A convincing story is not necessarily a well-supported one. That is why we do not simply take claims at face value. We look into how something works, where the information comes from, and what we can and cannot verify.
Research usually starts with a simple question: what exactly are we trying to understand?
We then break the subject down into smaller questions. How does it work? Where does the income or return come from? Who is in control? What are the risks? What claims are being made, and what are they based on?
Step by step, we build a clearer picture. Not from a single source or a single narrative, but by gathering and comparing information and, where possible, verifying it.
Wherever possible, we use primary sources. This includes official documentation, terms and conditions, technical documentation, public data, blockchain transactions, company information, and direct communication with the parties involved.
Other articles, videos, social media, and community discussions can provide useful context, but they are not automatically evidence that a claim is true.
Just because a project, company, or platform makes a claim does not automatically make it true.
So we look for evidence. Can the data be verified? Is the same information confirmed by other sources? Does the explanation match what can actually be observed, technically or in practice?
If we cannot independently verify a claim, we do not treat it as an established fact.
Research is not only about the information that is available.
Missing data, conflicting statements, unanswered questions, and information that cannot be verified can be just as relevant as what is published.
We do not fill in those gaps with assumptions. If something remains unknown or uncertain, we say so.
Understanding how something works also means looking at what could go wrong.
What dependencies are there? Who is in control? What economic, technical, or operational risks are involved? And do promises depend on circumstances that could change?
The presence of risk does not automatically mean something is bad. But risks are part of the full picture.
Based on the information available, we form a current assessment of a project. We look at the overall picture: the quality of the evidence, how well claims can be verified, the risks, any contradictions, and any information that may be missing.
Projects change. New documentation is published, systems are updated, risks can increase or disappear, and information that was previously missing may become available.
That is why a Veralisio assessment is never set in stone. If relevant new information changes the overall picture, we may update our articles and assessments.
Veralisio uses AI as a tool to support research and editorial work. For example, it can help us structure large amounts of information, compare documents, develop research questions, and make texts clearer.
We do not automatically treat AI output as fact or as a source. Important information must be traceable to verifiable sources.
The final selection, interpretation, and assessment remain part of Veralisio’s editorial process.
Research is never infallible. We may miss information, misinterpret a source, or later come across new evidence that changes an earlier conclusion.
If we discover a relevant error, we correct it. If we receive new information that could affect our research or assessment, we review it using the same principles.