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Three agencies in the available evidence publish specific B2B outcomes tied to AI search, but those outcomes are not equivalent: one reports LLM referral revenue, one reports its own AI-sourced pipeline and leads, and one reports a client’s AI-attributed demo requests. None of the examples is independently audited in the material available here. This is therefore an evidence-led shortlist, not a verified ranking of seven “best” agencies.
That distinction matters: visibility, referrals, demos, pipeline, and closed revenue are different measures. Use the reported results below to decide what to investigate—not as a promise of what another company will achieve.
Which AI search optimization agencies have B2B outcome evidence?
Optimist, Blend, and Atlantic Zero have published concrete outcomes in the evidence available for this comparison. Their claims cover different stages of the acquisition funnel, and their proof differs in who the work was for and how clearly the measurement is described.
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|---|---|---|---|
| Optimist | 4,900% increase in LLM referral revenue and 2,622% growth in LLM referral traffic over 14 months | Referral revenue and traffic from ChatGPT, Perplexity, and Claude | Optimist-published February 20, 2026 case study about an unnamed large B2B technology company; no independent audit or client identity is presented |
| Blend | AI presence rose from 17.1% to 34.7% across 500+ commercial prompts over 90 days; 133% growth in AI-sourced MQLs in Q4 2025; more than $500,000 in AI-sourced pipeline and nearly 3.5× growth from H1 to H2 2025 | Prompt visibility, marketing-qualified leads, and pipeline—not booked revenue | Blend-published self-case-study about Blend’s own performance; publication year for the 90-day presence result is not stated on the page; no independent verification is presented |
| Atlantic Zero | 27% increase in AI-attributed demo requests within three months | Demo requests, not revenue | Atlantic Zero customer story about an unnamed B2B analytics SaaS client serving mid-market and enterprise teams; the page gives no publication year and no third-party validation |
These are agency-published claims, not comparable test results. The client identities are withheld in the Optimist and Atlantic Zero examples; Blend’s metrics concern Blend itself. The available figures do not establish that any agency’s result will transfer to a different company, market, or measurement setup.
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Optimist: referral revenue, with an explicit attribution warning
Optimist describes work for a large, unnamed B2B technology company and reports 4,900% growth in LLM referral revenue over 14 months, alongside 2,622% growth in LLM referral traffic over the same period. The named sources were ChatGPT, Perplexity, and Claude. Those are unusually direct revenue and referral measures, but they remain an anonymous, agency-published case study rather than independently audited or named-client proof.
The case study says the work included restructuring content for citability, refreshing the catalog, surfacing subject-matter experts and bylines, and publishing proprietary first-party research. Optimist’s strategy director and principal, Tyler Hakes, adds an important qualification: “But we also want to be honest: AEO is an opaque system and directly attributing any specific action to any specific result isn’t something we — or anyone — can do with certainty.” That caveat belongs alongside the growth figures: the report connects the work and the outcome, but does not claim certainty about causation.
Blend: visibility, MQLs, and pipeline from a self-case-study
Blend presents its own performance as a test case. It reports that AI presence across more than 500 commercial prompts increased from 17.1% to 34.7% over 90 days. It also reports 133% growth in AI-sourced marketing-qualified leads in Q4 2025 and more than $500,000 in AI-sourced pipeline, with pipeline growing nearly 3.5 times from H1 to H2 2025.
These figures should not be collapsed into a single “revenue” result. Prompt presence is a visibility measure, MQLs are qualified leads, and pipeline is potential deal value; the cited report does not describe the pipeline as booked or closed-won revenue. Blend says its approach combined technical changes, content hierarchy, entity clarity, and intent-specific content. Because the example is Blend’s own case study, it is evidence of what Blend reports about its own performance, not a verified client outcome.
Atlantic Zero: demo requests from an unnamed client
Atlantic Zero’s customer story reports a 27% increase in AI-attributed demo requests within three months for an unnamed B2B analytics SaaS company serving mid-market and enterprise teams. A demo request is a meaningful conversion signal, but the page does not report revenue, pipeline, or a third-party validation of the result.
