Aitaro
Leveraging insights and generating new business opportunities by integrating data science and statistical learning in an iterative manner are the building blocks of data driven innovation. This enables today's corporations to thrive in competitive markets where the utilisation of advanced data techniques becomes more and more ubiquitous. We support our customers to explore their data driven capabilities by taking a holistic approach on the end to end innovation process.
This includes multiple aspects:
We started with a data analysis, validating the existing, labeled data and a technical roadmap. Via rapid prototyping we were able to quickly outperform existing benchmarks and to deploy a first beta version to testers, validating the results with real-time data and feedback.
Automatically detecting defective devices significantly reduces negative feedback – greatly increasing customer happiness and retention while keeping the gathered data clean.
Our project partner sells sensors to measure oil tanks’ fill levels. These sensors are sometimes not properly mounted, though. Incorrect, undetected installations can lead to wrong measurements and high costs, which in turn leads to unhappiness on the clients’ side.
Python, Jupyter, Streamlit, Kedro, MLFlow, Tensorflow
Let's innovate together!
Our project partner sells sensors to measure oil tanks’ fill levels. Incorrect installations of these sensors can lead to wrong measurements and high costs, which in turn leads to unhappiness on the clients’ side.
Learn moreInitially, we analyzed previous sales records per client. With our project partner’s domain experts, we defined a sensible metric for to measure the quality of a system that would automatically recommend potential buyers. Based on this metric, we quickly iterated various models to achieve a prototype for user testing. Based on these quick wins, we were able to ensure high user satisfaction and technical feasibility early in the process.
The outcome of the project was an user-friendly web interface that greatly increased the rate of successful customer interactions, increasing employee and customer satisfaction while reducing the overall costs.
Our project partner recurrently sells oil to various consumers, e.g. farms. Their sales department struggled to find the correct timing in approaching previous clients to maximize the likelihood of a successful sale.
Python, Jupyter, Streamlit, Kedro, MLFlow, Tensorflow, Microservices, Azure, Flask, SQL
Let's innovate together!
Our project partner recurrently sells oil to various consumers, e.g. farms. Their sales department struggled to find the correct timing in approaching previous clients to maximize the likelihood of a successful sale.
Learn moreThe final app needed to be used by both, patients and doctors. While more patients would use the app, the doctors acceptance was crucial to achieve a successful market entry. Therefore, we made sure to include both sides in the initial interviews, identifying the core features.
In the end, there was a fully elaborated project plan including an architecture diagram, selection of the right technologies and frameworks, concept of the solution and site map, as well as a first visualization of the central screens. This information was the basis for the implementation of the research project.
In this project we supported a hospital to execute the requirements engineering for an app which aims to reduce patients’ overall intake of antibiotics and improving medication efficiency.
Technical Requirements Engineering, Systems Architecture, Product Development, Corporate Design, Rapid Prototyping
Let's innovate together!
In this project we supported a hospital to execute the requirements engineering for an app which aims to reduce patients’ overall intake of antibiotics and improving medication efficiency.
Learn moreThe starting point was an in-depth analysis of the therapy data provided by their application. By combining all data available as well as our project partner’s logopedic expertise, we identified the key parameters influencing the treatment process of a patient.
By analyzing, combining and visualizing the treatment data we managed to clarify the role of certain parameters like the amount of syllables in a word allowing to put users with different degrees of Aphasia into perspective, making therapy progress tangible.
In this project we supported our project partner to empirically prove their Aphasia-app’s treatment benefits as required by law for medical products.
Python, Jupyter Notebook, Streamlit, Kedro, Great Expectations
Let's innovate together!
In this project we supported our project partner to empirically prove their Aphasia-app’s treatment benefits as required by law for medical products.
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