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Zetesis Scientific Due Diligence

Due diligence on a high-dimensional scientific claim, graded dimension by dimension against the published record. Give it a claim, a paper, an abstract or a pitch deck. It works out what would have to be true for the claim to hold, then reports what the evidence shows on each of those dimensions, what it does not show, and the ways claims like it have failed before. Every source it cites was retrieved from a public register, with a real identifier attached. Nothing is made up. Built for the moment a scientific claim is about to carry a decision: licensing a technology, funding a program, in-licensing an asset, citing a paper, or working out whether a company's stated result means what it sounds like. **No account or token needed for the Zetesis server itself.** Connecting through Smithery uses your Smithery credentials. ## What it covers Genomics and Mendelian randomisation, single-cell, bulk omics, CRISPR screens, clinical trials, real-world evidence, AI clinical decision support, diagnostics, preclinical models, cell and gene therapy, and structural biology. ## What it looks at Europe PMC, ClinicalTrials.gov, openFDA, NIH RePORTER and SEC EDGAR. Published papers, registered trials, regulatory records, grant funding and public company filings. Every source is checked against retraction and correction notices, and the notice is cited when one exists. A reading built on a withdrawn paper is the exact error this exists to prevent. ## The tools - `zetesis_scope` works out what kind of claim this is and what would have to hold for it to be true, including the ways claims like it have failed before. - `zetesis_evidence` goes and finds the evidence, with an identifier on every source. - `evaluate_claim` produces a graded reading, for when you want the assessment itself rather than the evidence to read yourself. - `verify_attestation` re-checks a signed Zetesis record for tampering. **The first two run no AI model at all.** They return in about a second, cost nothing, and send nothing to any AI provider. Your assistant does the thinking, with real sources in front of it. ## Judging a claim by what was known at the time You can ask about a claim as it stood in an earlier year, and only evidence that existed by then comes back. Hindsight is fenced out. This sharpens the answer rather than just making it fairer. On a test claim, an open search missed the one publication that mattered and reached 35% coverage of the relevant evidence. Fenced to the year the claim was made, the same searches found it and coverage reached 79%. ## Try it Ask what the published evidence actually supported about aducanumab and cognitive decline at the end of 2019, using only sources available by then. Then ask the same question without the year. The difference is the point.

First seen 2 Oct 2026. Evidence as of 2 Nov 2026.