Platform

One system for the evidence behind every decision.

Kaimen Rigor 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.

Request a demo See two real Kaimen Rigor reviews

A simple principle: rigor should be concise, robust, and elegant. The finding that matters, the evidence to defend it, and nothing to wade through.

A simple principle

Concise. Robust. Elegant.

A rigor review should surface the finding that could change the decision, give you the evidence to defend it, and spare you everything else.

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.

How it works

From scattered documents to a verifiable report.

Kaimen Rigor 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.

The deliverable

Every review returns a Kaimen Rigor review.

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 Kaimen Rigor review 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

See two real Kaimen Rigor reviews

Anatomy of a findingReal Kaimen Rigor review

Olaratumab (Eli Lilly, Lartruvo)

Randomized Phase 2, advanced soft-tissue sarcoma · The Lancet, 2016

This Phase 2 was the basis for accelerated approval. The disproportionate overall-survival claim (26.5 vs 14.7 months) carried the valuation and the EU filing.

Confirmed verbatim in the source text

criticalA statistically impossible result: an 18.2% objective response rate reported with 95% CI 29.6–29.8 — an interval that excludes its own point estimate.
criticalAn independent-review PFS hazard ratio of 0.670 with 95% CI 0.04–1.12 — a lower bound implausible for 133 patients. The journal later corrected it to 0.40–1.12; Kaimen Rigor flagged it from the text alone.
seriousFour reported percentages disagree with their own counts (e.g. neutropenia “25 [39%]” against N=65), plus no data-availability statement and unidentified assay reagents.

What happened next: Confirmatory Phase 3 ANNOUNCE (JAMA 2020, n=509) found no overall-survival benefit (HR 1.05). The FDA revoked the Lartruvo license and the EMA withdrew authorisation; olaratumab is no longer approved.

Kaimen Rigor read only the 2016 paper text — it never saw the ANNOUNCE result or the journal’s correction.

Open the full Kaimen Rigor review Reviewed text DOI

BD and R&D

The same rigor, two jobs.

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

Explore BD diligence

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

Explore R&D validation

More than an AI reviewer

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.

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.

Request a demo Explore an R&D use case

Prefer to look first? See two real Kaimen Rigor reviews.

One system for the evidence behind every decision.

A simple principle: rigor should be concise, robust, and elegant. The finding that matters, the evidence to defend it, and nothing to wade through.

Every review returns a Kaimen Rigor review.

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

Yes

Supports expert adjudication

Yes

Produces decision-oriented outputs

Generic

Yes

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.