The most important decisions in biotech are still:
- Assembled manually
- Hard to reproduce
- And impossible to revisit with confidence
Discovery teams are producing more evidence, more analyses, and more AI-assisted recommendations than ever before. But the decisions that matter most are still assembled across slide decks, spreadsheets, PDFs, papers, internal notes, expert conversations, and disconnected tools.
VeritasBio gives teams a structured workspace for high-stakes scientific decisions. Instead of rebuilding every assessment from scratch, teams can collect evidence, apply a rubric, create a decision packet, review it with stakeholders, and preserve the full reasoning trail over time.
Instead of rebuilding every decision from scratch, teams follow a repeatable workflow:
Bring together public, internal, computational, and AI-generated evidence in one reusable workspace.
Define the decision question, context, subject, and intended use — target assessment, candidate nomination, asset diligence, or AI recommendation review.
Standardize evaluation criteria while preserving expert judgment across teams and programs.
Link evidence directly to claims, scores, reasoning, gaps, and final verdicts in one structured object.
Track comments, changes, approvals, diffs, and decision history with full traceability.
Accelerate drafting and synthesis while keeping humans responsible for review, override, and final approval.
What once took weeks across spreadsheets, emails, and slide decks now becomes a single structured decision asset - fully traceable, reviewable, and ready for governance.
Before
Weeks of Scattered Work The KRAS G12C assessment lived across a biology team's spreadsheets, email threads, slide decks, and lab notebook excerpts. The team could assemble the story - but not prove how each conclusion was reached.
After
One Structured Decision Packet Cell-line data, biomarker rationale, selectivity insights, and expert judgment are all linked directly to each claim. Reviewers can move from any conclusion to the exact supporting evidence in one step. Governance-Ready by Default Rubric scores, reviewer comments, approval history, and version diffs sit in the same record - making it easy to defend the call, or challenge it with precision. Revisitable as New Data Arrives When new data changes the picture, a new version of the packet is created. The previous assessment is preserved, the new reasoning is added, and the full decision history remains intact.
Same science. Completely different clarity
VeritasBio fills the gap between where scientific evidence lives and where decisions need to be governed - with a purpose-built set of features for high-stakes R&D teams.
Search, filter, organize, monitor, and reuse scientific evidence across decisions and programs.
Create structured decision objects for target assessment, candidate nomination, external asset diligence, and AI recommendation review.
Use configurable scoring criteria and governance rules to make decisions more consistent across teams.
Track how evidence, reasoning, scores, and verdicts change over time with full version history.
Review AI-assisted recommendations with traceability, contradiction checks, human disposition, and override rationale.
Turn past decisions into searchable institutional memory across programs and teams.
VeritasBio is built for teams that make repeated, high-value scientific decisions and need those decisions to be easier to review, defend, reuse, and improve.
For discovery teams moving fast and needing defensible decisions without building internal infrastructure from scratch.
For teams using AI-generated outputs, model recommendations, and automated evidence synthesis that need human review and traceability.
For organizations delivering structured scientific assessments, diligence work, or target and candidate evaluation at scale.
For teams modernizing decision workflows and improving governance around AI-assisted scientific work.
VeritasBio supports the full range of high-stakes scientific decision workflows — from early target selection to AI recommendation review.
Search, filter, save, organize, monitor, and reuse scientific evidence across programs.
Build structured, evidence-backed packets for target selection and validation.
Support nomination and advancement decisions with scored, reviewable rationale.
Evaluate opportunities through a repeatable, evidence-linked diligence process.
Submit packets, collect comments, manage reviewers, and track approvals end-to-end.
Use AI for synthesis and drafting while preserving evidence links and human accountability.
Apply consistent scoring criteria across teams, programs, and decision types.
Search, compare, and reuse prior decisions as institutional memory.
Track how evidence, reasoning, scores, and verdicts changed over time.
Revisit decisions after new evidence or results and improve future judgment.
Scientific teams can now generate hypotheses, summaries, rankings, and analyses faster than ever. But faster output does not automatically create better decisions.
As R&D becomes more AI-native, organizations need a way to preserve what evidence was used, how recommendations were reviewed, who approved the final call, and what changed over time.
Scientific teams work across increasingly fragmented sources — from literature and public databases to internal experiments and computational outputs.
AI can accelerate synthesis, but teams still need to validate outputs, identify unsupported claims, and preserve human accountability.
Regulated R&D environments increasingly value traceability, documentation, reproducibility, and audit-ready records.
Scientific R&D has changed. Discovery teams now have access to more data, more models, more publications, more computational outputs, and more AI-assisted recommendations than any previous generation of scientists. But the way teams make decisions has not changed nearly enough.
Important calls — which target to pursue, which candidate to advance, which external asset to diligence, which AI recommendation to trust — are still too often captured in slide decks, spreadsheets, email threads, and scattered documents. The final verdict may be remembered, but the reasoning behind it becomes fragile.
Every major decision should preserve the evidence, assumptions, rubric, and reasoning behind it.
Teams should be able to revisit old decisions when new evidence appears and compare decisions across programs.
Teams should understand not only what they decided — but why, including disagreement and approval trail.
That's what VeritasBio is: the accountability layer for evidence-driven R&D organizations.
We are looking for a small number of design partners in biotech, techbio, CROs, and AI-forward R&D organizations.
Together, we can configure VeritasBio around one real workflow: target assessment, candidate nomination, external asset diligence, or AI recommendation review.
Early access to a working system built for real scientific workflows.
Direct influence on product direction and feature prioritization.
A workflow shaped around your team's real process and decision types.
Closer collaboration with the founding team throughout development.
Preferred early-partner relationship as the platform matures and scales.
AI is accelerating discovery. Decision-making hasn’t caught up.
VeritasBio is the system of record for scientific decisions.