Search has managed to do something impressive. At the precise moment it should be becoming more important and valuable to clients, large parts of the industry have chosen to argue themselves into irrelevance.
The real argument is about ownership.
Who gets to define what search becomes next?
Who gets the budget?
Who gets to explain what happens when search stops being a list of links and starts becoming a machine that recommends answers, brands, and actions?
“It’s just SEO” has done so much damage. It sounds calm and experienced, like the sort of thing a serious search veteran would say to quiet the room.
But it’s not strategy. It’s a meme constraining one of the biggest commercial opportunities the search industry has had in years.
Why memes matter to search
Memetics isn’t new. Richard Dawkins coined the term in “The Selfish Gene” in 1976, proposing that ideas, behaviors, and phrases spread through culture using the same logic as genes spread through populations. They replicate, mutate, and compete. The survivors aren’t necessarily the most accurate. They’re the easiest to copy.
Susan Blackmore took this further in “The Meme Machine,” arguing that humans are essentially meme machines: brains built to imitate, transmit, and store cultural information. The ideas that spread aren’t the truest ones. They’re the stickiest.
Consider “Happy Birthday to You.” The melody is simple enough to remember after one hearing. The words require no expertise to learn. The social context — a celebration, a cake, a room of people — gives everyone a reason to join in. Nobody decides to keep it alive. It keeps winning the competition for space in human memory and behavior.
“Jingle Bells” works the same way. It has no official guardian. It spreads because copying it costs nothing and signals belonging to a shared culture.
Slogans, rumors, political lines, and professional clichés travel the same way. They don’t survive because they’re correct. They survive because they’re easy to repeat, socially useful to the person repeating them, and emotionally charged enough to keep spreading. Accuracy isn’t part of the selection criteria.
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How ‘it’s just SEO’ became the dominant meme
When GEO entered the industry conversation, the reaction was immediate. Some people looked at generative search and saw a materially different interface. They saw AI systems summarizing, recommending, citing, and generating answers in ways that didn’t behave like classic search results. They saw a need for new tools, workflows, measurement, and thinking.
Others saw a threat. For much of the SEO influencer community, the response was containment. “It’s just SEO” became the line. Then the chant. Then the weapon.
The phrase worked because it was perfect meme material: short, repeatable, and certain without requiring much investigation. It also protected status.
If GEO is just SEO, the existing hierarchy stays intact. The same speakers keep the spotlight. The same consultants keep the authority. The same agencies keep the same budgets, or avoid having to rethink how the new landscape changes their work.
Then came the uglier meme: “GEO grifter.”
That one did even more damage. It didn’t just question the term. It framed anyone using it as suspect. It turned curiosity into suspicion and experimentation into opportunism. It encouraged dismissal instead of investigation.
This is how professional consensus often forms online. Visible people repeat a simple framing, algorithms reward it, and repetition starts to look like agreement.
And this is where the search industry started harming itself. As the framing spread, consultants repeating it gained visibility and social reinforcement, while clients and brands increasingly saw generative search differently.
Clients buy certainty, not acronym wars
Marketers outside the SEO echo chamber are already ahead of many search specialists. They can see the interface changing because they use generative systems every day.
I’ve seen it firsthand. At BrightonSEO and several recent conferences, I asked the room a simple question: Who here is using AI to make decisions, solve problems, or get work done?
The hands went up. Not a few hands. All of them.
Hundreds of people in different rooms gave the same answer without needing to be briefed, persuaded, or dragged through a 30-post LinkedIn argument about terminology.
When marketers and business people are already changing how they search, decide, and work, the industry doesn’t get to sit in the corner insisting nothing has changed.
Clients don’t buy theological disputes. They buy certainty.
SEO has never been an easy channel to sell. Many companies have been burned by vague retainers, vanity metrics, and content strategies that produced a library of articles nobody needed.
At the same time, good SEOs have built companies, saved jobs, and created revenue. Both things are true, which is why the current argument is so dangerous.
If the industry can’t explain what has changed, buyers will defer. They’ll move budget into paid search, paid social, or whatever advertising unit Google, OpenAI, or Meta sells them next.
