Editorial Policy
Last updated: April 2026
Our Commitment to Accuracy
QRLynx is committed to providing accurate, up-to-date, and unbiased information across our website, blog, and comparison content. We believe transparency builds trust, and we hold ourselves to high editorial standards.
How We Create Content
All content on QRLynx is written and reviewed by our team, led by Ahmad Tayyem, Founder & CEO. Our blog guides and tutorials are based on hands-on product expertise built since 2012 through Jorbox LLC.
How We Conduct Comparisons
Our competitor comparison tables are researched by visiting each competitor's official pricing page and documenting their published features, limits, and pricing. We verify data points against multiple sources including G2, Capterra, and independent review sites. All comparison data includes a date stamp (e.g., "March 2026") so readers know when the data was collected.
Disclosure: QRLynx is one of the products compared. We strive for objectivity but readers should independently verify current pricing and features on each competitor's website, as these may change after our publication date.
Sources & Citations
We cite authoritative sources including the ISO/IEC 18004 QR code standard, Reed-Solomon error correction documentation, and Cloudflare infrastructure specifications. Statistical claims about QRLynx (e.g., number of QR code types, edge locations, redirect speed) are based on our own platform data.
AI Use & Human Review
We use AI tools (including large language models) to assist with drafting, outlining, and researching content. Every article and comparison page published on QRLynx is reviewed and edited by a human — primarily our founder Ahmad Tayyem — before going live. AI is a research and drafting assistant, not an unreviewed publisher.
Statistical claims and platform data (e.g., scan benchmarks, security screening rates, creator behavior numbers) come from QRLynx's own production data and are verified against the source system before publication. Any external data cited in our articles includes a link to the primary source. We do not publish AI-generated statistics, quotes, or case studies as if they were real.
Our research reports (scan benchmarks, security, creator behavior) are built on anonymized platform data. Methodology notes and date ranges are disclosed in each report.
Corrections & Updates
If you find an error in our content, please contact us at support@qrlynx.com. We review all reports and publish corrections promptly. Significant corrections are noted with an update date on the affected page.
Independence & Funding
QRLynx is funded through paid subscription plans. We do not accept payment from competitors to influence our comparison content. Our editorial decisions are made independently of our business relationships.