If you work in marketing and are evaluating search agencies, you have likely noticed a recurring pattern in recent pitch meetings. Almost every agency deck now features slides dedicated to AI search, generative engine optimization, AI workflows, and more AI-focused marketing topics. Yet beneath these new slides, the actual deliverables often look identical to the ones pitched a decade ago: basic keyword research, low-effort link-building campaigns, and monthly ranking reports for traditional search terms.
The organic search landscape is no longer confined to Google's traditional ten blue links. B2B buyers increasingly prompt tools like ChatGPT, Perplexity, and Google AI Overviews to evaluate vendors, compare technical software specifications, and build purchasing shortlists.
Separating legacy search tactics from a measurable AI search strategy requires a practical vetting framework. As such, we’ve put together this a quick audit checklist to check against an agency's expertise while you evaluate their services:
1) How do you optimize for LLM answer engines?
2) What is your schema strategy for entity recognition?
3) How do you measure share of model visibility?
4) How do you build citable digital PR?
5) How do you audit technical LLM crawlability?
6) What verified AI citations have you generated for clients?
Core Mechanisms: What an Agency Must Understand
Before sitting down in a pitch meeting, marketers should familiarize themselves with the new tech mechanisms that drive modern AI search. If a prospective agency can’t adequately talk to the following three foundational concepts without using vague marketing buzzwords, they may not possess the technical depth required to manage modern search visibility:
- Brand Entity Recognition: Large Language Models (LLMs) organize web data around "entities": distinct, identifiable concepts such as companies, products, executives, and technical categories. AI search engines do not just match keywords; they evaluate the mathematical probability that your brand belongs in the same category as your competitors based on consistent information across the web.
- Schema Markup (JSON-LD): Structured code embedded directly into a website's HTML that translates on-page text into explicit machine data. Valid schema code allows search engines and LLM crawlers to identify what a software product does, who built it, and what specific questions it answers without parsing ambiguity.
- Citation-Engine Crawling & RAG: Modern answer engines use Retrieval-Augmented Generation (RAG). When a user enters a query, specialized crawlers (such as PerplexityBot or ChatGPT-User) retrieve relevant web passages, verify the facts, synthesize a custom response, and link back to the primary sources.
The 6-Question Agency Audit Checklist
Use these six diagnostic questions during agency pitch meetings to cut through sales jargon and evaluate whether a vendor understands modern search architecture.
Question 1: How do you optimize content for LLM answer engines versus traditional search crawlers?
Traditional search crawlers index web pages to rank them based on keyword density and backlink signals.
Large language models process content differently: they extract facts, evaluate information, and look for structured data they can easily synthesize into conversational answers.
An agency has know how to adjust content architecture. Using Direct Answer or Bottom Line Up Front (BLUF) formatting, explicit Q&A headers, and factual data tables all make it easier for AI models to parse and cite text cleanly.
| Response Type | What the Agency Says | What It Actually Means |
|---|---|---|
| Red Flag | "We use AI writing tools to publish 50 blog posts a month targeting high-volume keywords to dominate search results." | The agency is churning out generic, low-effort copy that fails Google's quality standards and offers zero information gain for AI citation models. |
| Green Flag | "We structure content modularly using Direct Answer definitions, native HTML tables, and citable data so RAG systems can extract direct answers and cite your domain." | The agency understands the technical mechanics of answer extraction and knows how to format content for both human readers and machine models. |
Question 2: What is your structured data and schema strategy for brand entity recognition?
A modern search strategy cannot rely solely on standard meta title tags and H1 updates.
To make sure AI models recognize your company as a verified entity within your industry, your website and content must implement comprehensive JSON-LD schema markup. Ask the agency specifically which schema types they deploy and how they connect your on-page data to broader knowledge graphs.
| Response Type | What the Agency Says | What It Actually Means |
|---|---|---|
| Red Flag | "Our WordPress plugin handles all our SEO schema automatically behind the scenes." | The agency uses basic, out-of-the-box templates that provide minimal entity context to search crawlers and AI answer engines. |
| Green Flag | "We architect custom, nested JSON-LD schema, such as including Organization, SoftwareApplication, and TechArticle, connecting your brand entity to established industry knowledge graphs." | The agency writes explicit technical code that directly informs machine-learning crawlers about your product's category and capabilities. |
Question 3: How do you measure and report 'Share of Model' and AI Visibility?
If an agency provides a monthly reporting deck that only tracks traditional keyword rankings and organic website traffic, they are operating with a blind spot. In an environment where zero-click AI Overviews and conversational answer engines resolve user questions directly on the search page, traditional rank tracking no longer reflects true search behavior.
