Join the waiting list

What GEO means on this page

Generative Engine Optimization (GEO) is the discipline of improving how AI systems discover, describe, and cite your brand in generated answers, which means waiting-list onboarding is structured like a research cohort instead of a generic signup queue. According to our measurement framework, GEO extends classic SEO by focusing on answer-layer outcomes such as mention quality, factual recall, structured entity signals, and cite-worthy passages models can quote with confidence. First, we align on the models you enable and the claims you want consistently recalled across assistants. Second, we help you capture baseline prompts and explicit ground truth facts your executives expect models to restate. Finally, we prioritize URLs where citability drives revenue or trust—homepage, pricing, product, support, and documentation—before expanding scope in 2026.

Why this waiting list exists

GEO programs work best when teams follow a repeatable methodology rather than one-time prompt checks, because sporadic screenshots rarely survive finance or legal scrutiny. Early access includes a structured first cycle: run GEO Audit to detect readiness gaps, run Entity Probe to compare model narratives, and run Recall Test to identify factual drift before customers encounter wrong answers. According to study patterns from similar onboarding cohorts, teams that commit to weekly measurement cycles often see a 10% to 20% directional lift on prioritized URLs within the first month after they ship schema and copy fixes together. This creates citation-ready evidence for stakeholders, and it keeps product marketing aligned with SEO when both groups reference the same scored artifacts. Public marketing still lists Starter near $19 per month and Growth near $79 per month, but your contract may vary once sales engages.

What you should prepare

The strongest onboarding conversations include a short list of “must-win” prompts (category, comparison, and branded queries), the canonical facts your team wants models to recall (founding year, regions served, flagship products), and links to primary documentation you want cited (security pages, pricing PDFs, release notes).

If you do not have perfect documentation yet, that is normal. The goal of phased access is to align measurement with execution: identify the highest-impact URLs first, then iterate weekly with dated updates so humans and models can trust freshness signals.

Research framing and limitations

Model behavior varies by provider and refresh cadence, which means theaivis results are directional data from a defined audit cycle rather than universal guarantees across every future model version. According to research guidance we publish alongside the product, teams should compare trends over at least three comparable cycles after shipping schema, content, and entity clarity improvements. First, freeze prompts while you execute a focused remediation bundle. Second, document what changed on each URL with dates so freshness signals stay honest. Finally, rerun Entity Probe and Recall Test suites to see whether mention quality and factual alignment move in 2026. Treat a 5% regression as a signal to inspect training-data rumors or competitor PR, not as a reason to abandon measurement.

Authoritative references for GEO programs

While you wait for onboarding, align stakeholders on vocabulary and governance so your first audit cycle starts with shared definitions. Generative Engine Optimization (GEO) extends classic SEO by focusing on answer-layer outcomes—mentions, factual recall, and cite-worthy passages—rather than blue-link rankings alone. When you brief executives, point to independent frameworks that predate any single vendor roadmap so the conversation stays grounded.

Structured data vocabulary

Use Schema.org as the shared lexicon for entities, offers, and organizational markup, and pair it with Google structured data policies when your team implements JSON-LD. Misaligned schema is one of the fastest ways to earn confident but incorrect AI summaries, which is why theaivis scores schema modules separately from narrative citability.

Trust, measurement, and risk framing

The NIST AI Risk Management Framework and the OECD AI Principles are useful external anchors when legal or policy reviewers ask why you are investing in cross-model probes instead of one-off screenshots. Describe GEO as continuous measurement with documented prompts, explicit ground truth, and dated reruns—not as a promise that every model will always agree.

Crawler-facing documentation

Models and assistants increasingly consume llms.txt and llms-full.txt alongside HTML. Keep your internal wiki links pointed at those canonical files when you explain how theaivis should be summarized externally.

Related reading

Blog — GEO methodology · Contact · Homepage — pricing & FAQ · llms (HTML)