The Problem
The internet has no infrastructure layer for verification. QYN is building it.
Every major internet platform has infrastructure for payments, identity, and communications. None have infrastructure for truth. AI-generated articles, synthetic video, and manipulated content spread freely with no accountability layer. QYN is the first open protocol designed to change that.
01
No verification layer exists
The internet was built without a native mechanism to verify the authenticity of content. What exists today are expensive, closed, and inaccurate.
02
AI content is invisible to detection
Modern language models produce text indistinguishable from human writing. Standard plagiarism checkers were not designed for this generation of AI.
03
Deepfakes have no accountability
Tools to generate synthetic video cost nothing. Tools to detect and prove manipulation remain locked behind enterprise contracts.
How It Works
Open infrastructure. Permanent record. Plain English results.
QYN is not a product bolted on top of the internet. It is infrastructure designed to sit underneath it — verifying content at the signal level and writing every result permanently to a public blockchain.
Paste a URL, article text, video link, or essay. No account needed. No file size limits.
QYN runs 14 independent signals: domain credibility, AI phrase density, sentence burstiness, manipulation patterns, and more.
Signals are weighted and fused into a single trust score (0–100) with a plain English explanation of every finding.
Every verification is hashed and written to the QYN chain. Results are permanent, tamper-proof, and publicly auditable.
Free Infrastructure
Built to be used. Not bought.
QYN's three verification tools are free, open, and require no account. We believe verification infrastructure should be as accessible as the content it verifies.
QYN Verify
Free for everyone
The public-facing verification layer. Paste any URL or text and QYN analyses source credibility, AI generation probability, and manipulation signals across 14 independent signal channels. Results are recorded on-chain.
- Source credibility scoring
- AI content detection
- Manipulation pattern analysis
- Blockchain-verified results
QYN Academic
For UK academic staff
The academic integrity layer for UK universities. Detect AI-generated submissions with clinical precision. Score personal voice, citation quality, and factual specificity. Designed for lecturers who need evidence, not just a percentage.
- AI probability scoring (0–100%)
- Personal voice detection
- Citation quality analysis
- Human vs AI indicator breakdown
Requires institutional email verification. Staff only.
Try it free →QYN Video
Coming Q2 2026
The deepfake detection layer. Frame-level analysis to identify AI-generated video, synthetic faces, and cloned voice. The infrastructure for video authenticity verification.
- Deepfake face detection
- AI-generated footage analysis
- Voice cloning identification
- Metadata authenticity scoring
Why QYN
The first verification infrastructure built in the United Kingdom.
Verification has been attempted before. Fact-checking platforms. Browser extensions. Media intelligence tools. They all shared a structural flaw: their verdicts were not verifiable. You had to trust the tool itself. That is not infrastructure. That is another opinion.
QYN records every verification on a public blockchain. The result you receive today is cryptographically identical to the result anyone will receive in ten years from the same input. No company can alter it. No government can suppress it. That is infrastructure.
We built QYN's multi-signal fusion engine from the ground up — combining domain intelligence, statistical language analysis, and manipulation pattern detection into a unified verification protocol. No equivalent open infrastructure exists anywhere in the United Kingdom.
QYN is the first open, blockchain-verified content verification infrastructure built in the United Kingdom.
How QYN compares
For Universities
Academic integrity infrastructure for UK institutions.
QYN Academic gives lecturers a verification layer for student submissions — with evidence-grade output your academic board can act on. Not a score. An audit trail.
Evidence-grade output
Every result includes the exact signals that triggered the AI flag, the signals that indicated human authorship, and a plain English summary. Output that stands up to academic review.
Built for lecturers
QYN Academic was designed around the needs of academic staff — not students, not administrators. Institutional email verification ensures the tool stays in the right hands.
Citation intelligence
Detect whether citations are specific and verifiable or generic and AI-typical. Hallucinated references are flagged and explained.
Permanent audit trail
Every submission analysed is hashed and written to the QYN chain. Your institution holds a tamper-proof verification record — not a CSV export.
