Arrow 
A real AI visibility service tests your brand across all seven consumer-facing answer engines — ChatGPT, Claude, Gemini, Perplexity, Grok, Microsoft Copilot, and Google AI Overviews — separately, because each one retrieves and cites sources differently. It reports monthly with engine-level detail, not one blended score. And it can actually change your site's content and structure, not just monitor it. Anyone selling "AI visibility" without those three things is selling a dashboard, not a service.
Over the last two years, "AI visibility" and "GEO" (generative engine optimization) went from a niche idea to a crowded category almost overnight. That's mostly good — it means AI-referred search is real enough that vendors are racing to serve it. It also means the category is full of rebranded SEO retainers with a new slide added to the deck, single-prompt "visibility trackers" masquerading as strategy, and agencies promising outcomes no one can actually control.
This guide is the buyer's-side version of that conversation: what to actually look for when you're evaluating a vendor, broken down engine by engine and then by three industries with genuinely different needs — B2B SaaS, nutrition and supplement brands, and yacht brokers. We'll note where Arrow fits along the way, but the criteria below apply whoever you're talking to.
What "covering" an AI engine actually requires
The single most common way vendors oversell themselves is treating "AI visibility" as one thing. It isn't. ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot, and Google AI Overviews are seven different products built by five different companies, each with its own training data, its own retrieval logic, its own citation behavior, and its own update cadence. A tactic that gets you cited in Perplexity — which shows sources on every answer — tells you almost nothing about whether Claude, which often answers from training knowledge with no browsing at all, can describe your company accurately.
A vendor that actually covers this space tests each engine on its own terms: what triggers a live-search answer versus a knowledge-recall answer, whether your brand shows up with a citation or just an unattributed mention, and how that changes over repeated testing. Below is what that looks like engine by engine — the questions a serious vendor should already be able to answer about their own methodology.
ChatGPT
ChatGPT blends live browsing with knowledge baked into the model from training. Coverage means testing both separately: what ChatGPT says about your brand with browsing off (pure training-time recall) versus what it surfaces once a query triggers live search — comparisons, "best X," anything time-sensitive. A vendor should track citations (visible source links in a browsing answer) apart from unsourced mentions, because they're won differently — citations come from crawlable, well-structured content; baked-in recall is earned over months of consistent public presence across the sources OpenAI trains on. If a vendor's "ChatGPT report" is one prompt typed once a month, that's a spot-check, not coverage.
Claude
Claude's web access is narrower and more surface-dependent than ChatGPT's, so its answers lean harder on training knowledge, and when browsing is available it tends to favor a smaller set of higher-authority sources. The useful test is whether Claude can describe your company accurately with browsing off — that reflects how consistently and accurately you're represented across the press coverage, documentation, third-party reviews, and reference sources Anthropic trained on. A vendor re-running identical ChatGPT prompts against Claude and calling it coverage is skipping this distinction entirely.
Gemini
Gemini sits inside Google's ecosystem and draws on Google's own index and grounding infrastructure — but the standalone Gemini app/API and Google AI Overviews (covered separately below) are genuinely different surfaces with different triggers, even though they share underlying data. Good AI Overviews visibility does not guarantee good Gemini-app visibility, and a vendor should test both. Gemini's grounding tends to favor pages with clear structured data and strong topical authority, so a competent vendor can show whether your schema markup and internal linking are actually shaping Gemini's grounded answers — not just whether you rank organically in Google Search.
Perplexity
Perplexity is built on live retrieval and shows its sources by default, which makes it the most directly measurable of the seven — there's no excuse for a vague "visibility" claim here, since every answer names where it pulled from. Good Perplexity coverage means tracking citation rate against the buyer-intent queries your prospects actually type, not just your brand name, and being able to show which competitor pages are winning those citations and why — page structure, freshness, how directly the page answers the question. Perplexity also has commerce and Pro "Focus" modes; if you're commerce-adjacent, ask whether the vendor checks those specifically.
Grok
Grok, built by xAI, integrates tightly with X (formerly Twitter), giving it a retrieval pipeline none of the other six engines share — a brand with active, verified presence and real conversation happening on X can surface in Grok even when it's thin elsewhere. Coverage should track visibility in Grok's X-integrated answers specifically (mentions, engagement, verified-account signals) in addition to its general web-grounded answers. Grok is also the newest and least stable of the seven in answer consistency, so a competent vendor samples it more than once per reporting cycle rather than treating a single snapshot as representative.
Microsoft Copilot
Copilot runs on Bing's index paired with OpenAI models, which makes Bing Webmaster Tools setup, sitemap submission, Bing-specific schema, and local listings in Bing Places all directly relevant — this is the one engine among the seven where a classic technical-SEO checklist has a measurable effect on AI grounding quality. It also matters more than most vendors admit: Copilot lives inside Windows, Edge, and Microsoft 365, so for B2B buyers in enterprise or Microsoft-shop environments, it may already be the AI surface they touch most often, even though it gets the least GEO attention industry-wide.
Google AI Overviews
AI Overviews is the AI-generated answer box inserted directly into Google Search results — the highest-volume surface of the seven, because it inherits Google's search volume, and the one most likely to intercept a click before a user reaches your organic listing. Ranking well organically does not reliably predict AI Overview inclusion; Google draws on a different, more answer-shaped set of signals — extractable passages, clear entity markup, corroboration across several indexed sources. AI Overviews also aren't shown consistently for the same query, even to the same user across sessions, so a single spot-check is close to meaningless — coverage requires repeated sampling over time.