Organic search won’t get the exploratory investment it needs because the people who should be leading the conversation are still arguing about whether the word GEO is allowed to exist.
LinkedIn’s B2B Institute and the Ehrenberg-Bass Institute make this clear in their report, “Easy to find: Being where B2B buying happens.” The argument isn’t built around acronym point-scoring. It’s built around mental and physical availability. B2B brands grow by being easy to think of, find, and buy.
Physical availability covers three dimensions: presence, prominence, and portfolio. In a digital world, that means being discoverable across every environment where buying actually happens, not just the ones that existed five years ago.
The report explicitly describes GEO as “the new wave of SEO” and states that generative engine optimization rewards foundational brand-building: authority, relevance, thought leadership, authentic reviews, and earned mentions. It also notes that generative search and LLM-powered discovery are reshaping how information is surfaced, with relevance determined by content authority and context, not keywords.
The marketing scientists aren’t saying “write more keyword articles and relax.” They’re saying discoverability is changing, but the underlying fundamentals remain.
Be easy to think of and easy to find.
Build distinctive assets, create authority, and show up where buyers are looking.
This isn’t a choice between SEO and GEO. It’s a physical availability problem in a new search environment.
The 9 a.m. to 5 p.m. test
“It’s just SEO” collapses too much into one bucket. SEO already means different things to different people. To one person, it means technical hygiene. To another, content production. To another, digital PR. To another, ecommerce feeds, internal linking, and category pages. To another, local search or revenue-focused organic growth.
So when someone says GEO is “just SEO,” the obvious question is: Which SEO, exactly?
“Just SEO” sounds simple until you ask what it means between 9 a.m. and 5 p.m.
What are you doing today to increase the likelihood that a generative system recommends your brand in a buying situation?
What are you measuring?
What sources are you influencing?
What third-party evidence are you earning?
What brand associations are you strengthening?
What prompts, citations, and recommendation contexts are you monitoring?
If the answer is “helpful content,” we’re in trouble.
Helpful content isn’t a strategy. It’s a phrase so vague it means everything and nothing.
Brands need extractable, repeated, credible information about the problems they solve and the situations in which they should be chosen.
That’s why GEO is closer to digital PR, brand strategy, and content marketing than many people want to admit.
No name, no budget
Markets don’t fund things they can’t name.
A name isn’t decoration. It’s a buying mechanism. It’s how a nervous CMO turns a vague threat into a line item. It’s how procurement understands why last year’s SEO retainer isn’t automatically the answer to this year’s generative search problem.
If GEO is “just SEO,” it gets dragged into the existing SEO budget. And most SEO budgets are already fighting for oxygen. So the industry’s grand commercial plan is this: take a new interface, a new buyer behavior, a new measurement problem, and a new competitive surface, then hide it inside the same budget clients were already reluctant to increase.
That’s commercial self-sabotage.
Call it GEO, AI search visibility, or SEO evolved. The exact label matters less than creating a commercially legible category. Once a category has a name, it can have a brief. Once it has a brief, it can have a budget, a team, a process, a dashboard, and a target.
Kill the name, and you don’t protect SEO. You shrink the market it should’ve owned.
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A better way to frame the shift
There’s a simple way out of this mess.
Call GEO “SEO evolved” if that helps. Call it “SEO rebranded for generative search” if that allows people to cross the bridge without losing face. But stop pretending nothing has changed.
Search is becoming generative, and brands need to become easier for AI systems to retrieve, understand, and recommend.
The goal is no longer just to rank. It’s to be recommended. To be:
Present in the answer.
Visible in the journey.
Credible sources.
Easy to choose when a buyer moves from curiosity to consideration.
That requires SEO skills. It also requires digital PR, brand strategy, technical understanding, measurement, and serious marketing thinking. GEO is SEO growing into the rest of marketing.
The brands that adapt to that shift will earn visibility as search changes. The ones still treating it as a naming debate risk missing the commercial opportunity entirely.
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Every SEO agency has a hidden context tax. It shows up when a strategist, content lead, or analyst opens Claude and starts rebuilding all the dos and don’ts for that particular account from memory: the brand voice, the keyword cluster killed last quarter, the CMS limitation, the founder’s rejected angle, the competitor the client doesn’t want mentioned.
