Platform
One system for the evidence behind every decision.
Adcurare connects clinical trials, publications, protocols, registries, and statistical analyses into a single evidence system, then evaluates them like a skeptical expert and hands back findings your team can check.
Scientific evidence intelligence for pharma. One platform, two buying motions: business development and R&D.
A simple principle: rigor should be concise, robust, and elegant. The finding that matters, the evidence to defend it, and nothing to wade through.
Important evidence deserves more than a surface-level review.
Scientific decisions are distributed across papers, supplements, protocols, registries, analysis plans, and regulatory documents. Adcurare evaluates them as one system and surfaces what matters, so your experts spend their time on the findings that could change the decision, not on re-reading everything.
Numerical and statistical consistency
Recompute reported statistics and check denominators, confidence intervals, P values, and tables against the underlying data.
Protocol-to-publication concordance
Compare protocols, SAPs, registries, and publications to surface deviations from what was prespecified.
Claim-to-evidence verification
Test whether each consequential claim is actually supported by the evidence presented.
Reporting and ethics completeness
Flag reporting omissions, missing disclosures, and ethics or registration gaps, without treating omission as misconduct.
Reproducibility and decision-risk assessment
Assess whether the analysis can be reproduced and where the decision risk concentrates.
From scattered documents to a verifiable report.
Adcurare connects and evaluates the full evidence base as a single system, then hands back findings a human can check.
Assemble the evidence
Publications, supplements, protocols, registries, analysis plans, regulatory documents, and relevant prior studies.
Extract consequential claims
Identify the claims that drive scientific, clinical, or commercial conclusions.
Cross-check and reconstruct
Compare documents, reproduce calculations, identify contradictions, and test whether claims are adequately supported.
Assess materiality
Separate typographical and reporting issues from findings that may change interpretation or decisions.
Produce a verifiable report
Every finding links to its source, supporting calculation, uncertainty, and recommended follow-up.
Every review returns a Rigor Record.
Not a chat transcript and not a single score. A versioned, source-linked report, concise where it can be and robust where it must be: it shows what matters and what your team can set aside, and traces every finding to its passage, its recomputed calculation, its confidence and materiality, and a recommended follow-up.
Every Rigor Record contains
- 01Executive summary
- 02Principal claims
- 03Evidence sources examined
- 04Findings organized by materiality
- 05Source passage and calculation
- 06Confidence and verification status
- 07Potential decision impact
- 08Recommended follow-up questions
- 09Methods and limitations
- 10Versioned audit trail
Primary-endpoint assessment window differs from the registry
Source claim
The primary endpoint was assessed at week 26.
“…the primary endpoint was assessed at week 26…”
Methods, Endpoints
Detected inconsistency
The prospective registry record and the SAP both specify a week-24 primary-endpoint window. The manuscript reports week 26. No amendment rationale is cited.
Recommended follow-up
Was the endpoint window amended, and if so, when relative to unblinding?
Where Adcurare earns its keep.
For business development
Know what the evidence is worth before you license the asset.
- In-licensing and acquisition diligence
- Clinical-trial evidence review
- Competitive and landscape evidence assessment
- Data-room and claim verification
- Portfolio and indication prioritization
- Decision-ready evidence reports
For R&D
Know if the science is solid enough to build on.
- Foundational-evidence validation before you commit a program
- Protocol, SAP, and registry-to-publication concordance
- Reproducibility and statistical review
- Internal prepublication and data-package QC
- Evaluation of AI-generated scientific work
- Systematic comparison of competing claims
What decision-grade verification requires.
| Capability | General AI assistant | Adcurare |
|---|---|---|
| Reviews multiple connected documents | Limited | Yes |
| Reconstructs numerical calculations | Inconsistent | Yes |
| Provides source-level evidence | Variable | Required |
| Separates confidence from materiality | Rarely | Yes |
| Maintains a versioned audit trail | No | Yes |
| Supports expert adjudication | No | Yes |
| Produces decision-oriented outputs | Generic | Yes |
Capabilities are marked implemented only where they ship today.
Designed to strengthen science, not sensationalize mistakes.
- AI outputs are candidate findings, not final judgments.
- Consequential findings require independent human review.
- Reporting omissions are not treated as evidence of misconduct.
- Authors and responsible parties are offered an opportunity to respond.
- Findings can be corrected, updated, or withdrawn as new evidence emerges.
- Conflicts of interest are disclosed.
- Public reports are versioned and retain the underlying evidence trail.
Get started
Bring decision-grade verification into your workflow.
For business development
See Adcurare on a live in-licensing or diligence decision: the pivotal trials behind an asset, a data room, or a competing evidence base. Start with a demo; we scope a private pilot when it is a fit.
For R&D
See Adcurare on the science you are deciding whether to build on: a protocol, an analysis, a data package, or a scientific-agent workflow. Start with a demo; scope a pilot on a real decision.
Prefer to look first? See a sample Rigor Record.