A review of Catalyst AEO centers on a question that is increasingly relevant to B2B marketers: when a buyer opens ChatGPT and asks which vendors in a category are worth considering, does the company appear in the answer?

G2’s Answer Economy report, published in April 2026, found that 51% of B2B software buyers now begin their purchasing process inside an AI chatbot rather than a traditional search engine, up from 29% 12 months earlier (Source: G2). The same survey of 1,076 decision-makers found that 69% ultimately selected a different vendor from the one they initially had in mind because the chatbot redirected them.

Catalyst built its answer engine optimization (AEO) and generative engine optimization (GEO) practice around this shift. The program is offered to B2B companies whose organic traffic is flattening as potential buyers increasingly use ChatGPT, Claude, Perplexity, and Gemini for research. Its five principal features are a free citation audit, an entity-authority strategy, live reporting dashboards, a citation-share guarantee, and senior-operator execution.

Key points

• Catalyst AEO focuses on entity authority and citation share rather than conventional keyword rankings.
• The free Citation Audit reviews 25 queries across four AI engines and returns results within 48 hours.
• Live dashboards are updated weekly instead of being delivered as quarterly PDF reports.
• The program includes a written guarantee of at least 40% citation share after six months.
• Catalyst says Rise increased Gemini sessions by 860% and influenced more than $8 million in pipeline.

What Catalyst AEO does

AEO, or answer engine optimization, is the practice of getting a brand cited, quoted, and recommended in AI-generated answers before a buyer visits the company’s website. Catalyst presents AEO as distinct from traditional search optimization because the inputs and evaluation systems differ.

Traditional search engines such as Google rank pages using signals associated with domain authority. Generative engines select sources using entity authority: their understanding of what a company is, which category it belongs to, and whether it is a credible answer to a specific question.

That distinction helps explain why companies with strong search rankings may remain absent from AI-generated answers. The signals that helped them rank in traditional search may not be the signals that generative models use to select sources. For teams evaluating the channel, this makes AEO a measurement and content problem as well as a search problem: the relevant question is not only whether a page ranks, but whether the company is represented in the answers to the queries its buyers ask. Catalyst’s five-part approach is designed to address that gap.

1. A free 25-query citation audit across four engines

Catalyst’s entry point is a free citation audit that takes 48 hours. The company maps 25 buyer-intent queries across ChatGPT, Claude, Perplexity, and Gemini, then measures the client’s citation share against three named competitors for each query.

The audit includes:

• Twenty-five buyer-intent queries selected from category language rather than brand terms.
• Coverage across four major AI engines instead of a snapshot from a single model.
• A head-to-head citation-gap analysis against three competitors.
• An entity-authority assessment showing how the models currently describe the company.
• A prioritized roadmap identifying which queries to pursue first.

Catalyst says the audit is useful because most teams do not know how often they are cited in the answers that influence buyer shortlists. A company may know that traffic has declined and suspect AI search is a factor, but it may not know whether it holds 2% or 30% of citations for relevant queries. The audit is intended to turn that uncertainty into a measurable baseline before a company commits to the program.

2. Entity-authority strategy instead of keyword optimization

Catalyst describes entity-authority strategy as the feature that distinguishes its AEO service from a conventional SEO retainer with AI terminology added. Rather than pursuing keyword positions, the program focuses on structured signals intended to give models enough confidence to name a company in an answer.

The work includes:

• Structured content designed for extraction and citation rather than click-through rate.
• Schema markup and entity signals that clarify what the company is and what it does.
• Development of authoritative sources to provide third-party corroboration.
• Competitive citation displacement on queries where a rival currently controls the answer.
• AI-optimized definition and FAQ pages that models can quote clearly.

Catalyst cites Lumanu, a FinTech company in the creator-payments sector, as an example. The company reportedly had no AI-search presence when the work began. Within 60 days of the entity-authority build-out, Lumanu was receiving 5,130 referral visits from ChatGPT alone, a 1,746% increase. Catalyst says those visits converted at higher rates than any other organic channel.

The company describes this as a recurring pattern, arguing that AI referrals can arrive with an implicit endorsement because the recommendation comes from the model. Catalyst directs companies seeking an initial assessment to its free Citation Audit, which covers 25 queries, four engines, and three competitors, with results delivered in 48 hours.

3. Live dashboards instead of quarterly PDF reports

Catalyst says most agency reporting is retrospective. Its clients instead receive a live measurement layer from the start of the engagement, refreshed weekly and connected to CRM conversions and closed revenue.

The reporting covers:

• Generative Engines: Citation frequency by model, tracked week over week.
• Prompt Visibility: Average rank and visibility percentage for tracked buyer-intent queries.
• Citation Share: The client’s share of AI citations compared with named competitors, by domain.
• AI Sentiment: How models describe the brand, categorized as positive, neutral, or negative.
• Recent AI Chats: A live feed of buyer prompts, the responding model, geography, and competitors appearing alongside the client.
• AI Traffic and Conversions: Clicks and conversion events by AI source, connected to the CRM.

Catalyst says clients respond particularly strongly to the Recent AI Chats feed because it displays the prompts buyers are using and shows when a competitor appears in the same answer. Citation share is the central performance measure, and the company says clients see it on the same weekly cadence as Catalyst’s internal team.

4. A 40% citation-share performance guarantee

Catalyst AEO includes a written performance guarantee of at least 40% citation share on a client’s top 25 buyer-intent queries across ChatGPT, Claude, Perplexity, and Gemini within six months. If the target is not reached, Catalyst says it will continue working at no additional cost until the threshold is met.