The evaluation criteria that actually separate vendors
Once you've established that a vendor tests each engine on its own terms, the next filter is operational: how they report, whether they touch your actual site, and how their pricing and contracts are structured. Here's what to check and why each one matters.
| Evaluation criterion | Why it matters | What good looks like |
|---|---|---|
| Engine coverage breadth | Optimizing for one engine (usually ChatGPT) can leave you invisible on the other six, which may carry more of your specific buyers. | Documented methodology and separate results for all seven engines, not a blended score. |
| Monitoring vs. implementation | A dashboard tells you where you stand; it doesn't change your standing. | The vendor can restructure pages, add schema, and close content gaps — not just report on them. |
| Reporting cadence | Answer engines shift often enough that a quarterly check misses real movement, good or bad. | Monthly reporting minimum, with engine-by-engine breakdowns and a defined query set. |
| Query/prompt methodology | Testing your brand name alone ignores how buyers actually phrase questions during evaluation. | A query set built from real buyer-intent language, re-run on a fixed schedule, not ad hoc. |
| Citation vs. mention tracking | An unsourced mention and a cited, linked answer require different fixes and mean different things. | The two are tracked and reported separately, engine by engine. |
| Structured data implementation | Several engines (Gemini, Copilot, AI Overviews) ground answers more reliably from well-marked-up pages. | Vendor can audit and implement schema, not just recommend it in a PDF. |
| Industry / compliance awareness | Regulated or high-stakes industries (health claims, financial claims, licensing) carry real risk if content overreaches. | Vendor asks about your compliance constraints before writing anything, and flags AI-fabricated claims about your brand. |
| Contract structure | AI visibility work has a lag between implementation and measurable answer-engine change — long lock-ins before proof of movement are a risk transfer to you. | A pilot period or month-to-month option before any annual commitment. |
| Pricing transparency | Bundled "AI + SEO + content" retainers can hide how little AI-specific work is actually happening. | A clear breakdown of what's AI-visibility-specific versus general marketing work. |
The Arrow read
Where Arrow fits in this framework
We built Arrow's GEO system around the criteria above because we kept seeing the same gap in the vendors we replaced: real monitoring across all seven engines, but no mechanism to actually change what those engines find. Arrow tracks citation and mention rate engine-by-engine against your real buyer-intent queries, then implements the content and schema fixes directly — not a recommendations deck. If you want to see where you currently stand before talking to anyone, run the free AI visibility audit; it's a faster starting point than any vendor's sales pitch. Pricing and scope are on the pricing page. For a deeper look at how to compare platforms in this category, see our buyer framework for AI visibility platforms and our roundup of GEO companies for 2026.
Buyer considerations by industry
The evaluation criteria above hold regardless of industry. What changes is which failure mode actually costs you money — and that's where most vendor pitches go generic. Here's what's different for three specific buyer types.
B2B SaaS: comparison visibility and technical buyer questions
A SaaS buyer's most expensive AI-visibility gap isn't obscurity — it's being left out of the comparison. Technical buyers ask AI engines "X vs. Y," "alternatives to X," and integration- or security-specific questions (SOC 2, data residency, API rate limits) well before they fill out a demo form, and a vendor competing for that budget line needs to show up accurately in those answers, not just in generic "best software" lists. When evaluating a vendor here, ask whether they track your presence specifically in named-competitor comparison answers, and whether the comparison content they'd build represents the landscape fairly — thin "why we're better" pages get ignored by both readers and answer engines, which tend to favor balanced, specific, verifiable comparisons over one-sided pitches.
Nutrition and supplement brands: claims accuracy and regulatory limits
For a nutrition brand, the real risk isn't just being invisible — it's an AI engine inventing or misattributing a claim you never made. Structure-function claims are permitted under DSHEA; disease claims are not, and the FTC separately polices substantiation for anything an engine might paraphrase as a guarantee. A vendor should be actively monitoring for engines fabricating dosage, efficacy, or health-outcome claims about your product, not just counting positive mentions. Just as important: what a vendor is willing to promise. No legitimate provider can guarantee an AI engine states a specific health outcome about your product, and any vendor offering to "get ChatGPT to say your supplement cures X" is describing a compliance liability, not a service.
Yacht brokers: listing consistency and a long, relationship-driven cycle
Yacht buyers research for months, often six to eighteen, across multiple listing platforms before ever contacting a broker — so AI visibility here isn't a conversion channel, it's trust infrastructure during a long research phase. The priority is different from the other two industries: listing data consistency across syndicated platforms like YachtWorld and boats.com, since AI engines frequently answer from those aggregators rather than a broker's own site. Mismatched pricing, specs, or availability across platforms undermines credibility in AI answers more than it does in most industries, because there's no single canonical source the way there is for a SaaS company's own documentation. Given the small number of high-value deals in play, tracking should stay qualitative and relationship-aware — a broker cares far more about being described accurately for the three buyers actively shopping than about aggregate mention volume.
Vendor red flags worth walking away from
A handful of pitches show up across every industry above and are worth naming directly, because they cost real budget before anyone notices the work isn't happening.
None of this requires exotic tooling to evaluate. It requires asking a vendor to show their work: which engines, which queries, how often, and what they're actually allowed to change. Most of the category won't have a good answer to all four.