Every question, answered once and published openly

AI search visibility is whether conversational engines name your brand when a buyer asks for a recommendation. Instead of ten blue links, ChatGPT, Claude, Gemini, Perplexity and Google AI surfaces return a two-to-three option shortlist. If you are not in it, no click, no impression and no analytics event records the loss — the buyer simply never learns you exist.

Each brand tracks ten grounded buyer-intent prompts across six AI surfaces, producing sixty checks per weekly cycle. Every check yields a recommendation stance, a sentiment, a position and a citation list. Visibility is position-weighted and stance-weighted, then normalised to a 0–100 score so it is directly comparable week over week.

A bounded parallel crawl reads your homepage, pricing page, features or documentation and llms.txt, plus the same pages on each competitor, under a hard latency ceiling. That grounding produces ten realistic buyer prompts across awareness, consideration and decision stages, and opens sixty checks across six surfaces.

The first snapshot exists as soon as the first cycle of answers has been extracted and scored, so you see real stance and citation data from week one. Trend, drift detection and measured lift need a second complete week, because a delta requires a prior point to compare against.

SEO optimises for a ranked list of links. AEO optimises to be the extracted answer. GEO optimises to be the source a generative engine reasons over and cites. The same foundations serve all three, but AEO and GEO weight structure, crawler access, entity clarity and third-party corroboration far more heavily than keyword density.

Run generation, response extraction, scoring, drift detection and remediation drafting are automated. Answer collection runs on a weekly cycle rather than continuously, so this is drift detection — not live monitoring, and not a real-time alerting system.

Every gap becomes a ranked asset with an effort badge, ranked by time to value: a five-minute objection-buster answer, an llms.txt file, entity disambiguation schema, a versus blueprint, then citation authority outreach. Each one ships with draft copy you can paste or hand to our engineers.

Yes. The platform exposes a Model Context Protocol server at /api/mcp with six brand-scoped tools covering citation reports, prompts, sources and the action queue, authenticated with per-brand bearer tokens so an engineering agent can pull visibility data into your own workflows.

Four areas scored out of 100: crawler access for GPTBot, ClaudeBot, PerplexityBot and Google-Extended worth 25 points; llms.txt and llms-full.txt worth 15; Schema.org coverage worth 35 including Organization, sameAs, Product, transparent offers and FAQPage; and semantic structure worth 25 for headings, tables, text density and landmarks.

Five drift signals are detected: a cautioned-against stance, negative sentiment, a stance drop from first choice or recommended to alternative or mentioned-only, a competitor taking first choice on a decision-stage query, and a weighted visibility fall of five points or more between two complete weeks.

Five deliverables generated from the detected incident: the cleaned objection, a forty-five-word refutation that opens by naming the claim as inaccurate, a FAQPage JSON-LD block, a verified-facts section you can append to llms.txt, and a five-step remediation plan ending in re-running the weekly cycle.

No. Asset generation is bound by a no-fabrication rule: facts come only from observed crawl and citation data, and anything unverified is emitted as an explicit placeholder marker for you to complete. There is a verification script that fails the build if a zero-price offer template or a missing placeholder marker appears.

No — it is a model, and it is labelled as one. It multiplies decision-stage query count by four to ten monthly buyer evaluations per query, then applies your omission rate. It estimates exposure ceiling, not revenue, and the platform does not report revenue from it.

No, though they share foundations. SEO competes for rank in a list a human scans. GEO competes to be the source a model retrieves, reasons over and cites inside a synthesised answer. Entity clarity, crawler permissions, machine-readable structure and third-party corroboration carry far more weight in GEO than link ranking does.

It is a proposed convention rather than a ratified standard, and no major provider has committed to it publicly. It costs almost nothing to publish and it makes your hierarchy explicit for any agent that reads it, so the expected value favours publishing it — but treat it as a low-cost signal, not a guaranteed ranking factor.

Stance describes how an engine framed you: first choice, recommended, alternative, mentioned only, or cautioned against. A mention is not a win — being listed fourth as a fallback is materially different from being named as the primary recommendation, so the score multiplies positional weight by stance weight.

Drift is the change in how engines describe you between cycles. Models are re-indexed and re-trained continuously, and competitor content and third-party sources shift underneath you. A brand can lose a first-choice position without any change of its own, which is why week-over-week comparison matters more than any single reading.

They are structured to be checkable: our capabilities reference the shipped implementation — sixty checks per cycle, five stances, five drift signals, five remediation tiers and six MCP tools — while competitor capabilities describe public positioning as of September 2026. Prices change, so every competitor price says to check the vendor.

Every comparison names it plainly: pick Otterly for the cheapest first read, Profound for enterprise compliance, Peec AI for many locales, Writesonic for draft volume, Semrush for an entrenched keyword workflow, Geoptie for a capped starter. Pick GEO when you need the reason behind every loss plus the drafted fix.

Export prompts as CSV, map categories to commercial, informational, transactional or branded, re-ground ten per brand across awareness, consideration and decision stages, and baseline-lock on import week so the first delta is honest. Newly tracked prompts backfill into the current cycle.

No, and any vendor who does is guessing. AI surface answers are not a system you control. What is guaranteed is measurement: every completed action gets a locked baseline and a measured after-value on the following cycle, so you see exactly what moved and what did not.