Understand the behaviour of your cells when exposed to diverse perturbations, including environmental, pharmacological, and viral, across 2D monolayers, 3D culture systems such as spheroids and organoids, and time-resolved live-cell imaging.

By carefully weighing your available data, endpoints of interest, and other constraints, we design and implement a custom algorithm that brings clear and interpretable insight, whether the challenge involves complex cell types, volumetric quantification, or tracking dynamic cellular processes over time.

For example, cell painting images can be used to predict the mechanism of action for each drug in a high-content screen, or detect early signatures of stress indicative of toxicity. Equally, live-cell data can be analysed to quantify migration, proliferation, and death kinetics that static snapshots cannot capture. This ensures that candidate compounds are more rigorously vetted before passing to the next testing stage, saving precious resources and time.

Biological systems generate data across fundamentally different modalities: imaging, genomics, transcriptomics, proteomics, clinical records, and sensor outputs. Each captures a partial view of the underlying biology. Multi-modal fusion is the principled integration of these heterogeneous data streams to construct a more complete representation than any single modality can provide.

This is not simply concatenating datasets. Naive combination frequently degrades rather than improves performance. Each data type carries its own noise profile, dimensionality, sparsity pattern, and sampling bias. Aligning these representations, handling missing modalities, and learning when to weight each contribution are non-trivial technical challenges that demand careful architectural choices.

We design fusion strategies (early, late, and intermediate) tailored to the biological question, the available data, and the downstream application, extracting signal that is genuinely synergistic across modalities rather than redundant or contradictory.

Optimise your process for yield, efficiency, cell viability or batch reproducibility. We apply machine learning to your process data to identify critical parameter interactions, predict optimal operating conditions and detect early deviations from target performance. Whether you are scaling from bench to bioreactor or troubleshooting batch-to-batch variability, our models integrate multivariate process analytics with real-time sensor data to deliver actionable, data-driven recommendations.

We apply advanced analytical and AI methods to clinical trial data and real-world data (RWD) sourced from electronic health records, registries, claims databases, and patient-reported outcomes. Our work supports evidence generation across the drug development lifecycle, from early-phase signal detection through post-marketing surveillance and the generation of real-world evidence to support health technology assessment and regulatory decision-making.

Our capabilities include the design and execution of retrospective and prospective observational studies, comparative effectiveness research, and the development of predictive models for patient stratification, treatment response, and disease progression. We can integrate multi-modal data sources to strengthen the evidence base where single data streams are insufficient.