280+
UK institutions supported
< £0.001
per analysis on-chain
14
independent signals analysed
100%
explainable — no black box
Signal Architecture
Fourteen independent signals. One fused verdict.
Most detection tools use a single model and call it intelligence. QYN runs 14 independent signal extractors in parallel — each measuring a different dimension of authenticity. No single signal can swing the result. The fusion engine weights them by statistical relevance and outputs a trust score between 0 and 100, with a plain English explanation of every contributing factor.
Domain Credibility
SOURCETier-ranked position against 500+ known domains and misinformation registries.
Source Classification
SOURCEClassification into Tier 1–4 credibility bands, including known misinformation outlets.
AI Phrase Density
LANGUAGEConcentration of 200+ statistically identified AI boilerplate phrase patterns.
Sentence Uniformity
LANGUAGEStructural repetition and length consistency atypical of human writing variance.
Vocabulary Diversity
LANGUAGEType-token ratio measured against established human writing norms.
Perplexity Estimation
LANGUAGEPredictability of word sequence transitions relative to natural language distributions.
Formulaic Structure
LANGUAGEDetection of AI-typical intro-body-conclusion structural formatting.
Burstiness Score
STATISTICALSentence length variance compared to human baseline distributions.
Personal Voice Score
STATISTICALFirst-person engagement depth, anecdotal evidence, and subjective reference density.
Citation Quality
STATISTICALSpecificity and verifiability of academic references and sourcing patterns.
Factual Specificity
STATISTICALPresence of verifiable statistics, named reports, and precise numerical claims.
Caps Ratio
MANIPULATIONAbnormal capitalisation frequency associated with sensationalist content.
Urgency Density
MANIPULATIONConcentration of emotional urgency language and call-to-action pressure patterns.
Emotional Loading
MANIPULATIONSentiment manipulation word frequency relative to neutral journalistic baseline.
Fusion Layer
Signals are independent. The verdict is fused.
Each of the 14 signals is extracted by an independent module with no knowledge of the others. This isolation prevents cascade bias — a flaw common in single-model detectors where one strong signal can override all others.
The fusion engine receives 14 signal scores, applies learned weights based on content type and domain tier, and produces a single trust score. The reasoning layer then translates the weighted output into a plain English explanation — not a black box number.
All intermediate signal values are logged to /var/lib/quyn/signals.jsonl on the QYN node, forming a growing dataset for future model training. QYN is designed to improve over time.
Signal Modules
Fusion Engine
Weighted signal aggregation
Trust Score 0–100
Plain English reasoning
Results recorded permanently on QYN chain.
Chrome Extension
Verification on every page.
No tab switching. No copy-paste.
The QYN Chrome extension runs quietly in the background. Every page you visit gets a trust score. Suspicious content gets flagged before you share it.
Auto-verifies every page
Badge updates on your toolbar the moment a page loads. Green for trusted, red for rejected. No clicks needed.
Right-click anything
Select any text or link, right-click, and verify it instantly. Results appear inline — no new tabs.
Highlights suspicious content
AI-generated phrases underlined in amber. Manipulation patterns underlined in red. See exactly what triggered the flag.
14 signals. On every page.
The full QYN fusion engine runs in the background. Every result recorded on the QYN blockchain.
Open API
Verification as infrastructure. Open by design.
QYN's RPC endpoint is open. Any developer, platform, or institution can integrate content verification directly. No API key on testnet. No vendor lock-in. Infrastructure should be open.
curl -X POST https://rpc.getquyn.com \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "qyn_verifyContent",
"params": [
"https://www.bbc.co.uk/news",
"article"
],
"id": 1
}'Newsrooms
Verify sources and detect AI content before publication. Integrate QYN into your CMS.
EdTech platforms
Embed academic integrity checking directly into your assignment submission flow.
Browser extensions
Surface trust scores inline as users browse. QYN's API responds in under 200ms.
Ready to Use
The verification layer is open. Use it.
No account. No paywall. No limit. QYN is free public infrastructure for content verification. Start using it now.