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The described work mapped questions to buyer stages, used direct answers and comparison sections, clarified product positioning, and added proof blocks. The client’s anonymous Head of Growth said: “We were ranking, but we were missing from the AI-generated shortlists. After the changes, we started showing up in comparisons and the summaries finally reflected how we actually position the product.” The account illustrates a practical aim of this work—making a product easier for answer engines to represent in a shortlist—without establishing that every company will see the same demo lift.
Which agencies can show revenue from AI search?
Optimist is the clearest example here of a published claim explicitly labeled LLM referral revenue: it reports a 4,900% increase over 14 months for an unnamed client. The figure is still an agency-reported result, and Optimist itself cautions that AI-search attribution cannot establish with certainty which action caused which result.
Blend reports more than $500,000 in AI-sourced pipeline, not closed revenue. Atlantic Zero reports AI-attributed demo requests, not revenue. Treating these as three versions of the same outcome would overstate what the evidence shows.
Before comparing a headline percentage, check what sits behind it:
- Visibility: mentions, citations, or presence in responses to a defined set of prompts.
- Traffic: visits referred from an answer engine or another AI-search surface.
- Lead outcomes: inquiries, demo requests, or MQLs, each with its own qualification rule.
- Pipeline: the value of open opportunities attributed to a source or influenced by it; this is not the same as booked revenue.
- Closed revenue: deals actually won, with an explicit rule for deciding how AI search receives credit.
A larger percentage does not automatically mean a stronger result. The starting baseline, date range, attribution rule, and evidence behind the number all affect what the metric can tell you. The examples above do not provide enough common measurement detail to rank the agencies by performance.
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How do I compare GEO and AEO agencies?
Generative engine optimization (GEO) and answer engine optimization (AEO) describe efforts to improve how a brand and its information appear in AI-generated answers. Labels vary between agencies; compare the work plan and its measurement rather than assuming the terms signal a standard service.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe examples describe a mix of technical SEO, content structure, product positioning, expertise signals, and reporting—not a single isolated tactic. Optimist describes content restructuring, catalog refreshes, expert bylines, and original research. Blend describes technical work, hierarchy, entity clarity, and intent-specific content. Atlantic Zero describes buyer-question mapping, direct answers, comparisons, positioning, and proof blocks.
Use these criteria to assess an agency’s proposal:
- Buyer-question coverage: Does the team map questions to buyer stages and show which pages or assets address them?
- Positioning and evidence: Does the plan make the product’s audience, category, differences, and supporting proof clear and consistent?
- Useful content structure: Does it explain how the team will make answers direct, comparisons fair, and claims easy to substantiate?
- Technical and editorial scope: Does the work cover the technical foundations and content changes relevant to your site, or only promise more AI mentions?
- Iteration: How will the team respond when answer-engine outputs change or monitoring shows that a product is missing from relevant answers?
- Reporting: Does the agency separate visibility, referral sessions, leads, pipeline, and closed-won revenue instead of presenting them as interchangeable?
These questions are useful whether a provider calls the work AEO, GEO, or AI-search optimization. The label matters less than a clear link between the proposed work, the observable outcome, and the reporting rule.
Are AI visibility and citations the same as revenue?
No. Visibility and citations describe whether an answer engine includes or references a brand; they do not, on their own, show that a person visited, became a qualified lead, or bought anything. Each step requires its own measurement.
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Blend’s reported change in presence across 500+ commercial prompts is a visibility result. Optimist’s LLM referral traffic is a traffic result, while its referral-revenue figure is a later-stage outcome. Atlantic Zero’s demo-request increase is a conversion result, but still not revenue. The measures answer different questions: whether the brand appears, whether people arrive, whether they take an action, and whether that action becomes business.
