Canatu, a deep-tech company, accelerated its product development with UnSeen’s help. An easy-to-use analytics application reduces manual work, frees up researchers’ time for development, and enables the team to generate and analyse far more data to support product development.
Challenge: More and Better Data, Less Time Spent Handling It
Canatu aims to revolutionise hormone diagnostics with a solution that can provide measurement results during a single patient visit. Today, accurate hormone testing may require days of waiting, expensive analytical equipment, and specialist expertise.
Developing the solution requires large amounts of reliable, repeatable, and comparable data, produced by analysing electrochemical measurement curves. At Canatu, this analysis phase was at risk of becoming a bottleneck.
“We wanted to automate the analysis so that the result would always be produced in the same way, regardless of who performs it, and without errors caused by manual work. We needed to free our researchers to focus on development instead of moving data and files around,” says Pekka Heikkinen, who is responsible for the development of Canatu’s diagnostic system.
Canatu’s own team had already programmed a tool to support the analysis. It worked, but it was far too technical and difficult to adopt as a tool for the whole team. The aim was to lower the barrier to use while making the analysis more consistent, faster, and scalable.
Solution: Turning an Analytics Algorithm into an Easy-to-use Application
UnSeen built an application around the core of Canatu’s existing tool. Its clear user interface guides users through the same analysis process every time. The application automates data processing, stores measurement history, and makes it easier to compare results.
“The application simplifies the use of the tool, not the analysis itself. The analytics algorithm remained fully in Canatu’s hands and can continue to be developed freely by their team,” says Timo Matinvesi, who led the project on UnSeen’s side.
The project was carried out in an agile way together with Canatu’s US team, with the time difference turned into an advantage. Changes made during the working day in Finland could be tested immediately in the US, and feedback was waiting for the UnSeen team the following morning. The application was in production in just six weeks.
“UnSeen listened to our needs extremely well. If something was unclear, they asked instead of building based on assumptions. That is why the project reached the finish line so smoothly,” Heikkinen says.
“UnSeen listened to our needs extremely well. If something was unclear, they asked instead of building based on assumptions. That is why the project reached the finish line so smoothly.”
Result: An Hour-long Task Reduced to Five Minutes
The application was quickly adopted at Canatu, and the benefits were immediately apparent. Previously, analysing the data from a single measurement took a researcher 30–60 minutes. Now, the same task takes five minutes.
The time savings free up a significant part of the researchers’ working day for actual product development. Instead of handling data, researchers can focus on new measurements, experiments, and optimising the solution.
“Working with UnSeen has enabled us to generate and analyse the volume of data our product development requires on a completely different scale. It helps us stay on schedule and build the body of data required, for example, to meet the strict regulatory requirements for diagnostics,” Heikkinen says.
The application produces measurement results in a consistent format, making comparison, reporting, and the detection of changes faster and easier. When decisions are based on comparable data, it is easier to take the next steps in product development in the right direction.
The time savings free up a significant part of the researchers’ working day for actual product development.
“The time savings free up a significant part of the researchers’ working day for actual product development.”
up to 92%
less time spent analysing data from a single measurement.
1.5 months
from development to production.
2
people in UnSeen’s agile core team.
300h
saved by reanalysing the entire historical measurement dataset with the application instead of doing the work manually.
