Decide with clarity under uncertainty

We help startups, biotech founders, and academic teams make high-stakes biological and translational decisions before irreversible investments in data generation, platform selection, or programme commitments are made.

• Platform viability and readiness assessment for specific biological questions

• Independent evaluation of emerging technologies and their suitability for a defined experimental objective

• Selection of appropriate gene panels, measurement modalities, and spatial transcriptomics strategies

• Comparative analysis of technological strengths, limitations, and trade-offs for a given research task

• Assessment of data sufficiency, required sample sizes, and evidentiary thresholds for robust conclusions

• Evaluation of platform adaptability when research questions evolve or expand

• Identification of technical and analytical risks before large-scale experimental investment

Typical output: Structured decision memo with risk mapping and recommendation pathway.

Experimental design grounded in biological and statistical rigor

Rapidly evolving technologies are transforming how biological science. We help teams to adopt accordingly to efficiently translate biological questions into experimental strategies that are technically feasible, statistically robust, and aligned with the capabilities and limitations of modern experimental and analytical technologies.

 Our advisory support focuses on designing technologically intensive experiments to ensure high quality data.

• Fit-for-purpose selection of experimental technologies and measurement modalities

• Study design architecture, including sample size evaluation and statistical power strategy

• Pre-execution feasibility assessment and identification of technical or analytical risks

• Design of cost-effective pilot experiments to validate experimental assumptions

• Evaluation of protocols for robustness, reproducibility, and adaptability

• Clarification of hypotheses and mapping of biological and experimental uncertainty

• Planning experimental scalability while controlling cost and operational complexity

Typical output:‍ ‍Technology-aware scalable experimental design for complex immunlogical questions

Integrate complexity without losing mechanism

Modern biological programmes generate increasingly complex, multi-modal datasets. We help teams design analytical and AI frameworks that extract meaningful biological insight while remaining interpretable, scalable, and mechanistically grounded.

Our advisory support focuses on aligning data architecture, computational methods, and biological interpretation from the outset.

• Alignment and integration strategies across heterogeneous biological data modalities

• Evaluation of analytical frameworks and computational tools for specific biological questions

• Design of data architectures that enable scalable cross-modality analysis

• Pre-analysis planning prior to data acquisition to ensure analytical tractability

• Selection and validation of appropriate AI and machine learning methodologies

• Safeguards for interpretability, robustness, and generalisability of computational models

• Linking computational outputs back to mechanistic biological hypotheses

Typical output: robust data integration and interpretability framework.