Get your developer tool recommended when engineers ask AI

Every engine and surface your buyers actually use

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

The roster is five AI engines plus two answer surfaces — AI Overviews and Microsoft Copilot — reported separately and never summed into an engine count. The five engines are measured today. Both surfaces are positioned, not measured: an engine is one foundation-model family we query directly, a surface is a product that renders an answer, and a surface never becomes an engine by being added up. Outside the labelled separate-surface panel we name the Google surface plainly as AI Overviews, because naming it beside the engines would read 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.

Your share of model

How often each engine recommends you vs. named competitors (e.g., Datadog, Grafana, New Relic), with confidence intervals.

The gap map

The exact prompts where competitors win and you're invisible — and the third-party sources (Reddit, G2, comparison pages) the AI pulled from.

A prioritized fix list

Technical (schema, AI-bot access), entity (how AI identifies your product), and off-page (where to earn citations) — sequenced by impact.

Ongoing monitoring

Roughly 40–60% of AI-cited sources change month over month (Profound). We track weekly and keep you in the answer as the engines change.