- AI & ML
- Frontend
Our client, who deals with large, complex biomaterial datasets, needed a cloud-based platform that could handle the volume, sharpen their analytical output, and present results in a structured way. As providers of custom bioinformatics solutions, we knew the work required both technical depth and a genuine understanding of the science involved. We worked across the full stack (from UI development to back-end consultation grounded in R&D) to deliver proteomics analysis software built around the real demands of modern research.
client
NDA Protected
Germany
5-10 employees
The client is a targeted proteomics company providing research services and discovering novel biomarkers especially around sports and personal well-being using state of the art proteomics methods.
request background
Collaboration journey
We were initially engaged to design and build a graphical interface for an existing back-end system. The client works in the bioscience sector and needed a UI that would allow researchers and analysts to make sense of complex data without grappling with confusing settings.
Over time, the relationship expanded well beyond that original brief. Our R&D team took on the processing and analysis of biomaterials, incorporating machine learning to handle tasks that would be impractical to perform manually. We also worked closely with the client's technical team on back-end performance, tracing inefficiencies in the system architecture and recommending changes that meaningfully cut processing time and resource load.
What began as a UI project became an end-to-end collaboration. Each stage informed the next, and the result was a suite of bioinformatics data solutions that served the client's needs on both the technical and scientific sides.
challenge
Fighting challenges
To deliver this project swiftly, we had to deal with compounding obstacles. The most persistent was restricted communication with the external back-end developer, whose low engagement slowed information exchange considerably. Aligning on data contracts, API specifications, and integration timelines became a drawn-out process, forcing our team to fill gaps independently and introduce additional risk into each development sprint.
Stakeholder engagement presented an equally significant hurdle during the UAT phase. Iterations accumulated without clear sign-off criteria, and priority shifts arrived without structured input. Still, we managed to move confidently toward delivery.
From a technical standpoint, processing large-scale biomaterial datasets demanded precision. Building data analysis software for proteomics capable of handling high-throughput inputs while maintaining accuracy required constant performance tuning and careful architectural decisions. Any misstep in processing logic risked downstream errors in analytical results.
goals
- Integrate AI and ML capabilities to automate biomaterial analysis and improve output accuracy.
- Optimize the back-end architecture to reduce processing latency and improve overall throughput.
- Streamline and accelerate analysis processing across the full data pipeline.
- Design and deliver an intuitive graphical interface for research data visualization.
- Establish structured communication and testing protocols to align all project stakeholders.
solution
Our working path
ReactJS, Python, NodeJS, AWS
1+ years- 3 specialists
We decided to organize the whole development process in accordance with Scrum methodology, which created a single approach for task lifecycle management. Daily stand-ups gave us smooth communication between team members and external back-end developers, while periodic demos gave our clients an understanding of the stage of the project and the ability to suggest changes in product priorities more effectively. Together, these practices enabled us to deliver the following custom bioinformatics solutions:
- Designed and developed a graphical user interface tailored to bioscience research workflows.
- Integrated AI and ML models to automate biomaterial processing and pattern recognition.
- Consulted on and implemented back-end optimizations to reduce query latency and improve throughput.
- Deployed cloud-based infrastructure to support scalable, high-volume data processing.
- Built proteomics analysis software modules capable of handling high-throughput biological datasets.
- Established real-time data pipelines connecting front-end dashboards to live biological data sources.
- Introduced role-based access controls and audit logging to protect sensitive research data.
- Delivered training and documentation to ensure seamless adoption across technical and research teams.
outcome
No obstacles can stop us
- Reduced the project time to launch by 30%
- Increased analysis processing accuracy by 40%
- Reduced analysis processing time by 38%
- Delivered a unified platform that anchors the client's ongoing bioinformatics data solutions
- Provided scalable data analysis software for proteomics.
- Positioned the team to grow their workflows without friction.
- 30% Reduced the project time to launch
- 40% Increase in the analysis processing accuracy
- 38% Analysis processing time reduced
