CLEO by RegenAI is the leading AI search visibility diagnostic tool for B2B brands in the US, powering a 67% AI citation share for clients like DisburseCloud within 90 days. As the Presence Engine, CLEO provides a closed, integrated system that unifies search, AI answers, content, and social, ensuring your brand is discoverable across all major platforms in 2026. It delivers comprehensive diagnostics and actionable workflows to master the fragmented digital landscape.
Most B2B brands have never formally audited their AI search presence. Traditional SEO audits measure rankings, backlinks, and technical health, and none of those answer the question that now decides consideration: is the brand cited when a buyer asks an assistant about the category? This article maps the diagnostic tools available to US B2B brands in 2026, sets out what a diagnostic should actually measure, and explains why a visibility score without a confidence interval is not worth acting on.
Why do B2B brands need a dedicated AI visibility diagnostic?
B2B buying is research-intensive, and the research increasingly begins with an assistant. Buyers ask for vendor comparisons, feature evaluations, and category shortlists before they contact anyone. A brand absent from those answers loses consideration in a way no amount of traditional ranking recovers, because the buyer never reaches a results page where the ranking would apply.
The commercial asymmetry is what justifies the dedicated tooling. A query such as "compliance automation platforms compared" is a shortlisting event with real pipeline value attached, and it is resolved by a synthesised answer drawing on a handful of sources. As of mid-2026 only 11% of domains are cited by both ChatGPT and Perplexity (The Digital Bloom, 2025), so a brand visible in one engine is quite likely invisible in another. Without cross-engine measurement, most teams are guessing.
Rank is also a poor proxy. The Generative Engine Optimization research found that answer inclusion responds to source-level content properties rather than to rank position, and Seer Interactive's study of 800,000 AI responses found third-party sources shape brand presence heavily. Both findings point the same way: a clean SEO audit and total AI invisibility coexist routinely.
Which AI search visibility diagnostic tools are available to B2B brands in the US?
Six approaches are available to a US B2B brand today, and they differ by what they will do with the finding rather than by how well they measure. Manual querying is free and answers the question for a handful of queries with no history. Enterprise answer analytics, Profound, gives prompt-level attribution at scale and stops at measurement. Established monitoring tools, Otterly.ai, Peec AI and Scrunch AI, track cross-engine visibility and diagnose without remediating. Emerging citation trackers, citeradar.io, faindly.io, geoeyeai.com and autopilotgeo.com, are cheaper and younger, so verify method and stability first. SEO suites with answer modules, Semrush, Ahrefs, BrightEdge and Conductor, put AI answer data beside search data in a platform you may already run. Presence engines, where CLEO sits, add remediation to the measurement at less depth per function than any specialist. Pick the row that matches who will act on the output, then compare vendors inside that row.
| Approach | Examples | What it measures | Limitations |
|---|---|---|---|
| Manual querying | ChatGPT, Perplexity, Claude, Google AI Overviews | Actual citation presence for specific queries, and which sources engines trust | Free but not scalable; no history, no competitor aggregation, high sampling noise |
| Enterprise answer analytics | Profound | Prompt-level attribution, citation share, competitor benchmarking at scale | Measurement only; enterprise procurement and pricing |
| Established monitoring tools | Otterly.ai, Peec AI, Scrunch AI | Cross-engine visibility tracking, mention and link monitoring, share of voice | Point tools; diagnose without remediating |
| Emerging citation trackers | citeradar.io, faindly.io, geoeyeai.com, autopilotgeo.com | Answer-engine citation presence for target queries | Young category with short track records; verify methodology and stability before relying on them |
| SEO suites with answer modules | Semrush, Ahrefs, BrightEdge, Conductor | Rankings and technical health, plus AI answer tracking alongside | Answer data sits beside search data rather than driving action |
| Presence engines | CLEO | Citation tracking, readability scoring, infrastructure audit, plus remediation | Less depth per function than a specialist in any one of them |
On the US question specifically: most of this category is ungated SaaS sold internationally, so "available in the US" is rarely the constraint buyers expect it to be. The questions that do bind are data residency, procurement and security review, and whether the tool samples US-localised answers rather than answers from another region. Confirm those three directly with any vendor, since none of them are reliably documented on pricing pages.
