We examine the structural foundation of your site and tell you exactly what AI systems can — and cannot — read, infer, and cite about your business.
This is not a keyword report or a prompt strategy. It is a technical and structural audit of the layer that exists before AI citation begins. Most sites fail here silently. We tell you specifically where and why.
Get Your Free AuditOne hour of dedicated review per submission. Delivered within two business days. No sales call required. Report sent to your email.
Every audit examines four areas. These are the structural signals AI systems use to decide whether your content is readable, trustworthy, and citable.
We examine how your pages are built at the HTML level. This includes whether content exists in crawlable source or is injected by JavaScript, how your heading hierarchy is organized, whether your primary value proposition is readable before any script runs, and whether your interactive elements (forms, quizzes, results) have their content present in the DOM.
We examine what AI systems can infer about your organization from your site's machine-readable signals. This includes your schema markup type, whether your schema graph is linked, what entity information is present (name, address, service area, language), and whether those signals are consistent across pages.
Based on what we find, we identify the specific structural changes most likely to improve AI citability. These are prioritized by impact — the changes that close the largest gaps first.
We identify patterns that actively reduce citation confidence: static publication dates, JavaScript-dependent content, unlinked schema entities, missing organization data, and architecture choices that signal low crawlability to AI systems.
Not included in this audit: Keyword research, prompt strategy, content recommendations, or paid search analysis. This audit covers the foundation. Everything else sits on top of it.
Fill out the form below. We ask for your URL, a brief description of what your business does and who it serves, your primary CTA, and what you already know about your current AI visibility. Text fields only — no multiple choice, no scoring. We read what you write.
A member of the Minnesota.AI team spends one hour reviewing your site against our structural framework. We look at page source, schema markup, entity signals, JavaScript dependency, and content crawlability. This is a human-reviewed audit informed by our published research, not an automated scan.
Your audit arrives by email within two business days. It covers all four audit areas with specific findings for your site. No call required to receive it. Contact information is included if you want to discuss implementation — but the audit stands on its own.
The form below captures everything we need to complete your audit. Here is what we ask and why:
Your report will arrive within two business days. If you do not receive it, check your spam folder or contact [email protected].
We are building toward an agentic web. For AI-assisted commerce and decision-making to function at scale, the structural foundation across sites needs to reach a baseline of coherence. Most sites are not there yet. These audits accelerate that.
The more sites that understand and implement proper AI visibility structure, the faster the ecosystem matures. We benefit from that maturity. So does everyone building on top of it.
We publish our findings publicly. The research that informs every audit is available at minnesota.ai/research and at daveedvalencia.com/research. We do not gatekeep findings. We believe these frameworks will arrive regardless — we are accelerating the timeline.
If you find the report useful, the most helpful thing you can do is follow us on LinkedIn and subscribe to our newsletter. We can only keep this service free if it creates enough signal that the work matters.
We make money if you need implementation help. We are transparent about pricing. But we actively encourage teams to implement findings internally first. The biggest beneficiaries of this work are organizations that can execute without us.
Every conclusion in our audits is grounded in controlled experiments with live sites and real traffic data. Our published research includes a controlled experiment measuring ChatGPT conversion rates on a domain with zero backlinks, and field notes from a controlled experiment documenting AI crawler behavior, citation patterns, and which structural decisions affected AI visibility. The framework we use is documented and available. Nothing is proprietary.
The audit is complete on its own. You do not need to book a call to receive your findings. You do not need to become a client to understand what to do next. The report contains specific, actionable findings for your site. If you need help implementing, we are available. If you do not, the audit still has value.
Most AI visibility services focus on content and keywords. We focus on the structural layer that comes before content decisions matter: page architecture, schema graph integrity, JavaScript dependency, entity signal consistency, and crawlability. These are the signals AI systems use to decide whether your content is trustworthy enough to cite. Fixing them is a prerequisite for everything else.
The audit framework is based on controlled experiments measuring actual AI crawler behavior, citation frequency, and referral traffic across live sites.
Published findings include:
Why AI citation matters: live data showing ChatGPT outconverting Google on a domain with zero backlinks within 60 days. The case for why structural AI visibility is worth getting right.