That’s the part of AI adoption we’re still underestimating. LLMs can help with specific SEO tasks, but the problem with unleashing AI on more complex work is providing enough account context to make it useful without creating more review work.
One solution is a per-client memory system called a “client brain.” It gives account context a place to live, allowing AI to support the work without treating every task like it’s the first day on the account.
Context is the problem
Context is essential for any worker. A senior SEO account lead onboards human teammates onto client accounts by sharing the strategy, history, politics, preferences, constraints, client language, technical limitations, and all the “don’t do that again” lessons that never quite make it into the brief.
LLMs have inherited that same agency problem. The difference is that AI hits it every time it’s asked to support the work without knowing the account.
A lot of the AI conversation in SEO right now is about connecting data sources. Load GSC, GA4, Ads, crawl data, rank tracking, and maybe CRM data into one place, so that we can finally “chat” with the data.
That’s genuinely useful, especially with live alerts. But for agencies, analysis is just one part of the job. AI also needs account context to summarize a technical audit without recommending a fix the dev team already rejected or to write a brief that sounds like the client and fits the strategy.
That kind of work depends on institutional memory: the account knowledge that builds up after months of working with a client and its stakeholders.
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A client brain is the solution
A client brain gives that institutional memory a shared home. The team updates it as decisions are made, feedback comes in, and the account evolves. This isn’t a replacement for human judgment. It’s infrastructure that helps that judgment travel across workflows.
In an agency world, SEO work rarely belongs to one person. The strategist sets direction, the content lead builds the brief, the writer drafts, the analyst checks performance, and the technical SEO reviews implementation.
When context stays in people’s heads, every handoff creates drift. When it’s shared, the work stays aligned. A strategist ramps faster, a writer misses fewer client preferences, and the team spends less time re-explaining the account.
What a client brain is
A client brain is a structured, per-client knowledge base that AI reads before it starts the work. Think of it as the institutional memory of an SEO account, written in a way the machine can use.
Not all client knowledge behaves the same way. Some knowledge is stable: the brand, audience, positioning, voice, product, category, and lines the client doesn’t want to cross. Some knowledge is active: decisions, experiments, objections, failed angles, technical blockers, and lessons from client feedback.
Those two types of knowledge need different homes. A client brain splits them into two layers: the soul and the memory.
The soul is static, identity-level knowledge: Who the brand is, how they speak, who they serve, what they sell, and what “good” sounds like for them
The memory is dynamic, experience-level knowledge: What the team tried, what worked, what failed, what the client rejected, and what changed during the campaign
This split keeps the brain usable. If everything goes into one big file, brand principles get buried under meeting notes, and old keyword decisions start looking like the current strategy.
The technical anatomy of a brain
A client brain doesn’t need to be a complicated system. It is built as a simple folder of plain-text Markdown files. You don’t need special software, a database, or a custom interface.
Building core logic of the soul
To get started, go into your existing client project folder and create a sub-folder named brain, then create one more folder inside that named soul. This folder path (brain/soul/) is where the core logic of the system lives. It consists of five files, each doing one specific job:
This is the operating version of the client, not the polished marketing version. Who is this client? What do they really sell? Who do they compete with? Where do they win? Where are they not trying to play?
Six honest sentences usually beat a six-page deck because the AI doesn’t need the full brand story. It needs enough context to avoid bad adjacent decisions.
A real example, anonymized:
“[Client] is a DTC Japanese-style kitchen knife brand selling chef knives, paring knives, and care accessories. They serve home cooks who value craftsmanship over price, with an average order value around $180. Their differentiator is free in-house sharpening for life. They compete with Made In and Misen on the tier just below Shun and Global. They don’t sell to commercial kitchens or restaurant supply, those have separate procurement cycles. Their highest-converting traffic comes from long-form reviews and YouTube cooking channels, not paid social.”
That’s enough information for AI to make better SEO choices. It knows not to chase restaurant-supply keywords, not to position the brand as the cheap alternative to Shun, and to weight content toward reviews, comparisons, and care guides.
