Context : The end user wanted to be able to track the progress of their survey after it was sent out.
Problem : The user, who had to wait for meaningful results from their survey, grew impatient when they saw their dashboard was empty.
Context : The Customer Success team had to create customized questionnaires for each customer, based on Word or Excel documents they sent us.
Problem : Each question had to be copied and pasted manually into the application editor, a repetitive and time-consuming task that could take up to an hour per questionnaire.
Context : Customer wanted to gain better visibility into their information system. Until then, no centralized monitoring solution was in place, which complicated incident analysis and slowed down decision-making.
Problem : The IT system suffered from sporadic latency, slowdowns, and opaque overall activity. It was impossible to quickly identify bottlenecks or the most resource-intensive applications.
Context : The rise in popularity of LLMs among the general public has prompted the technical team to design innovative AI-based features.
Problem : LLM APIs generate their responses in real time, word by word. To support this functionality, it was essential to develop a custom microservice from scratch.
