BUILDING AN AI-NATIVE COMPETITIVE INTELLIGENCE STACK WITH QUERY FAN-OUT AND AD SPY TOOLS

A buyer searching for a new software platform might ask an AI assistant for a recommendation, compare several options in the same conversation, and receive a shortlist before visiting a vendor's website. That single interaction can cover questions that once required multiple searches, comparison articles, and product pages.

For marketers, this creates a new competitive challenge: understanding not only which pages rank in traditional search, but also which brands appear in AI-generated answers, how those brands are described, and what their advertising communicates after a prospect encounters them.

This is where AI competitive intelligence becomes more than a search visibility exercise. By combining query fan-out research with Anstrex.com/blog/ai-cant-spy-on-your-competitors-ad-creatives-but-you-can-use-it-to-act-on-what-you-find" target="_blank" rel="noreferrer noopener">ad spy tools, marketers can connect the questions buyers ask to the competitors that appear in AI answers and the campaigns those competitors use to attract attention.

Query fan-out describes the process through which an AI search experience may expand an initial question into multiple related searches or information needs. The exact process varies by system, and the underlying searches are not always visible to users. Nevertheless, marketers can build a practical approximation by identifying the related questions that a buyer is likely to explore.

Ad spy tools then provide a different perspective. They help researchers investigate publicly observable advertisements, creative variations, landing pages, and offers associated with relevant competitors.

Together, these sources can reveal where a brand is missing from the conversation, which claims competitors emphasize, and what opportunities exist for more useful content or differentiated campaigns.

The objective is not to copy the competitors that appear most often. It is to build a connected research workflow that turns buyer questions into actionable market intelligence.

Traditional search remains important, but it is no longer the only way buyers research products and services. AI assistants and answer engines can summarize information, compare alternatives, and suggest vendors within a single conversation.

That changes how marketers should think about discovery. A buyer may encounter your brand through a cited article or a recommendation in an AI answer before visiting your website. Another buyer may see your competitors mentioned repeatedly while your brand is absent.

Neither outcome is fully captured by conventional website traffic reports.

Look beyond rankings and pageviews

Traditional SEO reporting helps marketers understand rankings, impressions, clicks, and organic visits. These metrics remain useful, but they do not fully explain how a brand appears in AI-generated answers.

A page can perform well for one keyword while providing limited coverage of the broader questions a buyer needs answered. Another page may not rank first for the initial query but may offer a useful comparison, technical explanation, or supporting detail that an answer engine references.

This is one reason marketers are exploring new ways to measure AI-era discovery. MarTech's discussion of measuring marketing when AI influences discovery provides context for evaluating brand awareness and engagement beyond conventional referral traffic.

A more complete measurement approach considers several signals:

• AI visibility: Whether your brand appears in relevant AI answers.
• Citation presence: Whether an answer links to or references your content.
• Positioning: How the system describes your product and differentiators.
• Competitive context: Which alternatives appear alongside your brand.
• Downstream behavior: Whether changes in visibility coincide with branded searches, direct visits, qualified leads, or other business outcomes.

These signals should be tracked separately. A brand mention does not automatically produce a visit, and a citation does not prove that an AI answer influenced a later purchase.

Understand query fan-out

Imagine that a prospect asks, “What is the best revenue intelligence platform for a 50-person B2B SaaS company?”

To answer that question well, a search system may need information about integrations, pricing, implementation, forecasting, team size, and alternatives. Depending on the system, it may issue related searches or retrieve information from sources that address those topics.

Marketers can approximate this broader research pattern by breaking the original question into related questions such as:

• What does revenue intelligence software do?
• How does revenue intelligence differ from call analytics?
• Which platforms integrate with Salesforce?
• What should a mid-sized SaaS team compare before buying?
• How do revenue intelligence tools affect forecast accuracy?
• What implementation and pricing considerations matter?

These questions form a useful research map even when the system's exact internal searches are not exposed.

Contently's article on Google AI Overview citations provides additional context for understanding how AI search visibility differs from conventional ranking performance.

