Frequently asked.

Does AnswerShare affect my SEO?

No. AnswerShare adds a parallel translation layer that AI crawlers receive. Googlebot continues to see your site exactly as it does today. SEO rankings, schema, link equity — all unchanged.

AnswerShare optimizes the path AI systems use for live retrieval — a layer that didn't exist as a discipline until live RAG architectures became mainstream.

How is this different from SEO?

SEO gets you into the candidate pool that AI retrieves from. ASQ determines whether you are actually retrieved, parsed, grounded, trusted, and cited.

The mechanical difference: traditional SEO scores you against the query the user typed. AI retrieval scores you against the 5–20 sub-queries the model generated per user prompt. A page at retrieval rank 1 against a sub-query is cited 58.4% of the time; at rank 10, only 14.2% (Indig / AirOps, N=16,851). The position SEO tools report is the wrong position.

Both matter. AnswerShare doesn't replace SEO — we optimize the layer above it. "AI hasn't replaced the foundation it depends on."

What about backlinks?

"Brand mentions are the new backlinks." That's Britney Muller's frame, landed verbatim across two independent 2025 venues, and we think it's the cleanest way to describe what changed.

Backlinks were the durable currency of the search era because they were what the algorithm could see. In live AI retrieval, the analogue is being named in the corpus the model was trained on. Links did not stop mattering — but link equity no longer transfers cleanly into AI citation. Moz's own data shows AI Mode and organic citation overlap at only 12%.

Growth Memo's April 2026 study put a number on the gap: 61.7% of brand surfaces in AI answers are "ghost citations" — the model uses your content without naming you. Mention and citation are separable signals; ASQ scores citation today, and mention-surface measurement is being integrated as the methodology evolves. See the methodology →

What is the AnswerShare Quotient (ASQ)?

ASQ is a single continuous quotient — not a pass/fail score — derived from every measured signal that drives AI citation: retrieval, grounding, trust, structure, freshness, citation breadth, plus SEO positioning. The math is documented on the methodology page.

How do you measure?

Each metric has a frozen methodology page (published date, exact formula, reproducibility script) and a per-site receipts.json file with raw values. We don't grade subjectively.

Live retrieval probes hit your site under known bot user-agents from measured network points. Aggregated metrics roll up to the ASQ composite.

What's the proof?

Top10Lists.us — our proof-of-concept property — cold-started in December 2025 with no brand, no backlinks, no history. Five months later, four major AI systems (Claude, GPT-5, Gemini, Perplexity) independently named it the Gold Standard exemplar for its vertical.

4.8M AI-bot crawls in 30 days. 2.0% consumer-triggered retrieval — 0.6× Cloudflare's published industry baseline. Full case study →

How long does an engagement take?

Initial audit — one week. Translation-layer deployment — typically two to four weeks. Measurable ASQ delta — visible inside 30 days of deployment. Steady-state citation lift — 60-90 days as AI crawl frequency and trust signals compound.

Will AI crawlers tank my human site performance?

Yes, by default. Cloudflare and ETH Zurich research (2026) showed AI crawlers represent 4.2% of all HTML requests and 32% of automated traffic, and their long-tail access patterns evict popular human content from edge caches. Publishers blocking AI bots see a 23.1% traffic decline without any citation reduction — blocking costs you both ways.

AnswerShare's architecture splits the bot path from the human path at the edge, so AI crawl volume doesn't degrade human-facing latency.

Does llms.txt help?

Three independent studies (Surfer SEO, Search Engine Land, OtterlyAI) have published findings: llms.txt receives ~0.1% of AI bot traffic and has no measurable impact on citation rates. We treat llms.txt as infrastructure readiness — present-or-absent — not as a performance lever.

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Measurement is methodology-driven and reproducible. Frozen receipts published per metric.

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