The GEO Measurement Problem: What AI Actually Says About Your Business

You spent months optimizing for AI visibility. You added schema markup. You restructured your content. You built authority signals.

Now what?

Most businesses have no idea if any of it worked. They optimized for generative engines but cannot measure if ChatGPT, Perplexity, or Gemini actually recommend them when customers ask for help.

This is the GEO measurement gap. Everyone talks about optimizing for AI search. Almost no one talks about measuring results.

Here is what you need to know and how to fix it.

What Is GEO and Why Measurement Matters

Generative Engine Optimization is how you show up in AI-driven answers. When someone asks ChatGPT “best moving company in Austin” or Perplexity “reliable local accountant,” your business either appears in that answer or it does not.

Traditional SEO gave you rankings. You knew if you were position 3 or position 15. You could track movement week to week.

GEO gives you uncertainty. AI tools are probabilistic. The same question asked twice can produce different answers. Your competitor might appear in one response and vanish in the next.

This creates a measurement problem that most small businesses and agencies cannot solve without expensive tools or manual testing across dozens of scenarios.

Surmado Signal tests how AI tools talk about your business across 7 platforms for $25. You get data instead of guesses. You find out what AI actually says when customers ask about businesses like yours.

We are not an AI listing service. We do not submit your site to ChatGPT, Gemini, Claude, or any other model. We do not sell placements or special partnerships. We test how these tools already talk about your business and give you a plan to improve.

Why Traditional SEO Metrics Do Not Work for AI Visibility

Search rankings are binary and trackable. You rank or you do not. You can see your position change over time.

AI recommendations are fluid and context-dependent. The same business might appear for “affordable plumber” but not “emergency plumber.” It might show up when someone asks about “best” but disappear when they ask about “nearest.”

Traditional SEO tools measure:

• Keyword rankings
• Backlink counts
• Domain authority scores
• Click-through rates

None of these tell you if ChatGPT recommends you when a customer needs help.

You need different data:

• Visibility across multiple AI platforms
• Persona-based testing with real customer questions
• Citation frequency when you do appear
• Competitor mentions in the same responses
• Quality of AI descriptions about your business

This is what AI visibility testing measures. It shows you what customers see when they ask AI for recommendations in your category.

The Seven Platforms That Matter for AI Visibility

Most businesses test ChatGPT and stop. That leaves blind spots.

Here are the platforms customers actually use:

ChatGPT – 180 million active users. Largest AI platform. Often gives detailed recommendations with descriptions.

Perplexity – Growing fast among research-heavy users. Cites sources directly. Good for technical and professional services.

Claude – Popular with developers and technical decision-makers. Tends toward thoughtful, detailed responses.

Gemini – Google’s AI. Integrated with Search and Maps. Strong for local businesses.

Meta AI’s Llama model – Powers AI features across Facebook, Instagram, and WhatsApp. Huge reach for consumer-facing businesses.

xAI (Grok) – Twitter integration. Matters if your customers are active on X.

DeepSeek – Emerging platform with technical users. Watch this space.

Testing just one platform gives you incomplete data. A local bakery might crush on Gemini but not exist on ChatGPT. A B2B consultancy might appear on Claude but miss Perplexity entirely.

You need visibility across platforms your customers actually use.

Platform-Specific Strategies: What Works Where

Each AI platform has different citation patterns and recommendation logic. What works on ChatGPT might fail on Perplexity.

ChatGPT: Depth Over Breadth

ChatGPT favors businesses with comprehensive, well-structured content. It pulls from detailed service descriptions, case studies, and expertise signals.

What works:

• Long-form content with clear section hierarchies
• Specific examples and case studies
• Author credentials and expertise markers
• Detailed service descriptions with outcomes

What fails:

• Thin content with generic descriptions
• Lists without context or examples
• No clear differentiation from competitors
• Missing expertise signals

Testing insight: ChatGPT often includes businesses in recommendations but buries them without strong differentiation. If you appear in position 8 of 10, you exist but lose to competitors with clearer value propositions.

Perplexity: Citations and Authority

Perplexity shows its sources directly. It favors businesses with strong external validation and citation-worthy content.

What works:

• Press mentions and media coverage
• Industry publication citations
• Third-party reviews with substance
• Clear, quotable expertise

What fails:

• Self-promotional content without backing
• No external validation or mentions
• Weak or missing review presence
• Unclear credibility markers

Testing insight: Perplexity displays source links alongside recommendations. Businesses with strong backlink profiles and media mentions get preferential treatment. You need external validation, not just good content.