The point isn’t to sound impressive. The point is to be true.
style-guide.md
This file is where most teams accidentally write something useless. “Warm but professional” doesn’t help AI much. Neither does “expert but accessible.” What works is concrete instruction: one paragraph on tone, a few examples that pass, and a few that fail.
audience.md
The audience file is where the team stops writing for demographics and starts writing for people. “Small business owners aged 35 to 55” is a targeting box, not an audience. Useful audience context captures worries, objections, misconceptions, language, and what earns trust.
keyword-map.md
You do not need to create a 500-row export from your keyword tool. Instead, capture how the brand thinks about the category: primary terms we own, secondary terms we want, competitor-owned terms we approach carefully, and terms we don’t want to touch.
never-do.md
This is the file I wish I’d had years ago. It’s the list of things AI should never propose, never write, and never recommend.
Some are brand-level: “Never describe the client as an industry leader.”
Some are operational: “Don’t suggest content that requires legal approval unless the account lead confirms it first.”
Some are strategic: “Don’t recommend State X landing pages. The client doesn’t serve that state yet.”
Every “we already discussed this and decided no” should eventually end up here. AI is very good at confidently resurfacing dead ideas. This file stops the team from having the same conversation every month.
Memory captures decisions, patterns, and logs
Memory lives in brain/memory/. It’s organized differently from the soul because it comes from doing the work.
brain/memory/
├── decisions/ — choices made and why
├── patterns/ — things that worked or didn’t, by task type
└── log/ — chronological notes by date
The decisions/ folder stores choices made and why. A memory entry looks like this:
# 2026-04-21 — Content brief for Q2 implant campaign
Decided NOT to target "dental implants near me" as the primary keyword.
Reason: Client doesn't accept Medicaid; the highest-volume "near me" searches in our markets skew Medicaid.
Pivot to "premium implants [city]" framing.
Source: Client strategy call notes, 2026-04-21.
Tags: client:[name], task:content_brief, type:decision
The reason matters more than the decision. If AI only knows “don’t target dental implants near me,” it may avoid that keyword forever, even when the context changes. If it knows why, it can make better adjacent decisions later.
The patterns/ folder
This stores what the team learns across repeatable work. After enough AI visibility audits, for example, our system started building a pattern file around where those audits tend to break: changing DOM selectors, fabricated review counts, Cloudflare blocking direct fetches, and tools returning partial data without making the failure obvious.
The log/ folder
Here is where you keep the running journal: meeting summaries (AI transcripts are great here), daily notes, client comments, and small updates that don’t yet deserve to become formal decisions. Most of it won’t be read again. But when something breaks two months later, the answer is often in the log.
One warning: A brain should capture operating knowledge, not raw sensitive data. Don’t turn it into a warehouse for exports, transcripts, credentials, private client documents, or anything the team wouldn’t want surfaced in the wrong context.
Don’t start with every client. Pick the account where context loss is already costing you time.
Usually, that means a long-running client with a strong brand voice, a history of rejected ideas, and multiple people touching the work each week.
Step 2: Block 90 minutes and write the soul together
Get the account lead and strategist in the same room or on the same call. Open the five soul files and write in plain sentences. Use real examples. Don’t try to make it perfect.
The goal isn’t to create a brand book. It’s to write down the context your best account person already carries around in their head.
Step 3: Decide where the brain lives
If you’re solo, a local folder may be enough. If you have a team, you need one shared source of truth.
Technical teams can use git: track the Markdown files, not raw client data. Non-technical teams can use Google Drive, Notion, or another shared workspace. The tool matters less than the rule: one client, one brain, one place everyone trusts.
Step 4: Set ownership rules
Soul changes need friction. That’s intentional. If every passing comment gets added to the soul, the brand layer gets polluted. The account lead should own it, review changes, and decide what becomes stable client truth.
Memory should be easier to update. Anyone working on the account should be able to add a sourced entry when a client rejects an angle, a tactic fails, a blocker appears, or the team learns something that shouldn’t be lost.
Step 5: Schedule maintenance
Memory gets messy if nobody owns it. Every couple of weeks, someone should clean the brain: consolidate duplicates, remove stale notes, surface conflicts, and check whether old decisions are still true.
