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Every completed scan is stored to your org’s long-term memory. Intelligence rolls that history up into cross-scan patterns: your own per-lens averages, the average per-second curve for each lens, and the seconds where your content repeatedly drops. Add a q= query and it also runs semantic recall across your stored scans, returning the most relevant moments. Everything here is strictly org-isolated: the rollup reads only your org’s scans, and recall searches only your org’s memory.

Query Parameters

string
A natural-language query. When present, the response includes a recall array of the most semantically relevant stored moments. Omit it for just the rollup.

Response

number
How many completed scans are in your org’s memory.
object
Your org’s own benchmark: the average 0–100 score per lens across all your scans.
object
For each lens, the average score at each second across your scans: your typical per-second shape.
object[]
The seconds where your content repeatedly weakens, ranked by how often. Each entry is { second, lens, count }.
string
Echoes q when supplied.
object[]
Present when q is supplied. The most relevant stored moments.