Measure the questions buyers ask, not just the keywords they type

The practical implication is that your competitive research should start with a set of meaningful buyer questions rather than a single keyword.

For each question, record whether your brand appears, how competitors are described, and which sources support the answer. Repeat the exercise periodically because AI responses can change with the wording of the prompt, the system, and the information available to it.

Treat visibility as one part of the buyer journey

AI search visibility is a useful diagnostic, not a replacement for every other marketing metric. If your brand appears more often in relevant answers, investigate whether that visibility is accompanied by stronger branded search activity, more qualified traffic, or improved conversion outcomes.

At the same time, do not assume that every increase in direct traffic was caused by AI discovery. Use several sources of evidence, annotate significant changes, and compare patterns over time.

The goal is to understand where AI-mediated discovery may be influencing the journey and how your content can become a more useful, credible source for the questions buyers are trying to resolve.

Query fan-out is a useful framework for understanding why one broad buyer question can lead to a much wider research process.

A single prompt may combine several needs: understanding a category, comparing solutions, assessing risks, and choosing a product. An answer engine may retrieve or search for information related to those needs before generating a response.

The exact implementation differs across platforms. Marketers do not need access to every internal search to use the concept effectively.

Start with a high-intent buying question

Choose a question that reflects a genuine purchase decision in your market. Avoid starting with a broad, low-intent phrase if your goal is to understand competitive positioning near conversion.

For example:

“Which revenue intelligence platform is best for a mid-market SaaS company using Salesforce?”

This question suggests several underlying information needs:

Awareness questions

• What is revenue intelligence?
• Which business problems does it solve?
• How does it differ from related software categories?

Evaluation questions

• Which products integrate with Salesforce?
• What features matter for a mid-market sales team?
• How do implementation requirements differ?
• What evidence supports each vendor's claims?

Decision questions

• What does each option cost?
• How long does implementation take?
• What support and onboarding are included?
• What are the main limitations or trade-offs?

These categories are a practical planning framework. They should not be treated as a guaranteed list of searches executed by a specific AI engine.

Build a repeatable question map

For each starting prompt, create a list of related questions. Include equivalent wording, narrower requirements, comparison queries, and objections a buyer may raise.

A simple spreadsheet can capture:

This turns query fan-out from a technical concept into a usable research process.

Understand why topic coverage matters

A buyer evaluating a product needs more than a headline or a general overview. They may need technical specifications, use cases, limitations, pricing context, comparisons, and evidence that supports a claim.

A brand that addresses these related needs clearly gives customers more material to evaluate. It also creates more opportunities for its content to be useful when AI systems retrieve information for different aspects of a question.

Backlinko's guide to query fan-out explores this topic and its relationship to AI search visibility. Treat specific citation-rate claims from third-party studies as dependent on their methodology, sample, and date rather than as universal guarantees.

Use visibility tracking to find gaps

Tools for AI search analytics can help track selected prompts, record brand mentions, and compare how competitors appear across answers. HubSpot's overview of AI search analytics tools provides a starting point for understanding this category.

Choose a consistent set of prompts and record the date, platform, wording, response, citations, and competitors mentioned. Repeat the checks under comparable conditions and avoid drawing major conclusions from a single response.

The result is a structured view of the questions surrounding a buying decision. This becomes the foundation for competitive analysis, content planning, and the ad spy research that follows.

An AI-generated recommendation is more than a record of which brands appeared. It can also provide clues about how the system describes the category, which differentiators it associates with each vendor, and what information appears to support those descriptions.

These observations are valuable when treated as research signals rather than definitive judgments about market share or product quality.

Analyze who appears and how they are positioned

For every relevant answer, record:

• Which vendors are named.
• Which vendors receive direct citations.
• What use cases each vendor is associated with.
• Which features, integrations, or benefits are emphasized.
• What limitations or trade-offs are mentioned.
• Whether your brand is absent, mischaracterized, or described too broadly.

