Contact theaivis
Published · Updated · Author: theaivis editorial team
Questions about Generative Engine Optimization (GEO), Entity Probe methodology, pricing design, or implementation planning are welcome. We support teams using a measurement-first approach to AI visibility and usually reply within one business day.
GEO is defined here as the practice of improving how AI systems discover, describe, and cite your brand in generated answers. That differs from classic SEO alone because the success metric includes answer-layer quality (mentions, factual alignment, and quotable passages), not only ranking positions in traditional search results.
According to our measurement framework, the fastest contact resolutions arrive when you include the domain under review, the models that matter for your funnel, and the URLs where citability failures would hurt revenue or compliance. First, describe the customer questions you want assistants to answer faithfully. Second, note whether you already run GEO Audit or Entity Probe cycles internally. Finally, ask whether you need schema.org updates, content depth targets, or pricing copy checks prioritized in 2026. Public marketing still references Starter near $19 per month and Growth near $79 per month, while Enterprise quotes remain bespoke, so finance teams can sanity-check budgets before deeper scoping. Additionally, a 10% directional improvement on flagship URLs is a realistic outcome when teams iterate weekly with documented releases.
External standards we reference
When we discuss structured data, we align vocabulary with Schema.org and implementation expectations described in Google Search Central structured data guidance . For AI governance conversations—not legal advice—we often cite the NIST AI Risk Management Framework and the OECD AI Principles as neutral framing for measurement, transparency, and human oversight.
Prefer email over forms — it keeps threads easy to forward inside your team.
Product sales & support: support@theaivis.com
Who we help
We work with marketing, SEO, product marketing, and growth teams that need a repeatable way to measure AI visibility. Typical threads include homepage and pricing clarity, schema.org coverage, help-center depth, and cross-model comparisons after product launches or repositioning.
A useful mental model is to treat GEO like an experiment program: define hypotheses (for example, “Organization JSON-LD will improve entity consistency”), ship changes, and re-measure with the same prompt set so results are comparable week over week.
How we typically respond
Most messages receive a first reply within one business day. We may ask for your primary domain, the models you care about, and which URLs matter most for revenue or trust (for example pricing, security, integrations, and documentation). That context helps us point you to the right workflow in the product and to public references such as the GEO blog and the homepage GEO guide.
About theaivis
Published · Updated
theaivis is a Generative Engine Optimization (GEO) platform: software for measuring how AI systems describe, rank, and cite brands in real-world answer scenarios. Our approach combines research-framed diagnostics (GEO audits), comparative prompt studies (Entity Probe), and factual memory validation (Recall Test), then converts findings into prioritized execution workflows.
Built by a remote-first team focused on reproducible AI visibility outcomes: clear definitions, controlled measurement cycles, and evidence-backed content and schema improvements.
In practical terms, teams use theaivis to answer three recurring research questions: (1) how consistently is the brand mentioned across models, (2) how accurate are factual details in generated answers, and (3) which page-level or schema changes are most likely to improve citation readiness. This framework helps marketing and SEO teams tie operational work to measurable outcomes.
If you are evaluating access, start from the public narrative on the homepage, review pricing on that page, and join the waiting list for phased onboarding. For methodology depth, see the blog and the machine-readable llms overview.