Gemini: Local and Integrated

Gemini pulls heavily from Google’s ecosystem. Google Business Profile quality, Maps data, and Search Console signals all matter.

What works:

• Optimized Google Business Profile
• Strong local SEO signals
• Consistent NAP (Name, Address, Phone) across web
• Integration with Google services

What fails:

• Inconsistent business information
• Weak or missing Google presence
• No structured local data
• Poor mobile experience

Testing insight: Gemini often recommends businesses for local queries even when their general web presence is weak. Fix your Google ecosystem first if local visibility matters.

Claude: Technical and Detailed

Claude favors technical accuracy and detailed explanations. It works well for B2B, professional services, and technical products.

What works:

• Technical documentation
• Detailed process explanations
• Clear methodology descriptions
• Professional credentials

What fails:

• Vague marketing language
• Missing technical details
• Unclear processes or methods
• Generic service descriptions

Testing insight: Claude often provides longer, more thoughtful recommendations. It includes businesses that can explain their approach clearly, not just businesses with the best SEO.

Meta AI: Consumer and Visual

Meta AI serves consumer-facing recommendations through Facebook, Instagram, and WhatsApp. Visual content and social proof matter more here.

What works:

• Strong social media presence
• Visual content and examples
• User-generated content and reviews
• Clear consumer benefits

What fails:

• No social media presence
• Text-only content
• B2B-heavy messaging
• Missing consumer reviews

Testing insight: Meta AI recommendations skew toward businesses with active social profiles and visual proof of work. If you ignore Instagram and Facebook, you probably miss Meta AI visibility.

Cross-Platform Patterns

Some patterns work across all platforms:

Universal winners:

• Clear differentiation from competitors
• Specific examples over generic claims
• External validation and proof
• Structured, scannable content
• Consistent information across web

Universal losers:

• Generic marketing language
• No clear expertise signals
• Inconsistent business information
• Thin content without substance
• Missing mobile optimization

The businesses that dominate AI visibility do not just optimize for one platform. They build a foundation that works everywhere, then layer in platform-specific optimizations.

El Tianguis Case Study: From 0% to #1 in AI Visibility

El Tianguis is a gluten-free Mexican restaurant in San Diego. Family-owned. Known locally for authentic recipes made safe for celiac families.

They had a problem. Customers searching for “gluten-free Mexican food San Diego” on Google found them. Customers asking ChatGPT or Perplexity the same question got recommendations for competitors. El Tianguis had zero AI visibility.

The Baseline Problem

Their first Signal report showed brutal numbers:

Authority Score: 12/100 AI systems rarely mentioned them and provided weak, generic descriptions when they did.

Presence Rate: 0% Across 54 organic test questions designed around their target customer, AI platforms mentioned El Tianguis exactly zero times.

Category Share: 0% When AI recommended Mexican restaurants with gluten-free options, El Tianguis never appeared. Competitors owned the category.

Ghost Influence: 78% This was the painful part. AI systems frequently described gluten-free Mexican food preparation, safe kitchen practices, and authentic recipes. They just attributed these features to other restaurants or gave generic advice without naming El Tianguis.

The restaurant had the expertise. AI just did not connect it to their brand.

What Signal Revealed

The report identified specific gaps:

Content structure problem: Their website had one page titled “Gluten Free Menu.” It listed dishes. It did not explain their celiac-safe kitchen practices, preparation methods, or why families trust them.

Differentiation gap: AI could not distinguish El Tianguis from competitors who just offered “some gluten-free options.” The depth of their commitment was invisible.

Authority signals missing: No press coverage. No blog content about gluten-free cooking. No customer stories. No expertise markers AI could latch onto.

Platform variance: Gemini occasionally mentioned them because of their Google Business Profile. ChatGPT and Perplexity never did. Claude sometimes included generic “restaurants with gluten-free options” without specifics.

The Changes They Made

Based on Signal findings and a Solutions strategy report, El Tianguis rebuilt their content:

Added expertise content: Created detailed pages explaining their celiac-safe kitchen, ingredient sourcing, and preparation protocols. They documented what made them different.

Built authority markers: Got featured in local celiac support group newsletters. Published guest posts on gluten-free living blogs. Added customer testimonials with specific safety stories.

Structured information clearly: Reorganized their site with clear hierarchies. Each dish page explained ingredients, preparation, and safety measures. Added schema markup for restaurant details.