Pay attention to the language used to distinguish alternatives. One vendor might be described as suitable for smaller teams, another as a more customizable option, and a third as a solution for organizations with complex integrations.

These descriptions may reveal positioning themes worth investigating. They do not necessarily reflect verified customer sentiment or an objective assessment of product performance.

Identify the evidence behind repeated claims

When an AI answer repeatedly associates a competitor with a particular benefit, investigate the public information that could support that association.

Look for product documentation, detailed use cases, customer stories, comparison pages, reviews, and other relevant sources. Check whether the competitor has created content that addresses the question more directly than your own material.

A repeated claim can be a useful clue, but it is not proof that the claim is true. Verify important product or performance statements before using them in your own strategy.

Watch for positioning shifts

Repeat the same buyer prompts over time. If a competitor begins appearing in questions where it previously had little visibility, investigate whether its content, positioning, product offering, or public reputation has changed.

Likewise, if AI descriptions of your own brand become vague or inaccurate, inspect the information available to support a clearer explanation. The cause may be incomplete content, inconsistent terminology, weak third-party references, changes in the AI system, or other factors.

Do not assume that a change in the answer necessarily proves that a competitor has changed its marketing strategy. Treat it as a prompt for further research.

Translate answer patterns into competitive hypotheses

Suppose your research shows that a competitor repeatedly appears in answers about ease of implementation. You could investigate whether its website contains detailed onboarding guides, whether its ads emphasize fast setup, and whether its landing pages provide implementation evidence.

If the competitor's paid messaging reinforces the same theme, you have found a positioning pattern worth understanding.

Separate visibility from business performance

AI mention frequency does not directly establish market share, conversion probability, or revenue contribution. A brand might appear frequently in one prompt set while performing poorly in paid campaigns, and another might convert effectively despite receiving fewer mentions.

Keep AI visibility metrics separate from advertising performance. Use them to generate hypotheses about positioning and content opportunities, then validate those hypotheses with customer research, campaign analytics, and business results.

For a broader perspective on this approach, see MarTech's discussion of AI-powered competitive intelligence.

Once you have mapped the questions buyers ask and identified the competitors that appear in AI answers, the next step is to turn those findings into a focused research brief.

This prevents ad spy research from becoming an unstructured exercise in collecting interesting screenshots. Every search should help answer a specific competitive question.

Step 1: Select the seed prompt

Choose one high-intent question that connects directly to your product, audience, and business goals.

For example:

“Which revenue intelligence tools are suitable for a mid-market SaaS company with Salesforce?”

Record the exact wording, the AI system used, the date, and any available model or product-version details. Consistent records make repeated checks more useful.

Step 2: Extract related buyer questions

Read the answer and identify the related questions it raises. Some AI systems may expose citations or search activity, while others may not reveal their internal retrieval process.

You can still build a useful approximation by extracting implied follow-ups, requirements, comparisons, and objections from the response.

Group them into awareness, evaluation, and decision categories. Prioritize the questions most relevant to your target customer rather than attempting to cover every conceivable variation.

Step 3: Record the competitor shortlist

Create a table for every relevant brand mentioned in the answer.

These are illustrative rows, not actual vendor findings. Populate the table with observations from your own research and preserve the source context.

Step 4: Identify content and positioning gaps

For each related question, ask:

• Does our website answer it directly?
• Is our information accurate, specific, and current?
• Does our brand appear in relevant AI answers?
• Which competitors appear, and what evidence supports their positioning?
• Do we have a clear differentiator or useful proof point?
• Is there a missing comparison, explanation, or use-case page?

The purpose is to prioritize gaps where better information could help buyers make a more informed decision.

Step 5: Translate the findings into ad spy searches

Now specify what to investigate in the advertising landscape.

For each competitor, document:

• Search terms and positioning phrases to investigate.
• Relevant ad networks and placements.
• Creative formats and offer types to examine.
• Landing pages and follow-up steps to inspect.
• Hypotheses connecting AI answer patterns to paid messaging.