The guarantee is defined by four conditions:

• The metric is citation share rather than impressions, sessions, or keyword counts.
• The query set is agreed upon in advance and cannot be changed later to move the target.
• Measurement covers ChatGPT, Claude, Perplexity, and Gemini rather than a single favorable engine.
• The remedy is continued work at no charge rather than a partial refund.

Catalyst says the threshold is based on citation share because a guarantee is only meaningful when the underlying metric is specific. It points to Rise as an example of the type of progression the program seeks to produce. Over 12 months, Rise increased Gemini sessions by 860%, recorded more than 8,278 total AI sessions across more than 100 countries, and influenced more than $8 million in pipeline. Catalyst says the company began with declining organic traffic and no AI presence.

Catalyst also offers a 30-minute discovery call to assess whether the program is appropriate for a prospective client.

5. Senior-operator execution rather than junior account managers

The fifth feature is a staffing model rather than a software capability. Catalyst’s AEO and GEO practice is led by Austin Heaton, who has 12 years of experience in search and has been featured in Fast Company, SimilarWeb, and Zapier.

Catalyst says the work is carried out by a senior operator embedded with the client’s team rather than passed down to an account coordinator. The operating model includes:

• One senior practitioner responsible for strategy and execution rather than a layered account team.
• A four-week onboarding period, with results expected within 90 days.
• No long-term contract requirement.
• Reported aggregate client results of 560% average AI click growth in 45 days and a 5-to-7-times return on investment for the program.

Catalyst’s current AEO roster includes Rise, DFNS, Lumanu, Pactvera, and AirOps. The company says clients most often cite senior execution as the program’s structural difference. Because AEO is still a relatively young discipline, Catalyst argues that judgment is more important than process documentation and that judgment is difficult to delegate.

Who Catalyst AEO is designed for

Catalyst says its service is intended for B2B SaaS and services companies from Series B through Series D, typically with more than $10 million in annual recurring revenue. The target customers already have buyers researching their category in AI tools and measure marketing performance through pipeline rather than traffic.

Potential indicators of a good fit include:

• The company has invested in SEO but still does not appear in AI answers.
• Competitors are cited in AI answers while the company is not.

Catalyst identifies the following as poor-fit conditions:

• The company is pre-product or has no defined buyer-intent query set.
• The company needs pipeline within 30 days, because entity authority compounds over several months.
• The company operates in a market that has not yet adopted AI for category research.

Catalyst says that if two or more poor-fit conditions apply, the audit will identify the mismatch before the company commits to the service.

The bottom line on Catalyst AEO

Catalyst’s central premise is that a buyer’s first impression of a brand may no longer come from its homepage. Instead, it may be a sentence generated by an AI model about the company’s category, assembled before the buyer visits the website or completes a form.

G2 found that one-third of buyers ultimately purchased from a vendor they had not heard of before entering their prompt. Catalyst presents that finding as both an opportunity and a risk for companies that do not appear in AI-generated recommendations.

The five features are designed to address different parts of the same problem. The audit establishes the company’s position. Entity-authority work is intended to improve that position. The dashboards track changes from week to week. The guarantee ties continued compensation to a defined citation-share result. Senior execution puts the work in the hands of a practitioner with relevant experience.

Companies can begin with Catalyst’s free AI Citation Audit to assess their position across 25 buyer-intent queries and four engines, or schedule a discovery call to discuss whether the program is suitable. Catalyst says the call is intended to provide a direct assessment of fit rather than a sales pitch.

Frequently asked questions

What is Catalyst AEO, and how is it different from SEO?

Catalyst AEO is answer engine optimization, the practice of getting a company cited and recommended inside AI-generated answers. It differs from SEO primarily in what it optimizes. Traditional SEO builds domain authority to improve rankings on a results page, while Catalyst focuses on entity authority, or the signals models use to determine which sources should appear in a generated answer. The two systems are related but not interchangeable, which Catalyst says explains why strong Google rankings may not produce AI citations.

How long does Catalyst AEO take to produce results?

Catalyst says the program typically produces measurable results within 90 days after a four-week onboarding period. Entity authority generally compounds rather than spikes, so early movement may appear as increased citation share for mid-tail queries before it becomes visible in traffic. Across its clients, Catalyst reports average AI click growth of 560% within 45 days, while pipeline effects may take longer as citations accumulate for high-intent queries.

What does Catalyst AEO measure each month?

The primary metric is citation share by query and engine. Catalyst also tracks competitor movement, AI traffic and conversions, new query opportunities, and sentiment in AI answers. The company reports these measures through live dashboards updated weekly rather than a quarterly presentation and connects AI-sourced conversions to the client’s CRM so the program can be evaluated by pipeline contribution instead of impressions.

Does Catalyst AEO require a long-term contract?

Catalyst says it does not require a long-term contract. The program includes four-week onboarding, targets results within 90 days, and carries a written guarantee of at least 40% citation share on the client’s top 25 buyer-intent queries after six months. If the threshold is missed, Catalyst says it will continue the work at no additional cost until the target is reached.

Who should not buy Catalyst AEO?

Catalyst identifies pre-product companies, businesses with less than roughly $5 million in ARR, and teams that need pipeline within 30 days as poor fits. It also turns away companies without a clear category or defined buyer-intent queries, as well as companies in markets that have not yet adopted AI for vendor research. The free Citation Audit is intended to identify those mismatches before either side commits.

Source: https://fintechzoom.io/catalyst-aeo-review-5-best-features/
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