1. What one check is
One check is one question sent to one engine once, asked as though the searcher is in the United States. The engine's full answer is stored, with the date, whether it searched the web, and every page it cited. Keylight then reads the answer for brand names. Yours, your competitors', and any others.
2. Why 25 questions and not 5
A topic like "AI writing tool" becomes about 25 different questions, phrased the way people type them. Five questions would give a score that jumps 20 points when one answer changes. Twenty-five is the point where adding more questions narrows the margin slowly enough that the money is better spent on another topic. Nothing is asked twice in the weekly run.
3. The margin of error, with a worked example
Jasper appeared in 8 of 25 answers on ChatGPT this week. The appearance score is 32%. The margin of error at 95% confidence on 25 observations is about ±18 points. The honest statement is: somewhere between 14% and 50% of the time, ChatGPT names Jasper when asked one of these questions. Keylight prints 32% ±18 and never the bare 32.
margin = 1.96 × √( p (1 − p) / n ) = 1.96 × √( 0.32 × 0.68 / 25 ) ≈ 0.18
reported: 32% ±18
4. When a change counts
Engine answers vary from one check to the next. Keylight calls a change real only when the two saved results are far enough apart that ordinary variation is no longer a believable explanation. Otherwise the product says within noise. The decision comes from the answers behind those two results. Keylight does not run a separate monthly calibration or use a fixed band for any engine.
5. The prominence reading
Appearance counts whether the brand was named. Prominence weighs each appearance by two things: how far into the answer the brand first appears, and whether the sentence around it recommends the brand, lists it neutrally, or names it as something to avoid. Each appearance scores 1.0 for a recommendation in the first third of the answer, down to 0.1 for a caution at the end. The workspace figure is the mean across appearances. It is labelled our reading everywhere, because the second part is a judgement. It is never shown on its own and never blended with appearance.
6. How each engine is read
Five engines are read through the developer connection the engine's maker provides, with web search turned on. Three have no such connection for their consumer search product, so a supplier reads the engine's own web page as a signed-out US visitor would see it. The table below says which is which. Where an engine is read through its web page, the answer is what a person would have seen that day.
7. Searched, or answered from memory
Every answer records whether the engine searched the web. Some engines sometimes decide a question does not need a search and answer from what they were trained on. Those answers count toward the score, because a buyer saw them, and they are marked "the engine did not search" on the evidence page so you know the answer reflects the engine's memory, not the web this week. DeepSeek cannot search the web at all, and every DeepSeek answer carries that note.
8. The eight engines
Engine names and logos identify the services measured and imply no affiliation or endorsement.