The hackathon project brought new innovations.
The client wanted to explore how to utilise industrial process data in an Elastic Cloud environment and find new ways to generate value from data. The developers had free rein to create solutions that combined technical problem‑solving, practical customer understanding, and modern technologies.
“I recommend hackathon projects to companies that are looking for fresh perspectives and new solutions for their own development ideas.” – Laura Leskinen, Business Director, PlantSys
New ideas for product development
The hackathon project was planned together by kood/Sisu, Documtec, and PlantSys. In addition to the technical results, the hackathon generated many new ideas and concrete insights. The project helped identify development opportunities that could improve system reliability, deepen customer understanding, and support proactive maintenance. These insights had a direct impact on operational efficiency and customer experience.
“The idea behind the hackathon was innovation. We sought out‑of‑the‑box ideas, new perspectives, and ways to utilize data — ideas that often go unnoticed in traditional development projects. We did not expect a ready‑made product, but fresh insights and new openings.” Laura Leskinen, Business Director, PlantSys
The hackathon team had access to real customer data and a clear problem to solve. Developers were able to explore the topic freely and build several working prototypes in just a few days, each offering new ideas for future development.
Experience and collaboration
The hackathon proved to be a valuable way to examine development work from a new angle.
“It was inspiring to see the participants’ motivation, and we received many ideas in a short time. We are integrating Elasticsearch into our product, and it was great to see that almost everyone managed to get it working quickly.”
Several prototypes were quickly developed for the customer during the 48‑hour hackathon.
Our solution included:
- Modeling and visualizing subsystem processes
- The solution produced views that made complex process data understandable.
- Automated temporal comparison analysis
- The system compared historical data with real‑time events and automatically identified anomalies.
- Root cause detection for anomalies
- The solution distinguished one‑off errors from recurring issues.
- AI‑based maintenance recommendations
- The AI model provides suggestions on which actions to take, when, and why.
- The ability to use prompt-based interaction in real scenarios
- The system included a mechanism that allowed directing the AI using natural‑language questions and tasks.
The end result was an automated data‑analysis system that provides insights, recommends maintenance actions, and explains detected anomalies.
“The solution got to the core of maintenance and service. AI‑based recommendations and the ability to use prompts were especially valuable for us.”
