Falcon Validara AI for Education

Build Academic Trust.

Verify originality. Review citations. Analyze AI usage. Protect academic integrity.

Students
Researchers
Professors
Universities
Institutions
Certification Bodies

Originality Intelligence™

Eight signals working together to describe a submission's integrity — not a single opaque score.

Originality Analysis

Multi-signal originality scoring at document, section, and sentence granularity.

Similarity Detection

Exact, near, and paraphrase-level matches across corpora and the open web.

Semantic Similarity

Meaning-preserving overlap detection powered by dense-vector embeddings.

AI-Assisted Content Analysis

Probabilistic indicators of AI-assisted drafting — never a verdict.

Citation Verification

Validate every citation resolves, is accurately quoted, and supports the claim.

Reference Validation

Detect broken, malformed, or fabricated references before submission.

Authorship Analysis

Stylometric signals compared against a student's verified writing baseline.

Trust Scoring

A composite Academic Trust Score™ for reviewers, not just a percentage.

Plagiarism detection, across five layers

From verbatim copies to meaning-preserving paraphrases — and the citation shortcuts in between.

01

Exact Match

Direct verbatim copies from source documents, web pages, and institutional archives.

02

Near Match

Lightly rewritten or word-swapped content that preserves original structure.

03

Semantic Match

Meaning-preserving paraphrases — detected via semantic embeddings, not surface strings.

04

Cross-Document Match

Comparison across uploaded assignments, institutional knowledge bases, and internal repositories.

05

Citation Abuse

Missing citations, incorrect citations, broken references, and fabricated references.

AI Usage Analysis

Indicators of AI-assisted content generation, presented as probabilities — never as definitive proof.

AI usage analysis is presented as probabilistic indicators and never as definitive proof. Human review is required.

AI Contribution EstimateRanged %, per section
Human Contribution EstimateRanged %, per section
Confidence ScoreLow / Medium / High
Review RecommendationsSuggested reviewer actions
Composite scoring

Academic Trust Score™

A single score for academic submissions — weighted across six factors, with every contributing signal traceable back to evidence.

Originality
Citations
References
Authorship Signals
AI Usage Indicators
Document Integrity

University Dashboard

Built for faculty, reviewers, department heads, and administrators — with role-scoped access and full audit trails.

FacultyReviewersDepartment HeadsAdministrators
Bulk Assignment Review
Class Reports
Student Reports
Risk Indicators
Academic Trust Trends
Originality Trends
LMS Integrations

Meets your classroom where it already is

Moodle
Canvas
Blackboard
Google Classroom
Microsoft Education
Research Integrity

For papers, theses, dissertations, and journals

Extend originality intelligence to formal research artifacts — with citation-level provenance and source validation.

Citation Analysis

Source Validation

Similarity Detection

Fact Verification

Academic Review Workflow

From submission to signed report

  1. 01

    Student Submission

  2. 02

    Originality Analysis

  3. 03

    Citation Analysis

  4. 04

    AI Usage Analysis

  5. 05

    Instructor Review

  6. 06

    Final Report

Required Disclaimer

Results are intended to support academic review and should not be used as the sole basis for disciplinary action. Human review is required.

Bring Originality Intelligence™ to your institution.

Pilot with a class, a department, or the whole campus.