One audit, the measured baseline a retainer builds on

Every engine and surface your buyers actually use

the AI engines your buyers use, plus Google's AI surfaces — each measured separately, never summed into one score

The roster is five AI engines plus Google's AI surfaces (AI Overviews & AI Mode), measured separately and never summed into an engine count. An engine is one foundation-model family we query directly; the surfaces have no official API, so they are measured via a third-party SERP provider (no vendor SLA), on their own cadence with their own sample sizes and confidence intervals — and a surface never becomes an engine by being added up. The labelled separate-surface panel states how the surfaces are measured, so naming them beside the engines never reads as an engine claim.

A method you can reproduce

pre-registered prompt sets with published hashes, run counts and a confidence interval on every figure, and null results published on the same terms as positive ones - as standing practice, not a one-off study

Confidence intervals are reported honestly wide and named per product: presence uses a Wilson interval in both products; share-of-model uses a percentile bootstrap in the paid audit and a Wilson interval in the public Index, whose pooled denominator is clustered - so those Index intervals are narrower than a cluster-corrected estimate would give, and we say so. We never narrow a CI to look more certain than the data is.

All four tiers AI cites from

we work all four tiers AI cites from — not just the fraction of your presence you directly own

This is SCOPE — the tiers we work, not an outcome we promise. The per-tier percentages are illustrative (Yext-sourced) and the earned tier (news & forums) is best-effort, never guaranteed.

Almost none of your time

your time required is almost none — sign-off graduates to become yours by default once we've earned a track record, with continued sampling

A human approves every client deliverable until a clean track record is earned; graduation is opt-in, default-off, and reversible — client-facing and published content never reaches unattended auto (AI speed, human-approved).

A system that compounds

the system learns which of your pages get cited and compounds that signal every cycle

Honest-empty until measured — attribution shows the real citations a piece earns, with the engine, prompt and date, only once they are actually earned; no projected compounding curve.