Attribution is especially difficult when a buyer may encounter a brand in an AI answer and later return through another channel. Optimist explicitly warns against claiming certainty about the relationship between a specific AEO action and a specific result. Ask an agency to explain how it handles that uncertainty rather than treating “AI-attributed” as a universal accounting standard.
What should a B2B company ask an AEO agency before hiring?
Ask for the measurement definition before accepting a case-study headline. A credible proposal should let your team understand what is counted, when it is counted, how attribution works, and what evidence supports the report.
- “What do you mean by AI visibility, citation, and AI-sourced?” Ask for the exact definitions used in reports, including what qualifies as a mention or citation and how a lead is assigned to AI search.
- “What was the baseline and date range for this result?” Get the starting value, end value, measurement window, and prompt or channel scope—not just a percentage increase.
- “Is this sourced pipeline, influenced pipeline, or closed-won revenue?” Ask the agency to distinguish opportunities first sourced through AI search from deals where AI search may have played an assisting role.
- “What analytics or CRM evidence backs the claim?” Ask what records support the reported number and how the agency connects answer-engine referrals or exposure to website sessions, lead records, and sales outcomes.
- “Was this a client engagement or your own case study?” Blend’s figures are explicitly about its own performance. For client work, ask whether the client can be named or whether the result can be checked another way.
- “Can you show a sample report?” Inspect whether the report separates visibility, traffic, leads, pipeline, and revenue, and whether it displays date ranges and attribution rules.
- “How do you handle changing answer-engine outputs?” Ask how the team monitors changes, revises its analysis, and decides what to update when a product appears inconsistently in AI answers.
A useful answer is specific enough that your marketing, analytics, and sales teams can audit the definitions together. If the agency can offer only a large multiplier without a baseline, period, and attribution explanation, the number is not a sound basis for comparing vendors.
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The available excerpt describing Virayo characterizes it as a B2B SaaS-focused agency that combines SEO, GEO, and AEO reporting. The same excerpt says publicly available case studies did not show revenue attributed specifically to AI citations at the time that article was written.
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That is a disclosure about what the excerpt surfaced, not proof that no such evidence exists anywhere. It also does not establish a revenue result that can be compared with the Optimist, Blend, or Atlantic Zero figures. Ask Virayo directly for current examples and the measurement details behind them if it is on your shortlist.
Why isn’t this a ranked list of seven agencies?
A reliable seven-agency ranking requires a verifiable shortlist and comparable evidence. The material available for this article supports three agency examples with reported outcomes, plus a limited description of Virayo, but does not reliably establish the remaining names in the original seven-agency list. Filling those slots with guesses would create a false comparison.
Even with a complete roster, “best” would depend on the buyer’s needs: B2B experience, strategy, geography, size, reporting maturity, and the specific outcome being measured. The available case studies also differ in client disclosure, measurement stage, time window, and attribution. Compare evidence and fit directly rather than reading the figures as a league table.
Frequently Asked Questions
Can an agency guarantee that my company will appear in ChatGPT answers?
The reported cases describe work intended to improve AI-search visibility, not a guarantee of placement. Ask how the agency monitors outputs and what it will do when results vary; the evidence presented here does not establish a reliable placement guarantee.
Does a 4,900% increase mean the client earned 4,900% more total revenue?
No. Optimist labels the figure as growth in LLM referral revenue over a 14-month period, not growth in the client’s total revenue. The case study does not provide enough detail here to infer the change in total company revenue.
Should I hire an agency that reports more AI citations or one that reports more leads?
That depends on your immediate goal and whether each measure is defined consistently. Citations can indicate answer visibility, while qualified leads are closer to a commercial outcome; ask how the agency will connect its chosen measure to your sales process.
Can a B2B company measure AI search without relying on an agency’s case study?
Ask for a reporting setup that shows the underlying analytics or CRM evidence and lets your own marketing and sales teams review its attribution rules. A sample report can reveal whether the vendor separates observed exposure from visits, leads, pipeline, and closed deals.
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