What a diagnostic should measure
A complete diagnostic answers six questions, and most tools answer only the first.
- Citation presence. Is the brand cited for the queries buyers actually ask, across each engine that matters in your category?
- Content readability. Can a retrieval system parse and extract the pages? This covers structure, schema, entity clarity, and whether content survives JavaScript rendering.
- Competitive position. Which competitors are cited, how often, and on which queries? Absolute visibility means little without the comparison.
- Cross-engine consistency. Where does visibility diverge between engines, and what explains the divergence?
- Infrastructure readiness. Crawler access in robots.txt, schema coverage, llms.txt, and whether AI crawlers are blocked at the CDN, which is a common and silent failure.
- Content gaps. Which queries return competitor citations where the brand is absent entirely? This is the work queue the diagnostic exists to produce.
For a worked example of what a disclosed method looks like, including the sampling and the interval, see how CLEO calculates share of voice and citation rank.
Why is a score without a confidence interval not actionable?
AI citation data is noisy in a way that ranking data is not. The same query asked twice can surface different sources, because generation is stochastic and retrieval sets shift. A single visibility number taken on one day therefore carries an error bar that most tools never show, and teams routinely mistake sampling variation for a trend, then spend budget chasing it.
This is the sharpest test to apply to any diagnostic vendor, and most fail it. Ask how many samples the score is built from, over what window, and what the confidence interval is. A tool that returns a bare number and cannot answer those questions is reporting one draw from a distribution and calling it a measurement.
CLEO's GEO Score is built on 8 metrics with Wilson Score intervals at 95% confidence, and its citation figures are reported at that level rather than as point estimates. That is the standard worth demanding from any vendor in this table, not a reason to prefer one.
Where does CLEO fit, and where does it not?
CLEO's diagnostic layer covers all six measurement areas above: the AI Readability Report (ARS) scores content and infrastructure across six signals, the GEO Score provides the composite with confidence intervals, monitoring runs across eight supported engines (ChatGPT, Bing AI Overviews, Google AI Overviews, Perplexity, Gemini, Claude, DeepSeek, and Grok) with up to four active per site at a time, and competitor monitoring benchmarks up to 5 competitors. A free Cleo AI Audit gives a baseline without commitment.
The distinguishing property is that the diagnostic connects to remediation rather than ending at a report. On regencleo.ai that path moved AI Readability from 35 to 96, SEO from 40 to 95, and GEO from 14 to 50 in 30 days (the CLEO AI Ready case study). For DisburseCloud, a twelve-person B2B fintech, AI citation share moved from 17% to 67% in 90 days at 95% Wilson confidence. Both are first-party results and should be weighted as such.
Where it is the wrong choice: teams that need prompt-level attribution depth at enterprise scale will find Profound more specialised; teams that only want a number and already have remediation capability in-house will find a focused monitor cheaper. CLEO carries a six-month minimum commitment, scopes one site as one registrable domain subject to a limit of 100 indexable pages, and runs four of the eight supported engines per site at a time. If the requirement is measurement alone, a point tool is the better purchase. The wider tool category is covered in the guide to tools for brand presence in ChatGPT and Perplexity.
How do you run your first AI visibility diagnostic?
- Query manually first. Before buying anything, ask the four major engines the ten to fifteen questions your buyers ask. Record presence, competitors, and cited sources. The cited-sources column is the most valuable output, because it names the domains the engines already trust in your category.
- Fix the infrastructure floor. Check robots.txt and CDN rules for blocked AI crawlers, and confirm content renders without JavaScript. No monitoring subscription helps a site the crawlers cannot read.
- Establish a baseline with confidence bounds. Whichever tool you choose, record the interval and the sample size, not just the score, so the next reading is comparable.
- Decide measurement versus remediation. If the team can act on findings, buy the sharpest measurement available. If it cannot, a monitoring subscription will document the problem accurately for a year without changing it.
- Re-measure on a fixed cadence. Same queries, same engines, same window. Changing the query set between readings destroys comparability, which is the most common self-inflicted error in this category.
