What a DecodeIQ result measures
One product. The same six questions.
No prompt slots to fill.
Enter a product URL. DecodeIQ drafts six unbranded questions from the product and category, then freezes them for later checks. Each question runs across ChatGPT, Gemini and Perplexity in three waves, for up to 54 answers. The question text and product identity stay frozen for re-checks.
Questions come from product identity and category. They do not come from a Voice Map.
Page Readiness is page analysis.
The live ChatGPT result is a separate observation.
Page Readiness is rounded from 40% Technical Readiness and 60% Buyer-Language Fit. Technical checks score pass as 100, partial as 50 and fail as 0. Non-applicable checks leave the average. If none apply, the score uses Buyer-Language Fit alone.
This is not the paid product-appearance metric.
Buyer-Language Fit reads five page signals: buying criteria, objections, use cases, outcomes and comparison anchors. Each receives 10 points for buyer phrasing, 5 for specification phrasing and 0 when absent. No seller-language examples adds 10 points; one or two adds 5; three or more adds 0. The page-analysis total is divided by 60, multiplied by 100 and rounded. Live mentions and winner-language alignment add no points to the current score. A language classifier is involved, so this is a diagnostic reading, not a measured share of shoppers.
Older v1 cached rows can contain the prior blended calculation. Compare results only within a compatible report version. The readiness bands describe page checks, not the chance of a recommendation.
Keep collection limits beside the result
Read engine status before comparing checks.
A paid partial check is chargeable only when an engine completes all six questions across all three waves. A check with no completed engine is not charged. Detection statistics require the full 54-record set. A partial result can show evidence without those statistics.
Gemini grounding is unknown and excluded from grounded comparisons.
Collection methods differ by engine. The current paid Perplexity collector uses a live LLM response endpoint under the shared record contract. Do not read engine names as a promise of identical consumer-interface collection. Answer context, model changes and response variation can affect a comparison. The current product does not support a claim that every shopper saw the recorded answer.
Compare the same stored questions with compatible scorer and normalizer versions. A changed question set is not a valid same-question comparison. An accessible page can be recorded as an intervention surface. Access may be unknown or blocked. For an Amazon-listing-only edit, keep external-crawler observations separate from Amazon-owned shopping systems.
Observed change does not establish cause.
We do not claim that generated content moves visibility scores.
A re-check shows what changed in the recorded answers. It does not show that your content caused the change. A change also does not establish traffic, sales or revenue from the recorded answer. Review content as an edit to evaluate, then inspect later evidence.
Do not treat a before-and-after example as proof of uplift.