Algorithm development services

Yes. We develop custom algorithms for laboratory software, diagnostic systems, medical devices, and other life sciences applications.

Our team combines software engineering with expertise in life sciences, diagnostics, and laboratory workflows. This helps ensure that algorithms are scientifically sound, production-ready, and suitable for their intended use. For regulated applications, we also consider risk management, traceability, verification and validation, and applicable requirements for medical device or AI-based software.

Yes. Many innovative algorithms begin as research code, proof-of-concept models, or scripts developed in research environments. Turning them into reliable, scalable, and maintainable software suitable for commercial use is a different challenge.

At BioSistemika, we refine and test the algorithm, improve its performance, and integrate it into laboratory software, instruments, cloud platforms, or other systems. For regulated applications, we also consider documentation, traceability, validation, and compliance requirements.

Our interdisciplinary team combines software engineering with expertise in life sciences and diagnostics, ensuring that the scientific integrity of the original concept is preserved while delivering software that is ready for real-world use.

It depends on the problem, available data, performance needs, and requirements for explainability, reproducibility, and validation.

AI and machine learning can be valuable for complex pattern recognition and prediction, but they are not always the best option. In some applications, statistical, rule-based, or signal-processing methods may be more reliable, easier to explain, and simpler to validate. For AI-based solutions, applicable requirements such as the EU AI Act may also influence the development approach.

We help select the approach that offers the best balance of performance, robustness, maintainability, and regulatory suitability.

It can. Whether the EU AI Act applies, and which requirements apply, depends on how the AI system is used and classified. Certain AI systems used as safety components of regulated products, including some medical devices and IVDs, can fall into the high-risk AI category under the EU AI Act.

This can introduce requirements related to areas such as risk management, data governance, technical documentation, record-keeping, human oversight, accuracy, robustness, and cybersecurity. For medical device software, these requirements may need to be considered alongside MDR or IVDR and the applicable medical device software standards.

Algorithm validation is essential in regulated industries to ensure that results are reliable, reproducible, and fit for their intended use.

We validate algorithms against predefined requirements and acceptance criteria using representative data, including expected conditions and relevant edge cases.

Depending on the algorithm, we evaluate measures such as accuracy, sensitivity, specificity, error rates, robustness, or processing performance. For regulated applications, we also document the results and maintain traceability between requirements, risks, implementation, and test evidence.

Building long-term partnerships requires trust. We understand that your algorithms, data, and know-how are valuable assets, and we treat them with strict confidentiality.

Intellectual property ownership is agreed at the beginning of each project and clearly defined in the contract. For custom development, clients typically retain ownership of the algorithms, software, and deliverables created specifically for their project. Confidential information is also protected through NDAs, secure development practices, and controlled access.

Yes. If you already have an algorithm, research prototype, script, or working implementation, we can review it and help improve its accuracy, performance, robustness, maintainability, or scalability.

We can also identify technical limitations and edge cases, assess whether it is suitable for its intended use, and review its documentation and test evidence. This is often more efficient than starting again when the scientific approach is sound, but the implementation needs to be strengthened for production or regulated use.