Then schedule a quarterly soul review and ask one question: “Is anything here no longer true?” A stale brain is worse than no brain because the AI will sound confident while working from old context.
How AI agents read the brain
Once a brain exists, the question becomes operational: Which files should the AI agent read whenit starts a brief, audit, competitor analysis, or reporting summary?
This is where the brain proves its day-to-day value. A strategist, content lead, and analyst may all touch the same client in the same week. Without shared context, the brief drifts from the strategy, the content drifts from the brief, and the audit repeats what the team already knows.
The brain keeps that work aligned without turning every task into another meeting, Slack thread, re-explanation, or rewrite. There are three ways to handle this.
Version A: Load everything
The simplest version is to have the AI read every file in the brain folder before it starts: all soul files and the full memory folder.
For a new client, that might only be a few thousand tokens. For a client active for six months, it can become 30K to 50K tokens per session. That’s a real cost, but often still cheaper than the human time lost re-explaining the account every week.
Start here if you’re testing the idea. Run the same task twice: once with the brain loaded, once without it. Use something real, like a content brief, metadata rewrite, technical summary, or internal linking recommendation. If the brain-loaded version is more accurate, more on-brand, or avoids a mistake the team would normally catch manually, you’ve got your signal.
Version B: Route by task type
The next version is selective loading. Instead of asking AI to read everything, you give it a router file that tells it which parts of the brain to load based on the task.
For example:
# claude.md
At the start of every task, ALWAYS read:
- brain/soul/company-profile.md
- brain/soul/never-do.md
IF the task involves writing copy, ALSO read:
- brain/soul/style-guide.md
- brain/soul/audience.md
IF the task involves SEO content briefs, ALSO read:
- brain/soul/keyword-map.md
- brain/memory/decisions/ latest 5 entries
- brain/memory/patterns/content_briefs.md
IF the task involves debugging a tool failure, ALSO read:
- brain/memory/patterns/tool_failures.md
AI reads the instructions, decides which rules apply, and loads only the relevant files. Token cost drops. Context gets cleaner. This is where most agencies should stop for a while.
It’s still just Markdown. No database. No new platform. No complicated setup. The discipline is in writing useful files, keeping them current, and making sure AI reads them before doing the work.
Version C: Vector retrieval
The more advanced version is vector retrieval. If you’re managing 20 or more active clients, each with deep memory, you can tag entries with metadata, embed them into a vector store, and retrieve only the most relevant items at the start of each task.
AI can also write back to memory, but this needs guardrails. Don’t ask it to summarize every session and dump the result into the brain. That creates noise fast. Write to memory only when something specific happens: a task fails, and the team finds a workaround, a client rejects an angle, the account lead corrects the AI on something client-specific, or a decision gets made that should affect future work.
Event-triggered writes are useful. Session-end summaries usually aren’t. And every write needs a source.
Using the brain across Claude Code, Chat, and Cowork
The surface matters less than the pattern. Whether the team is using Claude Code, Claude Chat, Cowork, or another AI workflow, the rule is the same: AI should read the client’s soul before doing anything important.
In Claude Code, place the brain folder at the root of your project and add a claude.md instruction telling it to read /brain/soul/ at the start of every task. Treat never-do.md as a hard constraint, not a suggestion.
In Claude Chat, create one project per client and upload the contents of brain/soul/ into Project Knowledge. Don’t share one project across clients. That’s how one client’s tone, rules, or constraints start bleeding into another.
In Claude Cowork, use a task template that attaches the brain folder at the start. For repeatable tasks like content briefs, SERP reviews, metadata refreshes, or AI visibility audits, build the brain attachment into the workflow.
You’re not just making AI faster. You’re making the starting context consistent.
Where this breaks (and how to fix it)
Once the brain starts shaping real work, a few failure modes show up quickly. Most aren’t technical problems. They’re maintenance problems, which means they’re fixable if someone owns the review process.
Drift: AI produces work that’s almost right, but slightly off. Usually, the style guide is too abstract. The fix isn’t more adjectives. It’s better examples: pass/fail pairs, before-and-after intros, weak and strong meta descriptions, or a sentence the client rewrote with a note explaining why.
