Life Science Software Engineering

Life science software development refers to the design and implementation of digital tools that support biological research, drug discovery, clinical studies, and laboratory operations. These solutions are tailored to scientific workflows and often involve integrating experimental data, automating lab processes, and enabling advanced analytics to accelerate R&D and decision-making in biotech and pharma.

Custom software engineering helps life sciences researchers overcome key challenges by addressing data fragmentation, manual data handling, and a lack of workflow standardization. While off-the-shelf software exists, it often falls short. Researchers may need to combine multiple platforms to cover a complete workflow, leading to high licensing costs and compatibility issues. Furthermore, many commercial solutions use proprietary data formats that can lead to customer lock-in, making it difficult to transfer or analyze data elsewhere. Commercial platform manufacturers are also often inflexible, unable to add support for custom data formats or unique functionalities required by a specific research project.

Custom software provides a solution by creating scalable, domain-specific platforms that are tailored to the researcher’s exact needs. A single, custom-built system can be more cost-effective in the long run than multiple off-the-shelf licenses. It can also be designed to use open data formats, giving researchers full control and ownership of their data. By automating repetitive tasks and improving collaboration, custom software ultimately enables more reproducible and efficient science.

Software is a crucial tool for improving lab efficiency by centralizing data, enabling remote access to results, and automating key processes. By tracking samples and reagents, and standardizing experimental procedures, it reduces reliance on manual documentation and spreadsheets. This not only streamlines daily operations but also strengthens data integrity and compliance by adhering to the ALCOA principles. ALCOA stands for Attributable, Legible, Contemporaneous, Original, and Accurate, a standard for ensuring data quality and reliability. With integrated dashboards and analytics, lab personnel can monitor progress, identify bottlenecks, and ensure compliance. It also strengthens data integrity and compliance, ultimately enabling faster and more reliable scientific discovery.

Yes. Ardigen develops customized Laboratory Information Management Systems (LIMS) adapted to the specific needs of biotech, pharma, and research labs. These solutions support sample tracking, data logging, quality control, and compliance with regulatory frameworks, while also enabling integration with external databases, instruments, and analytical tools.

Ardigen applies best practices in software engineering—such as modular architecture, API-driven design, and cloud-native infrastructure—to ensure performance, reliability, and scalability. We also design robust data integration layers to support interoperability across experimental platforms, legacy systems, and cloud-based tools, enabling end-to-end traceability of research data.

These solutions is overseen by leaders like Piotr Fabijański PhD and Director of Software Engineering at Ardigen. His expertise in cloud-native architecture is key to delivering efficient and scalable biotech workflows.

Computational lab solutions refer to software systems that assist with in silico modeling, simulation, data processing, and experiment management. These tools augment traditional wet-lab approaches by enabling data-driven hypothesis generation, statistical validation, and AI-powered analysis. They play a critical role in accelerating modern life science research.