Ask the prompt repeatedly
Each tracked prompt is collected daily across every enabled AI model. One unusual answer never has to stand in for the whole market.
Data Accuracy
Know how much to trust every AI score.
One AI response is not a measurement. friction AI samples your prompts repeatedly and attaches confidence to every figure, so you can separate a reliable pattern from normal AI variation.
One answer is not a measurement
Repeated answers turn AI variation into a signal you can use
Ask the same AI model twice and the answer can change. Reliable reporting starts by measuring the pattern across responses, not by treating the latest answer as truth.
Check whether the pattern holds
friction AI compares responses across the reporting window to see whether the result stays consistent or keeps changing.
Attach confidence to the score
Every figure carries a confidence label, so your team can tell an established pattern from an early directional signal.
Confidence behind every figure
Confidence is built from four checks
The confidence label is not decoration. It tells you whether the current score is supported by enough consistent data to guide a decision.
Enough answers
How many captured responses sit behind the score, not just how recent the last answer is.
Collection coverage
How much of the expected data was successfully captured across the selected period.
Answer consistency
How much the result varies from one AI response to the next when the same question is asked again.
Stability over time
Whether the pattern continues across the reporting window and from one week to the next.
Read every result correctly
The number, the confidence, and the mention tier belong together
A strong score with low confidence is still an early read. A lower score with high confidence is a dependable problem to solve. friction AI shows both.
The score
Shows what AI is saying about your brand across the selected prompts, models, market, and time window.
The confidence
Shows how much evidence supports that score and how consistently the result repeats.
The mention tier
Shows how often your brand appears in the answers as Core, Recurring, Occasional, or Absent.
Mention tiers
See how consistently your brand appears
Alongside visibility, the mention tier turns response frequency into a label your team can understand immediately.
More than 60% of responses
Your brand is a regular part of how AI answers this question.
30% to 60% of responses
Your brand appears often, but is not yet a dependable part of every answer.
More than 0% but less than 30% of responses
Your brand appears sometimes, so the opportunity is visible but still inconsistent.
Not mentioned in responses
AI is filling the answer with other brands instead of yours.
Evidence behind the measurement
Confidence shows whether the pattern holds. The evidence shows why.
Open the prompt details to move from the confidence label back to the actual answers, brands, and sources that produced the result.
Original AI answers
Read the responses used to calculate the result and check the interpretation yourself.
Brands and competitors
See who appeared, who was recommended, and which brands filled the gap when yours did not.
Sources and citations
Inspect the sources, searches, and citations surfaced by the platform where that evidence is available.
Search keywords
Review the search terms AI used while researching an answer whenever that evidence is returned.
Questions your team can answer
Act on a pattern, not a one-off answer
01Is this score backed by enough answers?
02Did the AI responses agree or keep changing?
03Is this an established pattern or an early signal?
04How often was the brand actually mentioned?
05Which answers, competitors, and sources produced the result?
06Is the movement stable enough to act on?
Continue with friction AI
Connect confidence to the rest of your AI visibility work
Track the questions buyers ask, keep a consistent reporting baseline, and inspect the evidence behind every movement.
FAQ
How friction AI builds confidence into every score
What does Data Accuracy mean in friction AI?
friction AI separates reliability from validity. Repeated sampling, coverage, variation, and stability show how reliably a result repeats. Answer-level evidence lets you inspect whether the interpretation is valid. Repeatability alone does not prove that a classification is factually correct.
How is confidence calculated?
Confidence rises as more expected responses are collected and the pattern stays consistent across the reporting window and from week to week. When the evidence is limited or changing, the figure is labelled as lower confidence instead of being presented as settled fact.
What does the mention tier mean?
The mention tier translates visibility frequency into a clear label. Core means the brand appears in more than 60% of responses, Recurring means 30% to 60%, Occasional means more than 0% but less than 30%, and Absent means it was not mentioned.
Does low confidence mean the score is wrong?
No. It means the result should be treated as directional because the sample is still limited, collection coverage is incomplete, or the answers are changing. The label tells you how cautiously to use the figure.
Can I inspect the answers behind a score?
Yes. Prompt details keep the original AI answers connected to the model, market, competitors, sources, citations, and available search keywords that produced the result.
Measure your AI visibility with confidence
Start with your own prompts. Your first analysis begins immediately, with the score, confidence, mention tier, and underlying answers connected.