driven assistance that enables agents to perform at their peak levels without lengthy training. Additionally, we’re building a dedicated dashboard for C-level executives, providing direct, real-time access to customer insights. This eliminates the need to wait for lengthy reports, empowering faster, data-driven decision-making.
Organizations have achieved remarkable transformations by leveraging Ender Turing SaaS's real-time insights and automation. For example, a financial services client reduced average handling time by 20% through AI-driven agent assistance, boosting efficiency and allowing agents to focus on upselling opportunities. Similarly, a healthcare provider utilized Ender Turing to analyze conversions into appointments, resulting in an 8% revenue increase across its entire chain of clinics. These outcomes showcase how actionable insights and streamlined processes can transform call centers into strategic growth drivers.