Semantic Foundations for AI in Life Sciences
Bridge siloed biology data and business systems with semantic technologies that unlock AI readiness
Strategic design of knowledge representations that unify biology data across departments and partners, making implicit scientific relationships explicit and actionable
Semantic Data Contracts that establish standards for data quality, harmonization, and interoperability—ensuring data flows reliably across departments and partner ecosystems
Roadmap to position your organization for both machine learning and symbolic AI systems—leveraging structured biology data for maximum model performance and interpretability
Strategic planning for data flow across your organization and partnerships. We design workflows that minimize manual transformation, accelerate critical handoffs, and establish patterns that compress timelines by months.
Leadership on data interoperability with external partners. Through semantic contracts and governance frameworks, we reduce manual reconciliation from weeks to minutes, enabling faster decision-making and delivering 40–60% efficiency gains.
Life sciences organizations invest heavily in AI, but most efforts stall at the data layer. Biology data remains fragmented, siloed between departments and labs, and defined by inconsistent or underutilized semantic models. This creates the AI Data Paradox: immense data volume meets zero semantic coherence, resulting in costly delays and missed opportunities. SignaMind eliminates this friction.
Founder & Principal Consultant
Shawn is a data and life sciences strategist with cross-disciplinary expertise in ontology-based data management, knowledge graphs, and FAIR data principles.
With a PhD in Neuroscience and having worked in some of the leading pharmas and researhc institutes including Novo Nordisk and EMBL-EBI, Shawn bridges scientific rigor with enterprise data strategy—translating complex biomedical challenges into scalable solutions that unlock AI value both from a technical and business perspective.