Created comparison content: Added a page comparing “gluten-free menu items” vs “celiac-safe kitchens.” This helped AI understand their differentiation.

Optimized for personas: Rewrote content to match how celiac families actually search. Less “gluten-free menu available” and more “safe Mexican food for celiac families.”

The Results After 6 Weeks

Second Signal report showed dramatic shifts:

Authority Score: 84/100 AI systems now described El Tianguis as a celiac-focused restaurant with dedicated safe practices.

Presence Rate: 67% Across the same 54 test questions, they appeared in 36 responses. Major platforms consistently mentioned them.

Category Share: 18% When AI recommended gluten-free Mexican restaurants in San Diego, El Tianguis appeared in nearly every response. They jumped to #1 or #2 position.

Ghost Influence: 22% AI still discussed safe kitchen practices generically sometimes. But now most expertise mentions connected directly to El Tianguis by name.

Business Impact

Traffic from AI referrals increased 340% over three months. They tracked this by asking new customers “how did you find us?” during reservations.

More importantly, the customers AI sent them were highly qualified. These were celiac families specifically looking for safe dining, not casual diners asking about gluten-free options.

Average ticket size from AI referrals was 40% higher than Google Search traffic. Higher intent, better fit, better economics.

What Made the Difference

Three specific changes drove results:

Expertise documentation: Showing their process and commitment rather than just listing menu items gave AI concrete differentiation to cite.

External validation: Press mentions and community features provided the authority signals AI systems trust.

Persona alignment: Writing for their actual customer rather than generic SEO keywords improved relevance across all platforms.

The measurement layer was critical. Without baseline data, they would not have known where they stood. Without retesting, they would not have known what worked.

How Persona-Based Testing Works (And Why Generic Prompts Fail)

Most businesses test AI visibility by asking generic questions. They type “best restaurants in San Diego” into ChatGPT and see what comes back.

This approach misses how real customers actually use AI.

The Generic Prompt Problem

Generic prompts produce generic results. When you ask “best X in Y location,” AI gives you the most mainstream, well-known options. Small businesses and specialists rarely appear.

Example generic prompt: “What are the best moving companies in Austin?”

What AI returns: National chains with huge marketing budgets and massive review volumes. Local companies with better service but less visibility get buried or omitted entirely.

This test tells you almost nothing about real customer behavior because real customers do not search this way.

How Real Customers Use AI

Real customers bring context and specificity. They describe their situation and ask for recommendations that match their needs.

Example real customer prompt: “I’m moving from a 2-bedroom apartment in South Austin to a house in Round Rock next month. I have some antique furniture that needs careful handling. Budget is around $2,000. What moving companies should I consider?”

What AI returns: Specialists in residential moves, companies with furniture handling expertise, businesses serving the Austin-Round Rock corridor. The results completely change.

This is what persona-based testing measures. It asks questions the way your actual customers do.

What Surmado Signal Actually Tests

Signal builds a customer persona based on your business and industry. It generates questions that match how real people ask for help.

For a moving company, test questions might include:

• Budget-specific queries (“affordable moving companies for students”)
• Service-specific needs (“movers who handle pianos”)
• Timeline scenarios (“last-minute moving help this weekend”)
• Distance variations (“local moves under 50 miles” vs “long distance to California”)
• Special circumstances (“senior-friendly moving services with packing help”)

Each question variation produces different results. Testing them systematically shows where you have visibility gaps.

The Nine-Question Framework

Signal runs nine organic questions designed around your customer persona:

Questions 1-3: Core service discovery How customers initially find businesses like yours. Broad but not generic.

Questions 4-6: Need-specific scenarios Customers with particular requirements or constraints. Budget, timeline, or special needs.

Questions 7-9: Comparison and validation Customers narrowing options and looking for differentiation.

Question 10: Direct competitive comparison We explicitly name competitors and ask for comparison. This is biased by design and excluded from organic metrics.

Total test volume: 56 responses (6 platforms × 9 organic questions + 2 platforms for Q10).

Why Multiple Platforms Matter

Different platforms serve different prompts. Testing ChatGPT alone misses how Perplexity handles research queries or how Gemini serves local recommendations.

Platform variance example: A B2B consultancy might show 80% presence on Claude (technical audience) but 20% on Meta AI (consumer audience). Without multi-platform testing, they might optimize for the wrong channels.

Signal calculates a Coefficient of Variation across platforms. High CV means inconsistent visibility. Low CV means reliable presence everywhere.