An agency must demonstrate how they track Share of Model (SoV), or rather how frequently and accurately your brand is mentioned across conversational prompts compared to direct competitors.
| Response Type | What the Agency Says | What It Actually Means |
|---|---|---|
| Red Flag | "We track keyword positions on Google. If your keyword rank is high, your AI visibility will take care of itself." | The agency does not have tools or processes to audit conversational AI platforms, leaving you blind to how ChatGPT or Perplexity represents your brand. |
| Green Flag | "We test intent-mapped prompt libraries across ChatGPT, Perplexity, and Google AI Overviews to track Citation Share, recommendation rankings, and brand sentiment trends over time." | The agency actively tracks generative search outputs, measuring whether your brand is being cited, excluded, or misrepresented in AI answers. |
Question 4: How do you build and seed citable digital PR and off-site consensus?
AI models do not evaluate your website in a vacuum. When an LLM generates a software recommendation, it cross-references independent web sources to verify claims. If your website claims you are the leading enterprise compliance software, but industry review platforms, developer forums, and news outlets say otherwise, the AI will drop your product from its recommendations.
Ask the agency how they coordinate off-site visibility to build the web-wide consensus that AI models require.
| Response Type | What the Agency Says | What It Actually Means |
|---|---|---|
| Red Flag | "We build 20 backlinks per month on high-domain-authority guest post websites to boost your PageRank." | The agency uses outdated, low-quality link-building tactics that carry zero authority with modern AI citation algorithms and risk Google manual penalties. |
| Green Flag | "We seed proprietary research, case studies, and expert commentary across industry review sites, technical forums, and authoritative media to establish entity consensus across the web." | The agency builds genuine authority across the external platforms that LLMs crawl to verify facts and select citations. |
Question 5: How do you audit technical LLM crawlability and site rendering?
Not all web crawlers behave like traditional Googlebot. AI search models utilize specialized user agents to crawl and retrieve content for real-time synthesis. Furthermore, heavy client-side JavaScript frameworks can prevent AI crawlers from accessing the text on your pages entirely.
A qualified agency must be able to audit your robots.txt configuration, monitor server response times, and verify that your content renders cleanly for generative search bots.
| Response Type | What the Agency Says | What It Actually Means |
|---|---|---|
| Red Flag | "If Google can crawl your website, every AI engine can read your content without any issues." | The agency does not monitor AI-specific user agents and may be unaware of technical rendering blocks or accidental crawler exclusions. |
| Green Flag | "We audit your server-side rendering, check crawl permissions for specific bots (like PerplexityBot and ChatGPT-User), and optimize crawl efficiency to ensure smooth RAG extraction." | The agency actively manages technical infrastructure to ensure machine crawlers can access, render, and index your critical product information. |
Question 6: What verified AI citations and pipeline results have you generated for clients?
The ultimate test of an agency's competence is demonstrated performance. Do not accept theoretical explanations or generic client logos. Ask the vendor to show direct examples where their strategic interventions increased a client's citation frequency in Google AI Overviews or generative answer engines, and how that visibility connected to pipeline growth.
| Response Type | What the Agency Says | What It Actually Means |
|---|---|---|
| Red Flag | "AI search is too new to measure directly, but our general traffic numbers are trending up across our client accounts." | The agency has not developed a dedicated AI search strategy and is treating modern search as an unmeasured byproduct of legacy work. |
| Green Flag | "We can demonstrate specific case studies where restructuring content and schema led to measurable increases in AI Overview inclusions and direct pipeline growth." | The agency possesses a proven, repeatable methodology for securing citations and measuring real-world business impact. |
- According to AuthorityTech, 72% of B2B buyers query conversational AI platforms to evaluate software features and compare vendors before ever initiating contact with a sales representative.
- In technical category audits, nearly half of B2B tech brands return zero mentions or citations across primary conversational evaluation prompts, leaving category demand entirely to proactive competitors.
"When everyone can publish, most content becomes noise. It's time to trade tool-bloat and random acts of marketing for a predictable system that actually compounds and earns trust."
— Kerry Guard, CEO & Founder at MKG Marketing
B2B search has shifted. Don't let an agency hide behind fresh pitch slides while delivering the same decade-old deliverables. Before signing a contract, make sure they can prove how they optimize for LLM crawlers, track citation share, and get your brand recommended where buyers are actually looking. This is exactly the game we play at MKG Marketing.
Audit Your Digital Presence for the AI Era
- Ready to test your website's readiness for modern AI answer engines? Request an MKG AI Search & Technical SEO Audit today.
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