For example, if AI answers repeatedly associate a competitor with fast implementation, investigate whether its public ads and landing pages emphasize that benefit. Look for demonstrations, customer stories, onboarding offers, or other supporting elements.

Keep the brief small enough to execute

A useful initial brief might contain:

• Three to five relevant competitors.
• Five to ten high-value buyer questions.
• A shortlist of positioning themes to investigate.
• A set of ads and landing pages to review.
• A clear question that the research must answer.

Expand the brief only when the initial evidence reveals a meaningful gap. This keeps the work focused on decisions rather than collecting data for its own sake.

Once the research brief identifies relevant competitors and their likely positioning themes, use Anstrex to investigate their observable advertising activity and funnel approaches.

The purpose is to connect what buyers ask and what AI answers emphasize with the messages advertisers use to attract clicks and guide visitors toward a conversion.

Anstrex can serve as the ad intelligence layer in this process. The available data and filters depend on the product, network, and current platform coverage, so verify which research capabilities are available in your account.

Prioritize relevant competitors and offers

Begin with the brands that recur in your high-value prompt set. Do not automatically assume that the most frequently mentioned vendor is the strongest competitor for every customer segment.

For each selected competitor, identify the relevant products, offers, and customer use cases. Then investigate the available ads and landing pages associated with those offers.

Where historical ad information is available, compare creative variations and periods of activity. A long-running ad may justify further examination, but it does not prove that the ad is profitable or receiving the largest budget.

Analyze the entire funnel, not just the creative

For each useful ad example, follow the public journey from the initial message through the landing page and subsequent steps you can observe.

Record three connected layers.

1. Hook and positioning

What promise earns attention? Does the ad focus on implementation speed, cost savings, ease of use, a specific customer problem, or another differentiator?

Compare this with the positioning language found in AI answers. If the same theme appears in both places, investigate how the competitor supports it and whether the message is relevant to your audience.

2. Proof and credibility

Look for evidence used to support the promise, such as product demonstrations, customer stories, technical explanations, named integrations, or published results.

Distinguish between a claim and the evidence behind it. A percentage or performance statement in an advertisement should not be treated as independently verified simply because it appears in a competitor's creative.

3. Path after the click

Examine the landing page, offer, and next action. Does the page explain the promise clearly? Does it provide enough information for the buyer's stage? Does the visitor encounter a demo request, trial, downloadable guide, pricing page, or another conversion step?

Document the journey as far as publicly accessible information allows. Some retargeting activity and post-click steps may not be visible to your research tools, so avoid assuming that the observed journey represents the complete funnel.

Compare paid messaging with AI positioning

Consider a hypothetical case in which a software brand is frequently described in AI answers as suitable for smaller sales teams.

Your Anstrex research might reveal that its ads emphasize simple onboarding, approachable pricing, or fewer technical requirements. Its landing page might then provide an implementation guide or a clear explanation of how the product fits smaller teams.

That combination would suggest a positioning pattern worth examining. It would not prove that the ads caused the brand's AI visibility or that the funnel outperforms competitors.

The next step is to ask whether your own product can make a stronger, more specific, and supportable case for the same audience—or whether a different customer segment represents a better opportunity.

Find gaps without copying competitors

Competitor research is most valuable when it reveals what the market is communicating and where your brand can offer something distinct.

If competitors repeatedly emphasize a particular feature but provide little supporting explanation, you could create a more useful guide, comparison page, or demonstration. If the market is crowded with generic trial offers, a more relevant onboarding resource may help your audience understand the product before committing.

The right response depends on customer needs, your product's actual strengths, and the evidence you can provide. Do not copy another advertiser's wording, creative execution, branding, or distinctive assets.

By the end of this stage, aim to have a short collection of documented competitor funnels, recurring messages, supporting evidence, and potential differentiation opportunities.

Query fan-out research and ad spy intelligence provide different kinds of information. One helps map the questions and comparisons buyers may explore; the other helps reveal how advertisers present offers and guide visitors through public-facing funnels.

The opportunity lies in combining the two without treating either as a guarantee of success.

