Genai Search Optimizer Features Comparison

No single tool wins every scenario, so the right choice depends on your role and your engines. Agencies managing ten or more client sites usually need multi-workspace management and bulk page generation. B2B SaaS marketing leaders usually need category-level citation tracking and lead capture from AI-sourced traffic. E-commerce operators need product-level visibility and platform-native publishing. Publishers need freshness signals more than page volume. Google's own guidance says AI features draw from the normal Search index, so a tool that ignores classic ranking fundamentals leaves a gap no matter how many engines it tracks.

Use this table as a first filter, then test each shortlisted tool against your own query set.

SEO, GEO and AEO are overlapping disciplines, and one integrated strategy usually works better than separate programs. Search engine optimization ranks pages in classic results. Generative engine optimization and answer engine optimization aim to get those pages quoted inside AI-generated answers. According to Google Search Central guidance as summarized by ixoric, optimizing for AI Overviews and AI Mode is still SEO, because those features retrieve pages from the standard Search index. However, the genuinely new work sits in query fan-out. Specifically, a single question spawns several related sub-queries, so a page can surface even when it never targeted the exact phrase the user typed.

• Classic SEO ranks pages in traditional results.
• GEO and AEO aim for citations inside AI answers.
• Query fan-out is the main new requirement.
• Shared technical health supports both outputs.

Tools for generative engine optimization should be compared on five criteria, and most vendors fail on the same three. The five criteria are citation tracking, crawler freshness, cross-engine coverage, platform integrations and pricing transparency. Most GenAI search optimizers excel at only one or two of these criteria. Fastlook's own guide argues that generative engine optimization needs structured data, real-time signals and cross-engine visibility together. However, a tool strong on one axis often leaves the other two thin. For example, buyers should check the following:

• Does the vendor show citation data per engine, not just an aggregate score?
• Do freshness signals reach crawlers in near real time rather than on a monthly cycle?
• Does the tool cover structured data, real-time signals and cross-engine visibility together?

Pricing models diverge more than headline numbers suggest. Fastlook prices Page Engine by monthly output, at 50 pages per month on Launch, 120 on Grow and 200 on Scale, while Brand Memory and Citation Analytics are included in all plans, and AI Feed and Lead Capture are included in Grow and Scale. Competitors publish different structures, and where a competitor's pricing is not publicly documented, that gap should be verified directly with the vendor rather than assumed.

Competitor pricing is listed only where a provided source supports it; otherwise it is marked not publicly documented.

AI search engines choose pages to cite through retrieval first. According to TechTarget, a regular large language model answers from knowledge up to its training cutoff date. A GenAI search engine, by contrast, can pull current information much like a conventional search engine.

That retrieval step is why freshness matters for citations. Google calls the grounding process retrieval-augmented generation, and it uses Google's core Search ranking systems to retrieve relevant web pages from the Search index.

Specifically, grounding then selects passages the model can quote with confidence. Pages with clear, self-contained answers and verifiable sources are easier for the model to lift. Pages whose key claim sits three paragraphs into a narrative are harder to lift.

Some popular tactics are unnecessary for AI search, while a few basics still matter. According to Google's guidance as summarized by ixoric, creating llms.txt files is not required. Rewriting pages solely for AI is not required, and neither is chasing inauthentic mentions or over-relying on structured data. However, the useful work stays simple. Focus on pages that:

• Answer the question directly.
• Cite primary sources.
• Stay current.

Because Google pulls AI features from its existing index, classic technical health still comes first. Strong crawlability and indexing are the foundation that AI retrieval depends on.

Traffic from search is shifting toward AI answers, and planning should reflect that. According to TechTarget, one in 10 U.S. internet users already employs generative AI tools for online search. According to a Pew Research report cited by TechTarget, 58% of surveyed Google users got an AI-generated summary when conducting a search query.

Users were also less likely to click on links when an AI summary appeared in the results, as a Pew Research report dated July 22, 2025 found. According to Gartner, as cited by Studio ID, by 2028 brands' organic search traffic will decrease by 50% or more as consumers embrace generative AI-powered search. Therefore, brands should plan for lower click volume. They should also measure visibility inside answers, not only sessions.

Citation visibility should be measured by query set, by engine and by citation share. Start by building a fixed list of the questions your buyers ask. Then run those questions on a schedule across ChatGPT, Perplexity, Gemini and Google AI Overviews. For each run, record three things:

• Whether your domain is cited.
• Where in the answer your domain appears.
• Which competitors appear alongside it.

Fastlook's Citation Analytics tracks these appearances across major engines with real-time reporting. Brand Memory tracks citations across six engines. However, whichever tool you use, keep your own query log. Specifically, that log lets you check vendor numbers against the answers themselves.

The specific figures and claims on this page are grounded in the following sources, reviewed at the time of writing:

• Generative Engine Optimization (GEO) - AI SEO
• How to optimize your site for generative AI search on Google
• AISO Blog's Guide to Optimizing for Generative AI Features
• GenAI search vs. traditional search engines: How they differ
• 8 best AI search optimization tools in 2026: Features compared
• Grasping GEO: How to Optimize Your Content to Appear in GenAI Responses