AI-assisted quality review for scientific documents. SciVerify flags potential numerical, statistical and cross-document inconsistencies, links them to the relevant evidence and helps experts focus their review before client delivery.
Upload a PDF or DOCX and receive a structured, evidence-linked QA report highlighting potential inconsistencies, ambiguities and areas requiring expert review.
Scientific teams should not have to manually cross-check every number, label, reference and quantitative statement across a complex document. SciVerify helps identify where reported information may be inconsistent, incomplete or unclear.
By the time a reviewer, regulator or client finds the discrepancy, correcting it is expensive: revision cycles, delayed submissions, damaged credibility.
A document goes in; a structured, evidence-linked report comes out. Each flag points to the conflicting evidence and to the section a qualified reviewer should inspect.
SciVerify turns a document into a structured review. It does not reproduce the underlying scientific or statistical analysis — it compares what the document reports against what it reports elsewhere.
Read available document text, tables, labels, captions and references.
Build a semantic map linking methods, groups, procedures, results, figures, tables and conclusions.
Compare identities, terminology, values, schedules and claims across related document locations.
Rank potential issues by likely impact on execution, interpretation and client confidence.
Show the conflicting or incomplete evidence, state uncertainty and recommend what a reviewer should inspect.
Not a style pass. A comparison pass — every flag traced back to the evidence behind it.
Flags inconsistent p-values, test names, thresholds or supporting context reported across the document.
Compares reported counts, percentages and totals across sections and identifies discrepancies or missing explanations.
Cross-checks whether these values are reported consistently, and with enough context to be interpreted.
Compares names, groups, routes, formulations, concentrations, dose descriptions, timing, measurements and terminology across relevant locations.
Detects mismatched labels, values, captions, group identities, schedules and references across formats.
Highlights claims that appear broader, stronger or less qualified than the results described elsewhere in the document.
Coverage qualifier. These checks apply where sufficient readable evidence is available in the document. Scanned PDFs, image-based tables, obscured content and incomplete source material limit what can be extracted and compared.
Does information reported in one part of the document appear consistent with the related information reported elsewhere? SciVerify is built around that question — not around a broad promise to validate the science.
Scientific users do not need hundreds of uncertain AI suggestions. Each flag shows the conflicting evidence, where it sits in the document, and how confident the system is.
SciVerify does not decide whether the science is correct. It shows experts where the document may not be internally consistent, and what should be reviewed before delivery.
Potential issues are ranked by likely effect on execution, interpretation and client confidence, so limited review time goes where it changes the outcome.
Recommended workflow:
Write → Automated QA Screening → Expert Review → Resolve → Submit
The strongest early users are teams where a discrepancy found late carries a measurable price.
Protocols, study reports and regulatory documents
High document volume, tight delivery deadlines
A consistency screen before results leave the team
Screening submissions before peer review
Protecting credibility across every publication
SciVerify is a document QA and consistency-screening tool. It flags potential issues for expert review and does not independently establish scientific validity, recalculate statistical analyses or replace statistical, regulatory or scientific sign-off. Coverage depends on document quality and the availability of readable supporting information.
SciVerify provides evidence-linked QA screening that makes expert review faster, more focused and better evidenced — it supports your approval process rather than replacing it. Adoption is simple: upload the document, receive the report.
Or write to us directly at sales@collectim.tech