The reasoning behind your team's scientific decisions shouldn't live in old slide decks, email threads, and departed colleagues' heads. VeritasBio turns your evidence, expert judgment, and AI output into decisions you can trace, defend, and keep current as the science moves.
If your assessments live in slide decks, shared drives, and meeting memories, you already know these moments.
The rationale behind last year's target call lives in a slide deck, an email thread, and two people's memories — one of whom left in March.
Every new assessment starts with the same literature search your colleagues already did — because last time's work isn't searchable or reusable.
Evidence gets corrected, contradicted, retracted. Nothing connects it to the decisions that relied on it — so approved calls quietly go stale.
Criteria drift between teams and quarters. Assessments can't be compared, and every review restarts the same arguments from zero.
AI summaries are everywhere, but nobody can show which evidence supports them — so they never make it into a decision leadership will sign.
Who reviewed it? Who approved it, and on what basis? The audit trail is a calendar invite, a thumbs-up, and hope.
A supporting study surfaces in a meeting, lives on in a follow-up thread, and never reaches the assessment — so a promising program is shelved, and nobody connects the dots until a competitor does. In VeritasBio, every finding attaches to the decisions it affects the moment it enters your evidence memory: readiness moves, the owner is alerted, and nothing valuable dies in an inbox.
None of this means your team isn't careful. It means the reasoning behind your decisions has no system of its own — it borrows whatever tool the meeting happened in. That's the gap VeritasBio closes.
VeritasBio doesn't replace your ELN, your data lake, or your AI tools — and it doesn't ask your team to change how it thinks. It adds the missing layer where evidence, expert judgment, and AI output come together into a decision your team builds, reviews, and signs in one place.
Every paper, dataset, experiment, and AI output becomes a versioned, searchable, semantically linked evidence object. Nothing valuable gets lost — and nothing gets re-discovered twice.
Configurable decision dimensions, scoring anchors, and quality gates turn ad-hoc judgment into organizational decision standards — versioned, so old calls are read under the rules of their time.
Teams build the decision together — claims, citations, scores, AI assistance, reviewer rounds — with a Decision Readiness Score that flags gaps and contradictions before anyone signs.
Approved decisions freeze into immutable, replayable records. When the science moves, impact tracing shows exactly which decisions are affected — and calls them back for review.
Evidence → Standards → Governed decision → Living ledger
Every claim links to the exact evidence version, every score to your rubric, every approval to an e-signature. When a partner, auditor, or your own leadership asks "why did we decide this?" — the answer is one click, not archaeology.
Evidence is captured once — versioned, searchable, semantically linked — and reused across assessments. New decisions start from your organization's memory instead of a blank literature search.
When evidence is retracted, corrected, or contradicted, impact tracing flags every affected claim, score, and decision — and calls it back for re-review before it hurts you.
AI drafting and synthesis with citations kept, review gates enforced, and override reasons preserved — on the model and infrastructure you choose, including fully self-hosted, so your science never leaves your walls.
And it compounds: every signed decision, every reused evidence object makes the next call faster — your team's judgment becomes an asset that outlasts any slide deck, and any departure.
We're partnering with a small number of biotech and pharma R&D teams who feel this problem acutely. Design partners get the platform early, shape the roadmap weekly, and build their institutional decision memory before anyone else.