Read the paper →Field notes from a controlled experiment documenting AI crawler behavior, citation patterns, and structural decisions on a live site, with specific findings on schema type, JavaScript dependency, and crawlability.
Read the field notes →Every finding we deliver is traceable to a documented experiment.
An AI Visibility Audit from Minnesota.AI examines the structural foundation of your website across four areas: page structure (how your HTML is built and what is readable before JavaScript executes), context structure (what AI systems can infer about your organization from machine-readable signals like schema markup), areas of opportunity (specific changes most likely to improve how AI systems read and cite your content), and areas of weakness or exposure (patterns that actively reduce citation confidence, such as JavaScript-dependent content or unlinked schema entities). The audit does not include keyword research, prompt strategy, or content analysis — it examines the layer that exists before those decisions matter.
An AI Visibility Audit examines signals that are largely invisible to traditional SEO tools. Where an SEO audit focuses on rankings, backlinks, and keyword optimization, an AI Visibility Audit examines whether AI systems can correctly read, interpret, and cite your content. The key differences are in schema graph integrity, JavaScript dependency, content crawlability, and entity signal consistency — technical factors that determine whether an AI answer engine will surface your organization when a user asks a relevant question. A site can rank well in Google and still be effectively invisible to AI citation systems.
Every audit is reviewed by a member of the Minnesota.AI team. We dedicate one hour per submission to ensure findings are specific to your site, not generic observations. The process is informed by a documented research framework and supported by tooling, but the findings are human-reviewed before delivery. We do not deliver automated scan outputs. The report reflects a specific assessment of your site against the structural criteria that our research has identified as predictive of AI citation behavior.
The intake form asks for your website URL, a description of what your business does and who it serves, your primary call to action, your geographic scope, and what you currently observe about your AI visibility. Text fields only. We read what you write and use it to contextualize the structural findings. No technical knowledge is required to submit the form.
Reports are delivered within two business days of form submission. If you do not receive your report within that window, check your spam folder or contact [email protected] directly.
No. The report is delivered by email. No call is required to receive it. Contact information is included in the report if you want to discuss implementation, but the audit is complete and actionable without a follow-up conversation.
The report includes specific findings for your site and prioritized recommendations. We encourage teams to implement the findings internally first. If there is a genuine gap in technical capacity, Minnesota.AI offers implementation services at transparent pricing. Most findings — schema updates, content structure changes, removing JavaScript dependencies from crawlable content — can be executed by any developer familiar with the site's codebase.
We are building toward an AI-native web where agentic systems can correctly understand and recommend businesses. For that to function, the structural foundation across sites needs to reach a minimum level of coherence. We benefit from that ecosystem maturing. Offering free audits accelerates the baseline. We make money if organizations need help implementing what they find. The audit itself costs us time, which we consider an investment in the infrastructure we are trying to help build.
Minnesota.AI offers paid implementation services for organizations that need help executing on audit findings. Services include schema rebuild, page architecture changes, applied AI systems, and ongoing LLM discoverability work. Pricing is transparent and listed at minnesota.ai/pricing. We do not use the free audit as a discovery call in disguise — the report stands on its own, and we actively encourage internal implementation before reaching out to us.
The audit framework is based on controlled experiments published at minnesota.ai/research and daveedvalencia.com/research. Key published findings include a controlled experiment measuring a 24.9% conversion rate from ChatGPT on a new domain within 60 days, and field notes from a controlled experiment documenting AI crawler behavior, citation patterns, and which structural decisions affected visibility on a live site. Every structural criterion in the audit is traceable to a documented experiment with real traffic and measurable outcomes.
Pre-launch audits are not generally supported through this form. Auditing a staging environment requires additional context and coordination that goes beyond the standard intake. If you are pre-launch and want to discuss a structural review before going live, contact us directly at [email protected] with a brief description of your situation and we will let you know if we can help.
Minnesota.AI publishes findings, builds frameworks, and delivers audits to help organizations become readable, trustworthy, and citable by AI systems. If you want to know where your site stands, the audit is free and the findings are yours to keep.
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