When your buyers ask AI for the best DevOps & dev tools, does your name come up?

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.

Same prompts, opposite answers

In CI/CD platforms, Buildkite appears in 50% of Grok's answers but only 3% of Claude's (n=100 per engine, 2026-07-01). In feature flags, GrowthBook shows up in 76% of Claude's answers vs 39% of Perplexity's (measured 2026-07-01). Optimizing for "AI" as one thing misses half your buyers.

The engines don't even read the same sources

In our CI/CD measurement (2026-07-01), the overlap of cited source domains between engine pairs averages roughly 4% (mean Jaccard) — each engine trusts a different mix of comparison posts, community threads and vendor content. That's why we fix citations per engine, not once per site.

Open categories exist right now

In vector databases, the top three — Weaviate (19.1%), Qdrant (19.0%), Pinecone (18.8%) — sit within 0.3 points of each other with overlapping 95% confidence intervals (measured 2026-07-01): a statistical tie at the top. Nobody owns that answer yet.

Your own domain barely gets cited

In the same CI/CD run, 98.4% of the sources engines cited were third-party — reddit.com and dev.to among them — and even the #1 tool's own domain was cited in under 5% of answers (measured 2026-07-01). The work is mostly off-site, which is exactly what the off-site authority pillar below does.

1. Money prompts

We build the exact questions your buyers ask AI. Real prompts from our pre-registered Index sets: "What CI/CD tool should we use for a Kubernetes microservices stack?", "Best PagerDuty alternative for a budget-conscious startup?", "LaunchDarkly vs Statsig for progressive rollouts?" — yours come from your category language and sales calls.

2. Measure every engine

Each prompt runs 10+ times across ChatGPT, Perplexity, Claude, Gemini, and Grok — median + 95% confidence interval, because a single answer is noise.

3. Your share of model

How often each engine recommends you vs. named competitors, plus the exact prompts where they win and you're invisible — and the sources the AI pulled from.

4. Fix, build & monitor

Done-for-you: we fix the SEO foundation, produce the content, and earn the off-site authority on a prioritized roadmap — AI-speed, you-approved, with every asset tuned to what actually performs in your niche (researched across YouTube, Reddit, Dev.to, Hashnode, Medium, Stack Exchange, and search) — then track weekly so your share of model climbs and stays. The full-stack service →