What Report Metrics Actually Mean

Authority Score (0-100): Weighted measure of how well AI positions you. Considers mention rate, description quality, ranking position, and competitive context.

Presence Rate: Percentage of organic questions where you appear. Only counts Q1-Q9 because Q10 is biased.

Category Share: Your mentions divided by total competitor mentions across all responses. Shows market dominance in AI recommendations.

Ghost Influence: How often AI discusses your key features but attributes them to competitors or gives generic advice without naming you.

These metrics are calculated from real AI responses, not estimated or modeled.

What Good AI Visibility Actually Looks Like

Most businesses do not know what success means in AI search. They optimized but cannot tell if they won.

Here is what good AI visibility looks like in practice:

High visibility: Your business appears in 60% or more of relevant queries across platforms. When customers ask for help in your category, AI tools consistently mention you.

Quality mentions: AI describes your business accurately. It includes your key differentiators. It does not confuse you with competitors.

Citation frequency: When you appear, you are not buried in a list of 15 options. You show up early and with context.

Competitor comparison: You know which competitors appear alongside you and how AI compares you to them.

Category coverage: You appear for multiple customer intents. Not just “best X” but also “affordable X,” “reliable X,” “emergency X,” and other variations customers actually search.

Bad visibility is not just low frequency. Bad visibility is appearing with wrong information, weak descriptions, or in contexts where competitors outshine you.

Surmado Signal shows you both frequency and quality. You see how often you appear, what AI says about you, and who else shows up in the same responses.

Industry-Specific Visibility Patterns

AI visibility strategies differ by business type. What works for local service businesses fails for B2B consultancies.

Local Service Businesses

What drives visibility:

• Google Business Profile optimization
• Local citation consistency
• Review volume and recency
• Service area clarity
• Specific service descriptions

Common gaps:

• Generic service pages without local context
• No clear differentiation from franchises
• Missing mobile experience
• Weak review management

Best platforms: Gemini dominates for local queries because of Google integration. ChatGPT second. Perplexity third for research-heavy local searches.

Testing insight: Local businesses often have strong Gemini presence but weak ChatGPT visibility. The fix is usually content depth, not just local SEO.

Professional Services (Legal, Accounting, Consulting)

What drives visibility:

• Expertise documentation and credentials
• Case studies and specific outcomes
• Published thought leadership
• Industry recognition and awards
• Clear methodology explanations

Common gaps:

• Vague capability statements
• No specific examples or results
• Generic “we help businesses” messaging
• Missing technical depth

Best platforms: Claude and ChatGPT for B2B decision-makers. Perplexity for research-heavy buyers. Gemini less critical unless local.

Testing insight: Professional services often appear in AI results but with weak, generic descriptions. Differentiation is the bigger problem than discovery.

SaaS and Tech Companies

What drives visibility:

• Technical documentation quality
• Integration and API details
• Use case examples
• Comparison to alternatives
• Pricing transparency

Common gaps:

• Marketing-heavy content without technical substance
• No clear use cases or examples
• Vague feature descriptions
• Missing integration documentation

Best platforms: Claude for technical audiences. ChatGPT for general business users. Perplexity for product research.

Testing insight: Tech companies often have strong technical documentation but weak marketing content. AI skips them for business use cases even when technical fit is good.

Agencies and Creative Services

What drives visibility:

• Portfolio and case study quality
• Process and methodology clarity
• Client testimonials with specifics
• Industry specialization signals
• Before/after examples

Common gaps:

• Generic “full-service agency” positioning
• Portfolio without context or results
• No clear specialization or expertise
• Missing process explanations

Best platforms: ChatGPT for creative and marketing services. Perplexity less common. Claude for technical agencies.

Testing insight: Agencies struggle with differentiation. AI sees hundreds of agencies offering similar services and defaults to the most specific or highest authority.

Cross-industry pattern: Specificity beats comprehensiveness. Businesses trying to be everything to everyone get generic AI visibility. Specialists with clear positioning dominate their niches.

How Agencies Use AI Visibility Testing for Client Retention

Agencies face a specific problem: clients ask if their content work improved AI visibility but agencies cannot show proof without manual testing.

Smart agencies use AI visibility testing as a deliverable:

Before optimization: Run a baseline test. Show clients what AI currently says (or does not say) about their business.

During work: Make changes based on visibility gaps. Fix content structure, improve differentiation, build authority signals.