Stale soul: The client repositions, changes their offer, shifts into a new market, drops a service, or changes how they want to talk about themselves. Nobody updates the soul, so AI keeps producing work from the old reality. The fix is a quarterly soul review. Ask: “Is anything here no longer true?”
Memory rot: Some memory entries were true when written, but stop being true later. A client rejected comparison content six months ago, then decided to test it. The fix is to date entries clearly, include the reason behind each decision, and remove or update entries when the account changes.
Fabrication: This is the failure mode to take seriously. AI can write false memory, not maliciously, but because it’s trying to be helpful. When a task fails or a source is incomplete, the model may still produce a clean-looking note that sounds plausible.
We’ve seen AI fabricate ChatGPT search queries, report review counts that weren’t tied to reality, and create explanations for tool failures that sounded reasonable but weren’t supported by the output. Memory compounds. One false entry can influence future briefs, audits, recommendations, and client-facing work.
The fix is provenance. Every factual memory entry needs a source: a meeting note, client quote, tool output, strategist correction, or linked deliverable. No source, no entry.
A brain is only useful if the team trusts it. Trust doesn’t come from the folder structure. It comes from knowing where the knowledge came from.
How to get started this week
You don’t need the full system to start. Start with one client, one 90-minute session, and one before-and-after test.
Pick one client. Choose the account where re-explaining context costs the most time.
Block 90 minutes this week. Write the five soul files with the account lead and strategist. Use plain sentences, real examples, and concrete corrections. Don’t let adjectives do all the work.
Add a router file. Keep it simple at first. At the project root, add one instruction: “At the start of every task, read everything in brain/soul/.”
Run a real SEO task twice. Use a content brief, keyword cluster, meta description rewrite, SERP analysis, internal linking recommendation, or audit summary. Run it once with the soul loaded and once without it. Compare the outputs honestly.
Start writing memory from the next session. When AI recommends a ruled-out keyword angle, a client pushes back on tone, or a technical recommendation gets blocked by the CMS, capture the lesson and the reason.
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AI works better when account knowledge survives
Most teams don’t have an AI intelligence problem. They have a context problem. They haven’t written down what their best account people already know, or separated stable client knowledge from working history. That’s what the client brain fixes.
The agencies that get the most from AI won’t just be the ones with better prompts, models, or automations. They’ll be the ones that preserve the context behind the work: the client history, rejected angles, technical constraints, tone corrections, and small decisions that make an account make sense.
Because speed without memory creates more review, more correction, and more “we already talked about this” moments.
The real opportunity isn’t using AI to push more SEO work through the system. It’s using AI to carry forward the context that makes the work better.
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Schema markup has earned its place at the center of the SEO and GEO conversation. Google and Bing have confirmed they use structured data to power AI Overviews, and ChatGPT factors it into product recommendations.
Now, schema markup is becoming part of the infrastructure behind the agentic web, where AI systems increasingly interact directly with websites on behalf of users.
For AI agents, understanding content isn’t enough. They also need to interpret and act on it. Schema markup helps make that possible.
The role of schema markup in the agentic web
In traditional search, schema helps drive visibility by making content more eligible for SERP features and helping search engines better understand entities. That information supports the index and knowledge graph, influencing how results appear to users.
AI agents take this further. They use schema markup not only to identify entities, but also to understand relationships, relevance, and whether content is trustworthy and actionable enough to support recommendations or complete tasks.
Structured data also makes websites easier and cheaper for AI systems to process. Parsing unstructured HTML is computationally expensive compared to reading clean, structured data, especially as LLMs operate within finite context windows and growing inference costs.
As these systems scale, sites that make their content easier to interpret become the path of least resistance for AI agents.
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NLWeb and the infrastructure of the agentic web
Schema markup is the foundation, and NLWeb is built on top of it. Understanding this connection is essential for anyone thinking ahead.
NLWeb, Microsoft’s open-source initiative, enables websites to easily add AI-powered conversational interfaces. It effectively turns any website into an AI app that lets users query content using natural language.
Think of it as the difference between a website a human browses and a website an AI agent can interrogate directly — asking questions, retrieving structured answers, and acting on them without any human in the loop.
