Kingy AI Trust Center
A repeatable path from source to conclusion.
Kingy AI reports should make the decision frame, evidence class, comparison rules, testing limits, and maintenance record visible to the reader.
Purpose and scope
This methodology governs Kingy AI research reports, market maps, comparison tables, scored frameworks, and evidence-led product analysis. It is designed to make the path from source to conclusion visible. It is not a claim that every article is scientific research, a controlled benchmark, or hands-on testing.
- Frame the decision.
Define the reader, question, time window, inclusion rules, and claims that matter. - Build the source record.
Collect primary documents first, record review dates, and identify gaps or conflicts. - Test where possible.
Record version, access level, device, task, input, conditions, and what was not tested. - Normalize comparisons.
Compare like with like; separate plans, regions, preview access, and vendor-reported metrics. - Review the draft.
Check each material claim, link, image, disclosure, date, limitation, and calculation. - Maintain the record.
Publish a cutoff date, add material update notes, and route errors through corrections.
Evidence labels
Hands-on
Kingy AI directly used the product under stated conditions. The article should identify the tested surface and meaningful limits.
Demo-observed
Behavior was seen in a live or recorded demonstration, but Kingy AI did not control every condition.
Public-source analysis
Conclusions are drawn from linked documentation, releases, repositories, records, and attributed reporting.
Vendor-reported
The organization supplied the claim. It remains attributed unless independently verified.
Comparison and scoring rules
A Kingy score is an editorial decision aid, not a universal scientific measurement. Every scored framework should name its dimensions, explain the scale, show material inputs, and identify subjective judgment. Weights should reflect the stated reader and task. Scores from different reports are not automatically comparable.
- Do not rank products that were evaluated under materially different access or evidence conditions without saying so.
- Do not infer performance from price, funding, customer logos, popularity, or benchmark marketing.
- Do not present modeled estimates as observed outcomes.
- Recalculate tables when a source changes; do not patch a headline number without checking dependent conclusions.
Freshness and dates
| Field | Meaning |
|---|---|
| Published | The date the article first became public. |
| Source review cutoff | The latest date through which the cited evidence set was reviewed. |
| Updated | A date attached only when the published content was materially changed. |
| Version or access date | The tested product version, plan, device, region, or date needed to interpret an observation. |
AI-assisted editorial work
AI tools may help organize source notes, identify inconsistencies, draft structures, transform formats, or support quality checks. They are not accountable authors or independent sources. A human editor remains responsible for material claims, citations, disclosures, calculations, and publication. Model output should not be used as evidence unless the output itself is the subject of a documented test.
Limitations and conflicts
Reports should disclose evidence gaps, products not tested, access constraints, fast-changing prices, geographic limits, and relevant commercial relationships. If a conflict cannot be managed with disclosure and editorial controls, the work should not be presented as independent analysis.