After optimization: Retest to show improvement. Clients see before-and-after data with concrete visibility gains.

This turns subjective content work into measurable results. It also creates a natural retention loop. Clients want to track visibility quarterly to catch new competitors and content drift.

Agency Implementation Patterns

White-label reporting: Agencies can rebrand Signal reports with their own name. PDF cover pages and PPTX titles show the agency brand. The underlying intelligence comes from Surmado.

API integration: Trigger Signal tests programmatically from your systems. Results flow back as JSON when reports complete.

Webhook delivery: Signal is async by design. Request a report, get a webhook when it finishes. No polling required.

Workflow example:

Client signs SEO package
  → Agency triggers Signal baseline via API
  → Report completes in about 15 minutes
  → Webhook delivers JSON to agency CRM
  → Agency reviews findings, builds strategy
  → Content work begins
  → 6 weeks later, agency triggers retest
  → Before/after report shows visibility gains
  → Client sees proof of value
  → Retention increases

Technical Integration Details

Endpoint: POST /v1/reports/signal

Authentication: API key in header

Required fields:

• brand_name: Client business name
• brand_slug: URL-safe identifier
• industry: Business category
• location: Geographic focus
• business_scale: Size indicator
• tier: “essential” or “pro”

Response: 202 ACCEPTED with report_id

Status check: GET /v1/reports/{report_id}

Webhook payload: Complete intelligence package with metrics, competitor data, and AI quotes

JSON schema: Versioned and stable. No breaking changes without notice.

Full API documentation at help.surmado.com.

The Dark Funnel Problem and Why It Matters

Most AI-driven research is invisible to your analytics.

Someone asks ChatGPT for recommendations. They get a short list. They visit two sites from that list and convert on one.

Your analytics shows: direct traffic, no referrer. You have no idea they found you via AI. You cannot measure top-of-funnel AI visibility because it happens outside your tracking.

This is the Dark Funnel problem. Traditional attribution breaks when customers research via AI before ever hitting your website.

You can fix this with two data sources:

1. AI visibility testing – Shows whether you appear in research queries at all
2. Conversion tracking with source questions – Ask new leads how they found you

Most businesses ignore the first and rely only on the second. This gives you lagging indicators but no leading ones. You find out people used AI after they convert, but you do not know if you are losing 10x more potential customers who never saw you in AI results.

Testing AI visibility gives you leading indicators. You know if you are in the consideration set before customers visit your site.

How to Test AI Visibility Without Burning Time

Manual testing does not scale. You can ask ChatGPT a few questions and screenshot the results. This takes hours and gives you anecdotal data.

Here is what systematic testing requires:

1. Write prompts like real customers, not like SEO professionals
2. Test across multiple AI platforms
3. Run the same prompts multiple times to account for probabilistic variation
4. Track who else appears in responses
5. Document what AI says about your business vs competitors
6. Test different customer personas and intents
7. Repeat regularly to catch changes

Most businesses try this once, realize it takes 10+ hours, and give up.

Automated testing solves this. Surmado Signal runs persona-based tests across 7 platforms in about 15 minutes for $25. You get a report with visibility data, competitor analysis, and what AI actually says about your business.

You can test once to establish a baseline. Retest after you make changes to see if visibility improved. Retest quarterly to catch new competitors or shifts in how AI describes your market.

No dashboards. No subscriptions. You pay $25, get a PDF and JSON, and act on the data.

How to Build an AI Visibility Testing Cadence

One-time testing is useful. Regular testing is strategic.

Here is a cadence that works for most businesses:

Month 1: Baseline Run your first AI visibility test. Document what AI says, how often you appear, and who your competitors are in AI responses.

Month 2-3: Optimize Fix the issues your baseline test revealed. Improve content structure. Build authority signals. Clarify differentiation.

Month 4: Retest Run a second test to measure improvement. You should see higher visibility scores and better descriptions.

Quarterly: Maintenance Retest every 90 days to catch new competitors, content drift, or platform changes. AI models update frequently. What worked in January might not work in April.

After major changes: Spot checks Retest after launching new services, rewriting core pages, or getting major press coverage. See if changes improved visibility or created new gaps.

This cadence balances cost with insight. You spend $100 per year on testing ($25 per quarter) and always know your AI visibility status.

The Questions Your AI Visibility Report Should Answer

A good AI visibility test answers specific questions:

Visibility questions:

• How often does your business appear when customers ask for help?
• Which platforms mention you most often?
• Do you show up for high-intent queries or just generic ones?

