1. Evidence
Does the text point toward supporting material such as sources, studies, documents, records, named organizations or other evidence that a reader could investigate?
Methodology
How the Supience Information Indicator Works
The Supience Information Indicator is designed to provide a structured way to examine observable characteristics of a piece of information before relying on it. It is not a truth detector, fact-checking database, source certification system, or probability that a statement is true.
Instead, the tool looks for signals that can make an information item easier or harder to investigate. The result is intended to help a person decide what deserves closer attention.
What the tool evaluates
The current Supience model considers four broad dimensions: evidence, specificity, uncertainty and verifiability. Each dimension represents a different property of the submitted text.
2. Specificity
Does the information contain concrete details such as names, dates, quantities, locations, conditions or clearly defined claims rather than relying primarily on vague statements?
3. Uncertainty
Does the wording acknowledge relevant limitations, conditions or uncertainty where appropriate, or does it present complicated claims with excessive certainty?
4. Verifiability
Does the submitted information contain clues that make its important claims easier for a reader to investigate independently?
Why these four dimensions?
Information quality is multidimensional. A paragraph can contain a citation but still be too vague to evaluate. Another statement may be highly specific but provide no way to determine where its numbers came from.
The four dimensions therefore complement one another. Supience does not treat one signal as sufficient by itself.
How the score is constructed
The tool converts observable characteristics of the submitted text into component scores. Those component scores are combined into an overall indicator displayed to the user.
The calculation is intentionally designed as an indicator rather than a probability. A higher result means that the submitted text contains more of the characteristics the model is designed to recognize. It does not mean that the information has been proven correct.
Important: A score is an assessment of observable signals in the submitted text. It is not a measurement of objective truth.
Signal detection
The launch version of Supience is designed to operate from characteristics that can be examined directly in the submitted text. Depending on the implementation version, these may include patterns associated with:
- references to sources or supporting material;
- named people, organizations, places or documents;
- dates, measurements, quantities and other concrete details;
- conditions or qualifications attached to claims;
- language indicating uncertainty or limitations;
- claims that contain identifiable subjects and outcomes; and
- details that give a reader something specific to verify.
These signals are useful because they can make an assertion more inspectable. They are not evidence that the assertion itself is accurate.
What happens when a signal is missing?
The absence of a detectable signal does not automatically make information false. A short statement may simply lack enough context for the tool to identify a particular characteristic.
For example, a sentence such as “The meeting starts at 3 PM” contains a specific time, but without knowing which meeting, date or location, there may still be important context missing.
The indicator should therefore be interpreted alongside the original information and the circumstances in which it will be used.
Why the tool does not produce a truth percentage
A statement can score well on observable information characteristics and still be wrong. Conversely, a correct statement can be brief, poorly sourced or lacking in details that an automated text analysis can recognize.
For that reason, Supience does not present results such as “87% true” or “92% reliable.” Such numbers could easily be misunderstood as a statistical probability when the tool does not have the evidence required to make that claim.
Example of the distinction
Consider this statement:
“Company X reduced processing time by 35% during a six-month pilot conducted in 2025.”
The statement contains several concrete details: a company, a measurable result, a time period and a defined activity. Those characteristics can make the claim easier to investigate.
However, the details do not establish whether the 35% figure is accurate. A reader would still need to locate the underlying report, study, announcement or other appropriate evidence.
Interpretation bands
The displayed result is intended to help users decide how much attention to give the information's observable characteristics. Broad interpretation bands may be used to make the result easier to understand.
Stronger signals
The text contains several characteristics that make its claims relatively easier to inspect. Verification may still be appropriate depending on the consequences of being wrong.
Mixed signals
The text contains useful characteristics alongside areas that deserve additional inspection. The user should identify which important claims remain unsupported or unclear.
Weaker signals
The text provides fewer observable characteristics that support straightforward investigation. Additional context or independent evidence may be particularly useful.
Important edge cases
Short statements
Very short text may not contain enough information for meaningful signal detection. A low or incomplete result should not be interpreted as evidence that the statement is false.
Opinions and recommendations
An opinion does not necessarily need the same evidence structure as a factual claim. For recommendations, users should consider the criteria, assumptions and circumstances behind the recommendation.
Creative writing
Stories, fiction, slogans and other creative material are not necessarily intended to make factual claims. The indicator is therefore not designed to judge creative merit or fictional accuracy.
Technical and scientific material
Technical claims can require domain-specific knowledge and primary literature. Observable text signals cannot substitute for qualified review of the underlying technical evidence.
AI-generated information
AI-generated text can be detailed, confident and well structured while still containing incorrect or unsupported claims. Supience can help identify characteristics worth examining, but it does not independently verify an AI system's output.
Known limitations
- It primarily evaluates observable characteristics rather than independently establishing facts.
- It cannot determine whether a cited source actually supports a claim unless separate verification is performed.
- It may miss context that exists outside the submitted text.
- It cannot reliably determine the credibility of every person, organization or website mentioned.
- It cannot replace specialist review for high-consequence decisions.
- Different types of information naturally require different standards of evidence.
Human judgment remains part of the process
Supience deliberately keeps the human decision-maker in the loop. The tool can organize observable signals, but the user still has to determine what matters, which sources are appropriate and how much verification is justified.
For important claims, users should move from the indicator to the underlying evidence. That may mean checking primary documents, official records, original research, qualified professionals or multiple independent sources.
Privacy-conscious design
The core Supience calculator is designed to perform its analysis in the browser rather than requiring every submitted passage to be sent to an external artificial-intelligence service. This reduces the need for a server-side analysis system for the basic tool.
The site's actual data practices may change if analytics, advertising, forms or other third-party services are introduced. The Privacy Policy should therefore be consulted for the site's current practices.
Methodology updates
The scoring model may evolve as the tool is tested and improved. Changes should be documented so that the indicator remains understandable rather than becoming an opaque score.
A methodology change can affect how the same text is evaluated at different points in time. Results should therefore be treated as outputs from the current version of the tool, not permanent facts about the submitted information.
The right way to use the indicator
Think of the Supience result as a signal for investigation. It can help answer:
- Does this information contain details worth checking?
- Are important claims specific enough to investigate?
- Is supporting evidence identified?
- Are relevant limitations acknowledged?
- What should I verify before relying on this information?
It should not be used to conclude that a person, company, article, AI response or website is truthful or untruthful solely because of its score.
Bottom line: Supience measures information-quality signals that can be observed in submitted text. It does not measure truth itself.