General

The Journey Behind ayona: How We Built a Customer Intelligence Platform

Ayona was born out of a need to automate and quantify customer feedback analysis when no suitable tool existed, leading a team to develop a scalable customer intelligence platform that uses advanced technologies like LLMs to help companies make use of their qualitative data.
Noah Chavannes
3 minutes

The origins of ayona

ayona began with a shared passion for building impactful products through advanced technologies. As the Chief Product Officer and AI Product Manager in the product management team, Rouven and Noah were constantly exploring innovations with the capabilities to scale such as large language models (LLMs). Our main driver was  to turn ideas into functional solutions.

While striving to stay data-driven in product management as well as as a company, we hit a major roadblock: there was no tool available to automatically analyse and quantify our vast amount of customer feedback. After extensive searches turned up nothing, we decided to build our own in-house solution, which became the foundation idea of ayona.

Building the prototype

An initial feedback management tool was built on retool, allowing us to quickly connect with existing systems to gather and categorise feedback. Initially, the process was manual and time-consuming, but it laid the groundwork for the advanced features such as automated data masking, LLM-powered tagging, sentiment analysis, and classification. A dashboard provided real-time insights into feedback categories and trends, turning the service into a powerful platform.



Founding and scaling ayona

Encouraged by our progress and first feedbacks from experts in the field, we decided to fund ayona and start building a comprehensive customer intelligence platform. At that time, Yves an IT and Data Science joined the team. In just a few months, we developing and deploying a scalable solution designed to help companies unlock deeper insights from their customer data.

Meanwhile, we expanded ayona to include automatic tag detection, eliminating manual steps and integrating features like problem graphs, anomaly detection, and recommendations that are quantified (e.g. on basis of affected unique user). The platform evolved into a full-fledged SaaS solution, enabling users to improve their products efficiently and effectively, prioritising with less effort - and to finally walk the talk when it comes to customer-centricity.

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