Our Technology
Building the next generation of intelligent biological systems
Precision medicine requires not only pattern recognition, but also systems that understand context, learn from limited data, explain their reasoning, and improve themselves as new information emerges. Neovivum develops adaptive intelligence technologies that transform how researchers, clinicians, and pharmaceutical partners model and predict the behavior of complex biological systems.
Our approach bridges cutting-edge AI research with the practical demands of regulated healthcare and drug development, in order to create tools that are scientifically rigorous, computationally feasible, and clinically deployable.
our core capabilities
Context-Aware Intelligence
Spatial, temporal, and microenvironmental context
Minimal Data Learning
One-shot and few-shot learning for small cohorts
Continuous Self-Improvement
Runtime ontology updating as new evidence emerges
Explainability by Design
Auditable biological reasoning and uncertainty
Multi-Modal Data Integration
Imaging, omics, organoids, and clinical outcomes
technology in detail
Context-Aware Intelligence
Biological systems are fundamentally contextual. The same genetic mutation means different things in different tumor microenvironments, at different disease stages, or in different patient populations. Traditional models treat parameters as fixed. Our systems recognize that biological rules are conditional; cell behavior depends on what else is happening around it.
Our context-aware framework integrates spatial data (where cells are positioned), temporal dynamics (how microenvironments change under treatment), and multi-modal information (images, omics, clinical history) into unified models that capture this complexity. This is critical for applications like CancerScan, where predicting treatment resistance requires understanding not just tumor mutations but how the entire cellular ecosystem communicates and adapts.
Pharma Impact: Predict how patient populations with seemingly identical biomarkers will respond differently to the same drug based on microenvironmental context.
Minimal Data Learning
Pharmaceutical development often requires predictions from limited examples: rare cancer subtypes, new drug mechanisms, small patient cohorts. Standard machine learning fails at scale in these scenarios. Our minimal-data learning approach leverages evolutionary principles and knowledge graph integration to build accurate predictions from few examples.
We employ one-shot and few-shot learning methodologies—enabling the system to understand novel biological concepts from minimal data by leveraging prior knowledge and pattern recognition. This is particularly powerful when combined with our knowledge graph integration, where scientific literature and existing experimental evidence augment scarce patient-level data.
Pharma Impact: Accelerate drug development timelines by making confident predictions with small Phase I/II cohorts. Deploy models to new patient populations or disease variants without requiring massive retraining datasets.
Continuous Self-Improvement
Most AI systems in healthcare are static: train once, deploy forever. But biology evolves, patient populations change, and new evidence emerges. Our systems incorporate runtime ontology updating—the ability to refine their own knowledge structures and decision logic as new data arrives, without requiring complete retraining.
This means digital twins improve their predictions over time as they encounter new patients. Models adapt to emerging resistance mechanisms as they’re observed. And regulatory knowledge updates flow through the system automatically, keeping predictions aligned with the latest clinical evidence.
Pharma Impact: Models that grow more accurate with each patient interaction. Regulatory compliance built in continuously, not retrofitted in audits.
Explainability by Design
Regulators require transparency. Clinicians need to understand why a system recommends a particular treatment. Our technology prioritizes mechanistic interpretability—predictions are grounded in biological reasoning chains that can be audited and understood.
Unlike black-box approaches, our digital twins make explicit the biological assumptions, knowledge sources, and data inputs driving each prediction. This explainability extends to uncertainty quantification: we provide confidence metrics and ensemble predictions that characterize the range of possible outcomes, enabling clinicians to make informed risk assessments.
Pharma Impact: Regulatory pathways accelerated by demonstrating scientifically principled decision-making. Clinical adoption enabled by systems that clinicians can trust and understand.
Multi-Modal Data Integration
Biological understanding requires integration across data types: high-resolution tissue images, single-cell sequencing, protein measurements, imaging (CT, MRI), clinical outcomes, drug efficacy data. Most systems handle one or two modalities well. The challenge is seamless integration without data silos.
Our architecture uses knowledge graphs as a unifying semantic layer, enabling different data types to inform the same underlying biological model. A pathology slide contributes cellular composition and spatial organization. Single-cell RNA provides molecular identity and signaling state. Clinical records provide treatment history and outcomes. All inform the same digital twin without requiring each data stream to be processed identically.
Pharma Impact: Leverage all available patient data simultaneously. Identify predictive signals that emerge only from multi-modal integration. Deploy models that work across hospitals with different imaging hardware and lab equipment.
applications in action
Tumor Digital Twins
Neovivum leads development of the CancerScan digital twin framework—learning how tumor microenvironments adapt when exposed to treatment pressure. The project integrates pathology image analysis, single-cell transcriptomics, organoid experiments, and clinical outcomes into a unified predictive model. The goal: predicting which patients will develop therapy resistance before it emerges, enabling proactive treatment modification.
This application exemplifies all five core capabilities: context-aware simulation of tumor microenvironments, minimal-data learning from small patient cohorts, continuous improvement as new patient data arrives, explicit mechanistic reasoning (proto-grammar of tumor communication), and integration of images, omics, and organoid data.
Longitudinal Breast Cancer Predictions
Extending our approach to breast cancer, ABIGAIL4D develops longitudinal digital twins that predict disease progression over months and years. The system learns from patient-derived organoids, longitudinal imaging, and molecular profiling to forecast which patients will develop metastatic disease and which will remain stable long-term.
Why This Matters
The gap between research AI and clinical AI remains enormous. Thousands of papers describe models with impressive accuracy on public datasets, but few translate into hospital deployment. The reason: research systems don’t account for contextual variability, they require massive training data, they’re opaque to clinicians, and they fail when encountering new patient populations.
Neovivum’s technology was designed from first principles to close that gap. Our systems are built for the constraints and demands of real clinical environments: limited training data, regulatory scrutiny, populations that shift over time, and the absolute requirement that clinicians understand and trust the system.
We apply this approach wherever complex biological systems must be modeled and predicted: oncology, infectious diseases, immunology, pharmacology. We begin where the need is greatest and the data richest; precision medicine, where patient-specific prediction can save lives.