Creating the Eclipto.net Product: From Idea to Implementation

Creating the Eclipto.net Product: From Idea to Implementation

When I was an employee, our team was tasked with developing a solution for a major bank aimed at assessing the performance of its managers. The bank operated around 12 call centers, each with its own quality control department. The main task of the staff in these departments was to selectively listen to recordings of conversations between managers and clients and then manually assign ratings.

These ratings were not just numbers in reports. They directly influenced employee bonuses and served as the basis for analysis and identifying areas for improvement. This could involve improving product knowledge or enhancing skills in handling customer objections. Despite the importance of the process, it was incredibly time-consuming and required a huge amount of effort.

That’s when we had an idea: why not use modern technology to simplify and automate this process? With the development of artificial intelligence, it became clear that manual work could and should be replaced by smart algorithms. After all, if quality control managers are spending hours listening to and evaluating conversations, why not entrust this task to a machine, leaving specialists to focus only on deep analysis of the results and work on real improvements?

This idea marked the beginning of our own product — Eclipto.

From Research to MVP

The first steps in creating Eclipto involved detailed market research and understanding which specific problems the product should solve. An important part of our analysis was conducting in-depth interviews with representatives from sales and quality departments across various companies. We discovered that virtually every company involved in sales or customer service faces similar challenges: evaluating employee performance takes too much time, and the results are often subjective.

Having confirmed the demand for the idea, we began developing a minimum viable product (MVP). In the early stages of development, our main focus was on integrating a speech recognition system and enabling automatic analysis of key communication quality indicators. We understood that for the product to work successfully, achieving high speech recognition accuracy was critical, as the quality of subsequent analysis depended directly on it.

Choosing the platform and technologies was also a crucial step. For us, it was essential to build a scalable and reliable service capable of integrating with clients' existing CRM and telephony systems. As the main engine for speech analysis, we chose proven solutions such as OpenAI’s Whisper, and for data processing and storage, we used AWS cloud services.

Prototyping and Flexibility

We tested the first prototype using real data from one of our early clients. The results were impressive: tasks that previously required dozens of man-hours were now handled in a matter of minutes. At the same time, the system provided highly objective assessments of each call based on predefined parameters.

But despite the success of the first prototype, new challenges arose. We needed to make the system as flexible as possible so that any client could customize the evaluation parameters to fit their specific tasks and standards. Therefore, we developed a user-friendly interface that allowed administrators and managers to independently create and modify evaluation criteria.

We also realized that in addition to evaluation itself, an important feature was the ability to conduct detailed analysis and generate recommendations for employees. The product included analytical reports that not only displayed current performance indicators but also highlighted specific areas requiring attention and additional training.

Successful Launch and What’s Next?

The launch of the full Eclipto product confirmed the correctness of our approach. Companies appreciated the idea of receiving automated and objective evaluations of the performance of their sales and customer service departments. Thanks to the implementation of Eclipto, our clients were able not only to significantly reduce the time spent on evaluating employee communication quality but also to improve the effectiveness of their sales departments by accurately identifying weak points and responding to them promptly.

Today, we continue to develop Eclipto by adding new features and improving the user experience. But the most important thing we’ve gained during this time is confirmation that automating the evaluation process and using artificial intelligence in this field is the future — and that future has already arrived.

In the following articles, I will describe the functionality and specific features of Eclipto in more detail. Stay tuned!

Want to automate your sales and support quality evaluation? Explore how Eclipto can transform your business.

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