Quality questions:

• What does AI actually say about your business?
• Are descriptions accurate and compelling?
• Does AI highlight your differentiators or generic features?

Competitive questions:

• Which competitors appear alongside you?
• How does AI compare you to them?
• Who dominates categories where you are weak?

Actionability questions:

• What specific changes improve visibility?
• Which platforms need the most work?
• What content gaps does AI expose?

Most tools give you dashboards with charts. Surmado Signal gives you answers with context. You see visibility scores but also the actual AI responses. You know not just that you have low visibility but exactly why and what to fix.

What to Do With Your AI Visibility Data

Testing without action is just expensive curiosity. Here is how to use your data:

If visibility is low (under 30%):

• Audit content structure and clarity
• Build authority signals via guest posts, press, and partnerships
• Fix technical issues AI tools struggle to parse
• Clarify your differentiation on core pages

If visibility is moderate (30-60%):

• Optimize for personas where you are weak
• Expand content to cover more customer intents
• Test different page structures to see what AI prefers
• Build more citations from industry sources

If visibility is high (over 60%):

• Monitor competitor changes that might displace you
• Expand into adjacent categories
• Test new platforms where you have not optimized yet
• Document what works so you can replicate it

Your report includes specific recommendations based on gaps. You get a prioritized list of what to fix first.

What Kills AI Visibility (Anti-Patterns to Avoid)

Optimizing for AI is not the same as gaming algorithms. Some tactics backfire.

Anti-pattern 1: Keyword stuffing AI models detect unnatural language. Cramming keywords into your content reduces trust signals instead of improving them.

Anti-pattern 2: Thin content One-paragraph service pages do not give AI enough context. It skips you because there is nothing substantive to recommend.

Anti-pattern 3: Inconsistent information If your website says one thing, your Google Business Profile says another, and review sites have different details, AI gets confused and avoids you.

Anti-pattern 4: No clear differentiation If your site reads like every competitor, AI has no reason to prefer you. Generic descriptions get generic results.

Anti-pattern 5: Poor technical structure Broken schema markup, slow pages, and unclear hierarchies make it harder for AI to parse your content correctly.

Anti-pattern 6: Ignoring citations AI models prefer content with external validation. If no one links to you or mentions you, you lack authority signals.

These mistakes do not just reduce visibility. They actively harm it. Testing shows you which anti-patterns you are hitting so you can fix them.

Why Small Businesses Need AI Visibility More Than Enterprises

Big companies have brand recognition. When someone asks AI for “accounting software,” QuickBooks and Xero appear by default. They do not need to optimize as hard.

Small businesses compete on merit, not brand size. A three-person agency can provide better service than a national chain. But if AI never mentions them, customers never find out.

AI levels the playing field in one way and tilts it in another:

The advantage: AI recommends based on relevance and quality signals, not just brand size. A local plumber with great reviews and clear content can beat a franchise.

The disadvantage: AI never mentions you if your online presence is weak, unclear, or unstructured. You do not just lose visibility. You become invisible.

This makes AI visibility testing more critical for small businesses than large ones. You need to know if you are even in the conversation.

The $100 Bundle: Test Everything at Once

Most businesses need more than just AI visibility data. They need SEO audits and strategic guidance too.

The $100 bundle includes:

• Surmado Scan – SEO audit ($25)
• Surmado Signal – AI visibility testing ($25)
• Surmado Solutions – Multi-AI strategic advisory ($50)
• Plus 2 bonus credits (worth $50) for future reports

You get a complete picture: technical SEO issues, AI visibility gaps, and strategic recommendations from multiple AI advisors debating your best moves.

All three reports run in about 15 minutes each. You get PDFs, JSON, and webhook delivery. No dashboards. No subscriptions. Just answers.

Get started with the bundle.

Next Steps: Test Your AI Visibility This Week

You do not need months of planning to start. You need 15 minutes and $25.

Here is what to do:

Option 1: Single test Run Surmado Signal for $25. See what AI says about your business across 7 platforms. Get a baseline.

Option 2: Complete audit Run the $100 bundle. Get AI visibility, SEO audit, and strategic recommendations. See the full picture.

Option 3: Agency workflow Integrate Surmado into your client reporting via API. Automate AI visibility testing and focus on strategy.

No sales calls. No demos. No subscriptions. Pay for a report, get results in about 15 minutes, act on the data.

Test your site now.