Summary
Key results
Treatment effectiveness increased from 25% to 75% thanks to platform stability and data quality
Maintenance of the AI-controlled microscope’s source code
Quality and predictability in a scaling medical solution
Mojo’s platform operates directly in the medical diagnostics flow. As deployments multiplied and the product evolved quickly, the risk grew that manual testing and fragmented environments would not provide sufficient control over software quality.
The company needed a model that would:
- Ensure repeatable quality of analytical results
- Reduce the risk of errors in clinical environments
- Let teams advance the product without slowing innovation
- Enable safe rollouts to additional laboratories
The priority was to move from manual, reactive testing to an automated, predictable quality-control process integral to AI platform development.
End-to-end automation of quality and delivery
Sii Poland assumed full responsibility for standardizing and automating test processes for the AI microscope control software and the diagnostic support applications. The engagement covered procedure analysis, test-tool development, CI/CD pipeline optimization, and cloud-deployment readiness.
Scope included:
- Building a test framework in PyTest and unit-test coverage for key application components
- Creating a system-testing module using Robot Framework
- Preparing a CI/CD environment integrated with automated tests and reporting
- Developing a GUI application with PySide and Anvil
- Deploying and operating applications in Microsoft Azure
- Maintaining and enhancing the AI microscope source code and rapidly fixing issues reported by operators
With a unified testing process and CI/CD in place, Mojo’s engineering teams gained full visibility into progress and software quality in every release cycle.
Stability, scalability, and faster innovation in fertility care
Automated testing and a disciplined release process turned a fast-moving product into a stable, predictable medical platform ready to run across many labs simultaneously. Clinics now rely on robust AI-microscope software that reduces the risk of analysis interruptions and ensures consistent results across sites – crucial for scaling diagnostics.
For Mojo’s teams, predictable releases and full test automation shortened deployment times and eliminated manual-work errors, allowing focus on advancing AI algorithms instead of firefighting production issues. Every change is validated in a controlled way, significantly reducing regression risk.
Most importantly, patients benefit. Platform stability and data quality translate into more accurate sample analyses, shorter turnaround for results, and greater predictability across the treatment journey. In practice, this means higher therapy success rates and stronger trust in the technology supporting clinical decisions – especially vital in infertility treatment, where every decision carries major emotional and medical weight.