About AnnieGuard
Built for biological questions prediction alone cannot resolve.
AnnieGuard is an independent scientific research company specializing in causal intelligence and computational biology for pharmaceutical and biotechnologyÂ
R&D.
Why AnnieGuard Exists
Modern R&D can generate biological signals faster than teams can causally interpret them. AI, omics, computational models, and observational evidence can surface compelling hypotheses without establishing which biological explanation is actually supported.
AnnieGuard was built to address that gap.
Built for High-Uncertainty R&D
AnnieGuard works across genomic, molecular, clinical, longitudinal, and multimodal evidence to investigate therapeutic targets, biomarkers, disease mechanisms, patient states, and competing causal explanations.
Our research engagements are designed for questions where substantial evidence may already exist, but uncertainty remains about what that evidence actually supports.
What Makes AnnieGuard Different
Causal, not merely predictive
We evaluate what the evidence can support about biological structure and mechanism.
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Designed for uncertainty
Missingness, conflicting evidence, and competing explanations are treated as part of the scientific problem.
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Built for heterogeneous evidence
We work across genomic, molecular, clinical, longitudinal, and multimodal sources.
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Decision-relevant by design
Findings are structured around what is supported, what remains unresolved, and what evidence should come next.
Tiara Jamison leads AnnieGuard’s scientific research, causal methodology development, and biopharma research engagements. Her background spans more than a decade in technology and computational systems, shaping a systems-engineering approach to complex biological evidence, uncertainty, and causal structure. Her work focuses on high-uncertainty disease settings where predictive or correlational methods do not fully resolve the underlying biology. She founded AnnieGuard after losing her mother to a rare sarcoma, with a long-term focus on improving how difficult disease biology is reconstructed and translated into therapeutic development.
Founder, CEO & Principal Scientist
Tiara Jamison
Our Scientific Approach
AnnieGuard begins with a simple principle: biological evidence should not support stronger conclusions than the evidence itself allows.
Rather than treating correlation, prediction, model confidence, or plausibility as evidence of causality, we evaluate competing explanations, uncertainty, temporal relationships, and structure across genomic, molecular, clinical, longitudinal, and multimodal evidence.
Our proprietary causal inference and causal reconstruction methods are designed to distinguish supported conclusions from unresolved possibilities and identify the evidence most likely to change the interpretation.
The goal is not to generate more analysis. It is to produce a more defensible scientific basis for the next R&D decision.
The underlying methodology is proprietary and confidential.