Predictive Cell Analytics from Label-Free Imaging

FateView™ is optimised for standard brightfield and phase-contrast microscopy. So you can typically begin using FateView using the microscopes you already have access to. The platform supports common image formats and works with data from a wide range of microscope manufacturers.

No. FateView™ performs non-destructive analysis, meaning cells remain viable throughout imaging and analysis. This enables longitudinal studies where the same culture can be monitored over time, revealing temporal dynamics that traditional destructive endpoint assays cannot capture.

Yes. FateView™ can integrate with automated laboratory and bioprocess workflows. Predictive analytics and metadata can be exported for use in automation systems, data platforms, or adaptive culture management pipelines. This makes FateView suitable for environments where automated decision-making and high-throughput experimentation are important.

No. FateView is designed to work with label-free imaging such as brightfield or phase-contrast microscopy.

In some cases, fluorescence-labelled samples may be used during model development to train models that predict immunofluorescence biomarkers from label-free images. Once trained, the assay can typically run using label-free imaging alone.

You retain ownership of the images and other materials you upload to FateView. We use uploaded data to operate the platform and may also use it to improve FateView, such as enhancing model performance and platform capabilities.

If your organisation has specific data-governance or retention requirements, please contact us to discuss them.

FateView™ is not yet fully GMP compliant under 21 CFR Part 11 and related regulations. Achieving formal GMP readiness is part of our product roadmap, and we are actively planning the technical, quality, and documentation steps required.

In the meantime, FateView can still support many process development and monitoring activities alongside existing GMP systems.

In GMP-regulated workflows, FateView analytics are typically used as decision-support data rather than direct release criteria. Teams can use label-free analytics to monitor trends, detect deviations, and compare process conditions while maintaining formal GMP decisions under established quality controls.

Typical applications include process development, in-process monitoring, comparability studies after process changes, and deviation investigation.

FateView predictions are generated using machine-learning models trained on large-scale time-series cell imaging datasets. Performance depends on the specific cell type, imaging conditions, and biological endpoint being analysed.

During onboarding, models can be evaluated on your datasets to ensure performance meets the requirements of your workflow.

Yes. FateView offers a free trial when you create an account. During the trial period (up to one month or until allocated trial credits are used), you can explore the platform’s core features.

After the trial ends, you may request a paid subscription to continue using the platform. If a subscription is not activated, the account will temporarily move into view-only mode before access ends.