The provenance standard: every row says where it came from.
AI search is full of confident numbers with no sourcing behind them. Citabld publishes the opposite standard: every question in every library carries a label describing how it was produced, and that label survives into scoring, reporting, and the build sheet a client signs.
A provenance label is a required field on every question row in a Citabld library that records how the row was made: MEASURED from a tool's own surface, DETERMINISTIC from a reproducible rule, EXTRAPOLATED from a comparable tool, or MODELED from behavior profiles. Labels stop projections from being read as observations.
The four labels
Captured directly from a tool that shows its own searches, like Perplexity's displayed search steps or a citation panel returned with the answer.
The only label that supports a claim about what a tool actually did.
Produced by a documented, reproducible rule rather than an observation, like expanding a query template across a known product line or set of locations.
Safe to act on, because anyone can regenerate the same rows from the same inputs.
Derived from measured behavior on one tool and projected onto a similar tool with published architectural similarity.
Directionally reliable, weaker than measured, and always flagged with the source tool.
Generated from behavior profiles when the tool exposes no search trace at all, which is the case for Gemini and Google's AI surfaces.
Useful for planning, never presented as evidence of what the tool did.
A number without provenance is a decoration.
The only label that supports a claim about what a tool actually did.
Safe to act on, because anyone can regenerate the same rows from the same inputs.
Directionally reliable, weaker than measured, and always flagged with the source tool.
Useful for planning, never presented as evidence of what the tool did.
Why the standard exists
The AI visibility market grew faster than its evidence. Tools report share of voice across tools that publish no retrieval data, and agencies hand over query lists as if they watched the tool ask them. Both are guesses dressed up as measurement. When a client can't tell which rows were watched and which were generated, they can't spend budget rationally, and they can't tell whether a result came from the work or from noise.
Labeling fixes that at the row level rather than in a disclaimer nobody reads. A theme built on twelve measured rows and three modeled rows is a different investment than one built on fifteen modeled rows, even when both look identical in a coverage chart. Our scoring treats them differently, our reports state the mix, and our recommendations say plainly when a decision rests on a model.
What is a provenance label?
A provenance label is a required field on every question row in a Citabld library that records how that row came to exist: MEASURED, DETERMINISTIC, EXTRAPOLATED, or MODELED. The label travels with the row into scoring, into the build sheet, and into every deliverable, so no reader mistakes a projection for an observation.
Why can't every question be measured?
Because most tools don't publish their internal breakdown. Perplexity shows its search steps, and some surfaces return citation sets that reveal what was pulled, but Google's AI Mode and AI Overviews, Gemini, and most assistants expose no query logs to anyone, including their own advertisers. Any vendor selling you measured Google fan-out data is selling you a model with the label taken off.
How does provenance change what gets built?
The priority math weights measured rows above modeled ones, so production money flows first to themes with observable evidence behind them. Modeled rows still earn coverage, because a theme with no content can't get cited on any tool, but they never justify a claim in a report.
Every row in your library will say where it came from.