How BioSniper verifies every number
A catalyst date tells you when. An investigation must also establish what the event is, what it could change, and which primary records support that view. BioSniper keeps that evidence attached at every layer — here is exactly how.
7 authoritative public sources
BioSniper aggregates only public, authoritative datasets. Data availability reflects the latest successfully ingested source records; source and as-of dates are shown where available. Nothing is scraped from opinion sites or social media into the evidence base.
- SEC EDGAR
10-K, 10-Q, 8-K, S-1 filings & insider transactions
- FDA
PDUFA dates, approvals, CRLs & Advisory Committee meetings
- ClinicalTrials.gov
Trial registrations, status changes & readouts
- PubMed
Peer-reviewed scientific literature
- Patents
USPTO & Google Patents via BigQuery
- News
Biotech & pharma news with source timestamps
- Market Data
Stock quotes, ETFs & market indicators with observed timestamps
Citation enforcement — no evidence, no claim
Every AI-generated statement on BioSniper must cite the source it came from — inline tags like [sec], [clinical] or [fda] point at the document the model actually read.
Quoted evidence is held to a stricter bar: quotes must appear verbatim in the source text. After the model answers, an independent verification step re-checks every quote against the retrieved documents, character for character. A quote that cannot be found is discarded before it ever reaches a page — regardless of how plausible it sounds.
And when the evidence simply is not there, BioSniper withholds the claim instead of guessing. Missing evidence is never estimated, interpolated, or filled with a model’s best guess.
The citation-verification pass rate shown above is computed live from our verification logs over the past 7 days — it is a measurement, not a marketing claim.
Automated correctness checks, every day
Ingesting data is the easy part; keeping it correct is not. A daily automated audit sweeps the entire dataset across more than a dozen correctness dimensions — freshness, duplicates, referential integrity, misattribution (a real number attached to the wrong company), fabricated-success patterns, field quality, and coverage across every actively listed company.
Failures page the team automatically. When an upstream source silently changes its format — regulators do this without notice — these checks are how we find out before you do.
Frequently asked questions
- Where does BioSniper data come from?
- BioSniper aggregates only public, authoritative datasets — SEC EDGAR, FDA, ClinicalTrials.gov, PubMed, patents, news, and market data. Data availability reflects the latest successfully ingested source records; source and as-of dates are shown where available. Nothing is scraped from opinion sites or social media into the evidence base.
- How are AI citations verified?
- Every AI-generated statement must cite the document the model actually read, and quoted evidence must appear verbatim in the source text. An independent verification step re-checks every quote against the retrieved documents character for character; a quote that cannot be found is discarded before it ever reaches a page.
- What happens when there is no evidence for a claim?
- BioSniper withholds the claim instead of guessing. Missing evidence is never estimated, interpolated, or filled with a model’s best guess.
- How is data correctness maintained over time?
- A daily automated audit sweeps the entire dataset across more than a dozen correctness dimensions — freshness, duplicates, referential integrity, misattribution, fabricated-success patterns, field quality, and coverage across every actively listed company. Failures page the team automatically.
Apply the evidence standard
Apply this evidence standard to a biotech question
Turn the citation, version, and decision checks described above into the acceptance rules for a real company, drug, or catalyst.