AI can accelerate the organization and exploration of scientific information. But meaningful translational decisions still depend on biological context, evidence quality, critical reasoning, and expert judgment.
Our approach is designed to bring these elements into a structured process.
Working Principles
01 — Question-Led
We begin with the decision to be informed, rather than collecting information without a defined purpose.
02 — Evidence-Centered
We prioritize the quality, relevance, and limitations of the underlying scientific evidence.
03 — AI-Enabled, Human-Reviewed
Appropriate computational and AI-assisted workflows may support information organization, synthesis, and exploration. Material interpretations and external deliverables require qualified human review.
04 — Uncertainty-Aware
We make assumptions, limitations, conflicting findings, and missing evidence visible rather than concealing them behind simplified conclusions.
05 — Decision-Oriented
The aim is not merely to summarize information, but to help define the questions and actions that matter next.
Example Workflow
- Step 1 — Frame the Question
- Step 2 — Identify and Organize Evidence
- Step 3 — Evaluate Strength and Relevance
- Step 4 — Map Risks and Unknowns
- Step 5 — Develop a Decision-Oriented Synthesis
- Step 6 — Define Next Validation Priorities
Responsible AI and Data Handling
We emphasize responsible use of AI, source traceability, confidentiality, and appropriate human oversight.
Specific tools, datasets, workflows, and information-handling requirements must be agreed within each project.