Incomplete references slow down submission.
Academic writers lose hours recovering DOI, journal, volume, issue, pages, and publisher details before a manuscript can ship.
LIVE DEMO
Reference verification
42% confidence
Smith J. Neural citation behavior in scientific writing, 2024
Missing DOI, journal issue, and normalized page range
Parsing reference information
Finding metadata sources
Recovering missing identifiers
Calculating confidence score
Generating verification report
Resolved citation
DOI recovered, journal matched, metadata normalized, review priority low.
Ref
DOI
Journal
Author
Reference work is still a manual quality-control problem.
Researchers do not need another place to store citations. They need a reliable way to know whether a reference is complete, correct, and ready for submission.
Citation metadata is scattered.
Researchers jump between Google, Crossref, publisher pages, PDFs, BibTeX exports, and reference managers just to validate one source.
Confidence is hard to judge manually.
A citation may look formatted but still point to the wrong paper, stale DOI, missing author list, or inconsistent publication record.
SOLUTION ARCHITECTURE
From loose references to citation intelligence.
CiteWise turns ambiguous citation input into structured, scored, and verified academic metadata for publication workflows.
Layer 01
Reference Parsing
Extracts structured citation fields from raw references, DOI strings, BibTeX entries, and pasted reference lists.
Raw referencesDOI / BibTeXField extraction
Layer 02
Metadata Verification
Checks identifiers, journal data, author details, publication dates, and missing fields against available metadata sources.
DOI recoveryMetadata repairSource checks
Layer 03
Quality Reporting
Highlights duplicate, conflicting, incomplete, and low-confidence references before they reach a manuscript.
Issue detectionConfidence scoreCitation report
A focused stack for reference verification.
CiteWise is built around parsing references, repairing metadata, scoring confidence, and reporting citation quality issues.
Structured citation data
Reference Parsing Engine
Extract structured citation information from raw references.
Unified citation model
Citation Intermediate Model
Normalize references into a unified citation representation for reliable verification.
Reliability score
Confidence Scoring Engine
Evaluate citation reliability using multiple verification signals.
Metadata recovery
Smart Reference Resolution
Recover missing DOI, identifiers, and incomplete metadata automatically.
Consistency checks
Citation Consistency Detection
Detect duplicate, conflicting, and inconsistent references before submission.
Quality report
Verification Report Generation
Generate actionable reports showing citation issues and confidence levels.
From reference input to verified citation in a few steps.
The activation path is designed to make value visible before a researcher commits to a full workspace.
01
Paste your references
Add a reference string, DOI, BibTeX entry, or pasted reference list.
02
Extract metadata
CiteWise identifies citation entities and publication fields.
03
Detect citation issues
Missing identifiers, incomplete fields, duplicate entries, and inconsistent metadata are flagged.
04
Calculate confidence
Multiple verification signals produce a clear reliability score and review priority.
05
Export final references
Use cleaned, normalized citations in your manuscript workflow.
TECHNICAL FLOW
1
Raw reference data
2
Citation Intermediate Model
3
Confidence Scoring
4
Verification report
WHY IT IS DIFFERENT
Reliable research starts with reliable references.
CiteWise goes beyond simple lookup by validating metadata, identifiers, and citation consistency.
Citation Intermediate Model
A unified representation layer that connects citation data from different sources.
One consistent citation model.
Confidence Scoring Engine
Combines verification signals to estimate reference reliability.
Clear review priority for each reference.
Reference Verification Report
A clear summary of detected issues and citation quality.
From raw references to actionable checks.
Accuracy first
Confidence scoring highlights uncertain records before they reach a manuscript.
Reference data control
The production roadmap includes encryption, permission isolation, private spaces, and deletion controls.
Academic focus
The product is scoped around scholarly metadata, DOI resolution, and citation workflows.
Validate your references before submission.
Clear monthly limits keep the beta useful for researchers while keeping verification costs predictable.
Free
$0
For trying CiteWise with small reference sets.
- 20 reference checks/month
- Single reference verification only
- DOI and metadata lookup
- Basic confidence score
- Copy-ready citation export
EARLY ACCESS
Ready to verify citations with AI?
Join the beta list to help validate the first workflows: DOI recovery, metadata repair, reference confidence scoring, and citation enrichment.
Questions researchers ask before trying CiteWise.
Is CiteWise another reference manager?
No. CiteWise focuses on citation verification, metadata recovery, DOI resolution, confidence scoring, and reference quality control. It can support reference workflows, but it is not designed to replace storage-first tools.
What formats does the MVP support?
The landing page demonstrates the workflow for reference text, DOI, BibTeX, and pasted reference lists. The MVP focuses on reference verification rather than literature search or writing assistance.
How does CiteWise judge reliability?
The confidence scoring engine combines parsing quality, identifier recovery, metadata matching, and consistency checks into a unified reliability score.
Is my reference data safe?
The product requirements call for encryption, access controls, and deletion controls before production reference processing is launched.
Why is the Pro price $19 per month?
$19 per month gives individual researchers a practical monthly allowance for citation verification while keeping metadata lookup and AI processing costs predictable.