A funding decision turns on whether the claims actually read on real products, and how defensibly. ClaimHit screens the asset against the live market, ranks what it finds by how strong the evidence really is, and shows the excerpt behind every line — including, deliberately, the limitations it could not evidence.
Targets are sorted by what could actually be verified — confirmed on a manufacturer's own page, or agreed by the models but unconfirmed. The distinction is stated, not blurred, so the strong cases separate from the thin ones.
Every claim is mapped element by element. PARTIAL is never rounded up to FULL and an undocumented limitation is reported NONE, so you see the vulnerability before you underwrite it rather than after.
Run candidate assets in batch through the same pipeline, so the comparison across cases is like-for-like rather than a sample judged by feel.
Citations are validated against the passage they claim to come from; a plausible reference that does not contain the support is rejected, not shipped. The evidence trail is one your own counsel can check.
Candidates come from several models, live web and neural search, forward citations, and the authoritative registries for the field — but breadth only widens the net. Each one is still checked against real, citable evidence, and anything that cannot be evidenced is dropped rather than shown with a caveat. See every source →
ClaimHit produces preliminary research, not a merits opinion or legal advice. The point is to reach the diligence conversation with the evidence — and the gaps — already on the table.
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