Editorial Methodology
How AInvasion Researches, Verifies, and Updates AI Coverage
AInvasion covers a field where facts, product capabilities, pricing, benchmarks, regulations, and company claims can change quickly.
This methodology explains how we decide what to publish, how we evaluate evidence, how we communicate uncertainty, and what happens when we get something wrong.
The goal is not to make every article sound certain.
The goal is to make the level of certainty visible.
Last reviewed: September 2026
Who Is Accountable
Editorial lead: Tom Morgan, Founder & Editor
AInvasion is not presented as a large newsroom or as a network of specialist experts where that does not exist.
Editorial responsibility rests with the named editor.
Where a subject requires specialist expertise, we rely on relevant research, institutions, technical documentation, primary sources, independent testing, and clearly attributed expert material rather than presenting unsupported personal authority.
Our Editorial Process
1. Start with the strongest available sources
Where possible, research begins with primary or authoritative material, including:
- official documentation;
- technical specifications;
- academic papers;
- government and regulatory sources;
- financial filings;
- first-party datasets;
- direct testing;
- established research organizations.
Secondary reporting can provide context, identify developments, or surface information worth investigating, but important claims should not rely solely on secondary coverage when a stronger source is available.
2. Verify substantive factual claims
Statistics, benchmarks, product capabilities, pricing claims, regulatory developments, and other consequential factual statements should be traceable to a source.
If a figure cannot be independently verified, we attribute it to the organization or person making the claim.
If the evidence is too weak to support the statement, the claim is narrowed, qualified, or removed.
3. Separate evidence from interpretation
AInvasion distinguishes among three levels:
DOCUMENTED: What a source, dataset, company, regulator, research paper, or test directly establishes.
SUPPORTED INTERPRETATION: A conclusion supported by available evidence but still subject to reasonable uncertainty.
EDITORIAL ANALYSIS: Our interpretation of what the evidence may mean, including practical implications and limitations.
The distinction matters because a company’s claim is not automatically the same thing as an independent fact.
4. Label confidence where useful
Longer analysis articles may use three confidence levels:
ESTABLISHED: Directly verified through reliable documentation, reproducible testing, or multiple independent authoritative sources.
PROBABLE: Supported by credible evidence but not fully independently verified, measured at sufficient scale, or established across all relevant conditions.
SPECULATIVE: An informed interpretation, scenario, or prediction that cannot currently be established as fact.
Speculative claims are identified as such rather than presented as established information.
5. Challenge the article’s own argument
Before publication, substantive analysis is reviewed for weaknesses such as:
- unsupported assumptions;
- outdated information;
- vendor claims presented as facts;
- benchmarks without sufficient test conditions;
- regional limitations;
- pricing or feature differences between plans;
- samples that are too narrow to generalize;
- causal conclusions that the evidence does not establish;
- conclusions stronger than the underlying evidence.
The question is simple:
What could make this conclusion wrong, incomplete, or too broad?
6. Publish, then monitor
Publication does not permanently freeze an article’s claims.
AI products, pricing, regulations, benchmarks, and company strategies change.
When material facts change, we update the relevant article.
Significant factual corrections are identified rather than silently rewritten when the change materially affects the article’s meaning.
Evidence Standards
We prefer evidence in roughly this order, depending on the question:
- Direct testing or reproducible observation (for example, a calculator run on defined inputs; see How We Test)
- Primary technical documentation
- Government or regulatory sources
- Peer-reviewed or established academic research
- First-party datasets and financial filings
- Independent research organizations
- Industry research and surveys
- Reliable secondary reporting
This is not a rigid ranking for every claim.
A government source may be the best source for a regulation, while direct testing may be the best source for a software feature.
The important question is whether the source actually supports the specific claim being made.
Vendor and Company Claims
We do not automatically treat company statements as independent evidence.
For example:
Company claim: Vendor X reports a 3× speed improvement.
is different from:
Independent finding: An independent test reproduced a 3× improvement under specified conditions.
When a company is the only available source for a claim, we attribute the claim clearly and identify relevant limitations where they matter.
Benchmarks and Statistics
A benchmark is meaningful only in context.
Where relevant, we identify:
- hardware;
- model or software version;
- dataset;
- prompt or workload;
- test conditions;
- sample size;
- date;
- geographic scope;
- pricing tier;
- who conducted the test.
We avoid turning a narrow benchmark into a general statement about real-world performance.
Similarly, a percentage without a defined population, date, sample, and source can be misleading even when the number itself is technically accurate.
What We Do When Evidence Conflicts
AI research frequently produces apparently contradictory results.
We do not automatically choose the number that supports the article’s thesis.
Instead, we examine whether the studies differ in:
- definitions;
- sample populations;
- geographic coverage;
- time periods;
- methodology;
- measurement criteria;
- product versions;
- deployment stage.
Sometimes apparently conflicting numbers are measuring different things.
When the disagreement cannot be resolved, the disagreement itself becomes part of the answer.
AI Use in Our Editorial Workflow
AInvasion uses AI tools, including Claude, in parts of the editorial workflow. How we test products and tools is described on our How We Test page.
AI may assist with:
- research discovery;
- organization;
- outlining;
- drafting;
- editing;
- data analysis;
- brainstorming;
- technical tasks.
AI assistance does not determine whether a claim is true.
Factual claims still require appropriate sourcing and editorial review.
AI-generated wording is revised or removed when it goes beyond the available evidence.
The human editor remains responsible for the published article, its interpretation, corrections, and conclusions.
We disclose this process because transparency is preferable to implying that AI was never involved.
Corrections
Errors are possible, especially in a rapidly changing field.
When a factual error is confirmed, we correct the relevant article.
Material corrections may include:
- what changed;
- when it changed;
- why the original statement was incorrect or incomplete.
We do not consider a correction a failure of the editorial process.
A transparent correction process is part of the process.
Current Limitations
Our research has practical limitations.
Coverage currently relies heavily on English-language sources, with particular strength in US and EU documentation, companies, research organizations, and public regulatory material.
Some regions, languages, products, and organizations have less accessible documentation.
Where that limits confidence or generalization, the article should say so.
We do not treat absence of accessible evidence as proof that something does not exist.
Independence
AInvasion does not sell editorial conclusions.
A company cannot purchase a favorable review or a higher ranking.
Where sponsorships or affiliate relationships exist, they should be disclosed.
Affiliate relationships do not determine editorial conclusions.
Company claims, independent findings, and editorial interpretation should remain distinguishable.
A Simple Rule
The standard can be summarized simply:
- If a claim has strong evidence, show the evidence.
- If the evidence is limited, label the limitation.
- If the evidence is uncertain, say so.
- If we are wrong, correct it.
That is the editorial standard AInvasion aims to maintain.
Corrections & Questions
If you find an unsupported claim, outdated statistic, factual error, missing source, or misleading interpretation, contact us through the AInvasion Contact page.
Confirmed material corrections are incorporated into the relevant article.
Methodology owner: Tom Morgan, Founder & Editor