Defined scope
Document the brand, competitors, engines, topics, intents, period, and market before measuring.
Fihrist methodology
A measurement is useful only when its limits are visible.
Fihrist compares like with like, keeps the evidence attached, and never presents one changing answer as a universal truth.
Fihrist begins with a defined brand, market, competitor set, topic scope, and buyer intent. Answers are retained at engine and question level. Rates keep their sample context, and repeated measurement is used to separate a meaningful pattern from ordinary output variation.
AI answers are variable. The method must account for that.
A screenshot can be useful evidence, but it is not a market-wide conclusion. Reliable interpretation depends on comparable prompts, declared scope, retained answers, sample context, and honest confidence language.
What you can learn
Comparable runs
Use the same evaluation frame when comparing brands and engines.
Answer-level evidence
Keep the response, mention, recommendation, and source behind every finding.
Qualified confidence
Show sample context and limitations instead of false precision.
How the analysis works
Define the market
Agree on the brand, competitors, audience, and measurement boundaries before measurement begins.
Collect comparable evidence
Run the same buyer questions across the same AI engines, with every finding linked to the answer behind it.
Turn the gap into work
Turn the findings that matter into source, content, brand, or technical priorities, then measure again.
fihrist.io/dashboard/insights
What your team can do with it
- Know exactly what a result represents
- Audit every important finding
- Avoid conclusions from isolated answers
- Compare change on a consistent basis
Frequently asked questions
Why can the same prompt return different answers?
Models are probabilistic and their systems, sources, and context change. Variation is expected and must be reflected in interpretation.
Does a visibility score predict revenue?
No. It is an observable indicator within a defined answer set, not a direct revenue attribution model.
How do you communicate confidence?
By retaining evidence, stating scope and sample context, repeating comparable measurements, and qualifying findings that are not stable.
See where your brand stands in AI answers.
Request early access. We will help you define the market, competitors, and buyer questions worth measuring.
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