Translate buyer questions into offer ideas

For each important question cluster, identify:

• The problem or concern behind the question.
• The evidence a buyer needs to evaluate a solution.
• The features or requirements that shape the decision.
• The objections that could prevent a purchase.
• The information your brand can provide credibly.

Suppose buyers repeatedly ask how to demonstrate the return on investment of a software purchase. Competitor research may reveal that existing offers focus on generic product trials, while their landing pages provide little guidance on reporting outcomes.

One possible experiment could be an executive reporting template, an implementation workshop, or a guided evaluation resource bundled with a trial—provided those deliverables are feasible and genuinely useful to your customers.

This is a hypothesis to test, not a proven winning offer.

Match creative angles to the question being answered

Use the research to develop distinct creative concepts for different customer needs.

For example:

• Problem-focused angle: Explain a specific operational challenge.
• Comparison angle: Clarify meaningful differences between approaches.
• Demonstration angle: Show how a product feature works.
• Proof-focused angle: Present substantiated results or customer evidence.
• Risk-reduction angle: Explain implementation requirements, support, or limitations.

Select the angles that align with your product and audience. Keep claims accurate and ensure that the landing page continues the message introduced by the ad.

Choose channels based on audience and evidence

The combined research can help prioritize which channels to investigate, but it does not automatically identify the best channel for every campaign.

Use customer behavior, your own campaign results, and the competitive landscape to decide where to test. For B2B audiences, that might include professional networks, search advertising, newsletters, or industry content. For other markets, visual discovery channels or native advertising may be more relevant.

AI-mediated discovery also makes it useful to develop content that answers real evaluation questions. Detailed comparison pages, product walkthroughs, transparent pricing explanations, and credible case studies can help buyers who encounter your brand before clicking an advertisement.

For additional context on the changing relationship between discovery and advertising, see MarTech's discussion of AI-native advertising.

Measure both discovery and conversion outcomes

A combined intelligence workflow should connect research to measurable outcomes. Track relevant AI visibility observations alongside branded search activity, qualified visits, lead quality, and campaign performance.

Use consistent measurement periods and annotate major changes to content, advertising, and product positioning. Where possible, test new offers or creative angles against an appropriate baseline rather than attributing every change to AI visibility.

A rise in direct traffic or branded search may be consistent with growing awareness, but it does not independently establish that AI answers caused the increase. Treat these metrics as supporting evidence and investigate other possible explanations.

Keep human judgment at the center

AI tools can organize questions, summarize observations, and suggest creative variations. Ad spy tools can help researchers investigate public advertising activity and funnels. Neither can decide on its own which opportunities are best for your margin structure, positioning, resources, or long-term strategy.

Review the evidence, choose a manageable number of experiments, and document the results. Then update the question map and competitive research brief with what you learn.

Over time, this creates a repeatable process:

1. Map important buyer questions.
2. Monitor AI answers and competitor positioning.
3. Investigate relevant paid ads and funnels in Anstrex.
4. Identify content gaps and differentiated offer ideas.
5. Test original campaigns and measure business outcomes.
6. Feed validated insights into the next research cycle.

AI search is changing how buyers gather information and compare alternatives. Query fan-out provides a useful way to map the related questions that can shape those decisions, even when an AI system does not expose its internal search process.

Ad spy tools add a complementary view of the advertising landscape. They help marketers examine observable creative, offers, landing pages, and funnel structures associated with relevant competitors.

When these sources are combined, competitive intelligence becomes more actionable. Marketers can move from simply tracking who appears in AI answers to investigating why certain messages recur, which customer needs remain poorly addressed, and where a more useful offer or clearer explanation could create an advantage.

The key is to separate observations from assumptions and assumptions from validated results. AI mentions do not guarantee pipeline, persistent ads do not prove profitability, and competitor tactics do not guarantee success when copied.

Build your intelligence stack around a clear cycle: map the questions, investigate the competition, create a differentiated response, and measure what happens. That is how AI discovery research and ad spy intelligence can support a more informed, connected, and practical marketing strategy.