Ender Turing SaaS B2C Analysis by Lidiia Suslova is a highly esteemed winner of the very prestigious 2026 A' Design Award in Software Design Competition Category.
IronSoftware· 2026
Ender Turing SaaS B2C Analysis
Client: Ender Turing
A' Design Award Winner in Software Category
Verified Award Winner
Verify Award Status for #157760The Design
About Ender Turing SaaS B2C Analysis
Ender Turing is a cloud based software platform for the analysis of customer service conversations. Calls and chats are scored automatically by artificial intelligence, which shortens quality assurance routines. Natural language processing supports text classification, sentiment analysis, entity recognition, translation and summarization. Custom models can be built with a drag and drop interface, and pre-trained models can be added to other applications. Flexible filters, custom scorecards and data visualization help supervisors monitor interactions.
The Recognition
About the A' Design Award
Ender Turing SaaS B2C Analysis is an Iron A' Design Award winner, a prestigious recognition granted to good designs that meet the professional and industrial requirements expected from well-designed creations. Iron winners demonstrate great skill, high specialization, expert understanding, and creative capacity of their creators. Ender Turing SaaS B2C Analysis is expected to be a highly regarded, admired, and cherished creation that is practical and innovative. This Iron A' Design Award-winning design integrates industry best practices and has competent technical characteristics linked with know-how and talent. Ender Turing SaaS B2C Analysis is respected for its thoroughness and is designed to provide quality of life improvements that help make the world a better place.
Interviews
Design Interview
What is the main principle, idea and inspiration behind your design?
The project started from a very practical observation:
companies already have huge amounts of customer insight inside conversations, but most of it is never really used.
Traditional QA workflows usually review only a small percentage of calls manually. Everything else disappears into archives because there’s simply too much information to process consistently.
What interested me most wasn’t only the AI side of the system. It was the human side.
How do you help people quickly understand what actually matters?
How do you surface patterns without overwhelming users with dashboards and noise?
How do you make large-scale conversational data feel clear instead of intimidating?
That became the core direction of the design.
A lot of the work was really about reducing complexity at the interface level while still preserving depth underneath.
What has been your main focus in designing this work? Especially what did you want to achieve?
The biggest focus was usability under real operational conditions.
The platform processes large amounts of conversational data across multiple languages, but raw AI output alone is not very useful if people cannot confidently act on it.
So the goal became making the system feel understandable for non-technical users:
supervisors, QA specialists, operations teams, business stakeholders.
I wanted people to open the platform and quickly understand:
what needs attention,
what changed,
what patterns are repeating,
where the problems actually are.
A lot of AI products look impressive during demos but become exhausting in everyday use. I wanted this system to feel useful in real workflows, not just visually sophisticated.
What are your future plans for this award winning design?
The platform is gradually evolving from a QA-focused system into something closer to customer intelligence infrastructure.
Initially the focus was operational:
agent performance, call quality, compliance, coaching.
Now the more interesting direction is understanding broader customer behavior through conversations:
complaints, product friction, pricing concerns, sentiment shifts, recurring patterns.
That changes the design challenge quite a lot.
A supervisor and a business owner consume information very differently. One lives inside the platform daily. The other needs strategic clarity very quickly.
So a lot of the current work is around adaptive dashboards, information hierarchy, data visualization, and helping different types of users navigate large amounts of information without getting overloaded.
Behind the Design
Press Kit
Credits, team members and technical details provided by the designer.
- Unique Properties
- 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. - Dimensions & Packaging
- 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.
- Tags
- 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
- Production Technology
- 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.
- Design Challenge
- 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.
- Research
- 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.
- Inspiration
- 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. - Project Duration
- 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!
- Operation Flow
- 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.
- 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
- Image Credits
- Lidiia Suslova
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