No fabricated metrics
Do not publish invented search volume, ROI, adoption, accuracy, market size, client results or performance claims.
Editorial Standards
Useful guidance should make its evidence visible.
PlanckCyber publishes practical AI and software guidance to support business and technical decisions. We distinguish PlanckCyber frameworks from external facts, prefer primary sources for material factual claims, date substantive updates, and avoid invented statistics, testimonials, credentials or guarantees.
Source Hierarchy
Prefer the source closest to the claim.
- Official product, standards-body, regulator or primary technical documentation.
- Peer-reviewed or first-party research when the methodology is relevant and visible.
- High-quality secondary analysis when a primary source is unavailable or the analysis itself is the subject.
- Community experience for clearly attributed practical perspective, not as proof of a factual claim.
PlanckCyber Frameworks
Original planning guidance is labeled as guidance.
Many pages describe decision frameworks derived from software engineering, operational analysis and AI implementation practice. Those frameworks are PlanckCyber guidance rather than universal standards. They should be tested against the client’s workflow, data, risk and operating constraints.
Publication Rules
Rigor over hype.
No fabricated trust signals
Do not invent testimonials, reviews, client names, certifications, partnerships or credentials.
Separate demos from client work
Label production, client, demonstration and reference-build evidence accurately.
Use update dates responsibly
Change the substantive update date when the content materially changes, not on every build.
Preserve tradeoffs
Comparisons should explain where each option fits and where it does not.
Correct material errors
When a material factual error is found, correct the page and update the date when the correction changes the guidance.
AI-Assisted Drafting
Automation does not remove editorial responsibility.
AI tools may assist with research organization, drafting, editing, code or structured-data production. Published material remains subject to factual, technical and brand review. AI-generated volume is not a reason to publish low-value or duplicative pages.
Corrections & Questions
Challenge the evidence.
If a reader identifies a factual error or needs the basis for a material claim, use the project contact route and identify the page and statement. Non-confidential corrections can be reviewed against the source.
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Have a specific AI decision?
Use the guidance as a starting point, then test it against your workflow, evidence and constraints.