To be truly agentic, a site must move beyond being “read” to being queryable. NLWeb is designed to help AI agents interact with websites through natural-language queries and structured responses.
While schema tells an agent what is on the page, NLWeb enables more direct interaction with that information in real time. It’s the difference between an agent reading a static menu and an agent asking, “Do you have a table for four at 7:00 PM tonight?” and receiving a deterministic, real-time answer.
NLWeb was conceived and developed by R.V. Guha, who recently joined Microsoft as CVP and technical fellow. Guha is the creator of widely used web standards, including RSS, RDF, and Schema.org.
The same person who built the vocabulary that defines structured data on the web is now building the protocol that lets AI agents use it. That’s a through-line, not a coincidence.
NLWeb leverages existing structured formats, such as Schema.org and RSS, and LLM-powered tools to create natural language interfaces usable by both humans and AI agents.
It isn’t asking you to rebuild your content infrastructure. It’s asking you to have your schema markup in order so it can take it from there.
As a search marketer, you’ve probably been implementing schema markup for years. Here are some new considerations as you optimize for the agentic web.
1. Prioritize completeness over coverage
It’s better to have fully populated schema markup on your most important pages than thin markup spread across your entire site. AI agents prioritize properties that help them answer user queries directly.
For a product page, that means price, availability, ratings, and specifications, not just a product name. Incomplete schema signals uncertainty to agents, while complete schema signals reliability.
2. Automate where you can
Manual schema management doesn’t scale, which is a challenge for teams without dedicated technical SEO resources. Some platforms can handle this automatically for key page types — like product pages, blog posts, events, bookings, and local business information — generating markup by default when content is created.
This baseline matters for both coverage and consistency. Stale or mismatched structured data actively works against you: If your schema says a product costs one price and your page displays another, agents will distrust both signals. Agents can also trust a signal more when it appears reliably across a site than when it appears sporadically.
3. Use AI to scale implementation
Platform automation handles the baseline — but AI can go further, analyzing your content to generate more specific and relevant markup. With AI, you can scale structured data generation, installation, and validation.
4. Use JSON-LD
This isn’t new advice, but it’s more important than ever. JSON-LD is cleanly separated from your HTML, making it far easier for agents to parse programmatically. Google’s official guidance explicitly recommends JSON-LD for AI-optimized content.
5. Think about your schema as a site-level graph
Agents benefit from understanding how your content connects across your entire site: how articles relate to authors, how products relate to categories, how services relate to locations. This means you should periodically audit your structured data at scale. Take note of:
Which page types have markup and which don’t.
Where entity definitions conflict across URLs.
Whether your Organization or Person markup is consistent.
The goal is a coherent, connected picture of your site’s entities, one that an agent can trust regardless of which page it enters from.
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The window for early mover advantage
AI systems increasingly prefer sources they have already indexed, validated, and found reliable in prior interactions. For agentic optimization, early adoption matters. Content that establishes itself as agent-friendly now builds compounding advantages as agents develop preference patterns.
Schema markup has always rewarded the teams that took it seriously. In the agentic web, the stakes of getting it right — and the cost of ignoring it — are substantially higher. The agents are already crawling. The question is what they find when they get to you.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-01 12:00:002026-06-01 12:00:00How to use schema markup to optimize for the agentic web
Google and LLMs both rely on third-party signals — backlinks, brand mentions, expert commentary, and coverage in trusted publications — to decide which brands deserve visibility.
PR and SEO both generate those signals, but most teams still operate independently.
When they do collaborate, it’s usually to treat PR as a link-building opportunity rather than a real partnership.
This leaves authority on the table.
But the real gains happen when these teams operate as one.
In this article, you’ll learn a five-step playbook for turning PR and SEO into an always-on authority engine.
I also spoke with two digital PR experts about how they’re partnering with SEO to build more authority across search, media, and LLMs.
Free resource: Download our PR + SEO Outreach Planner to align pitching, prioritize outlets, and track results. It includes a pitch ownership guide for deciding who pitches what and when.
Step 1: Align PR and SEO Research
An always-on PR and SEO partnership starts with shared intelligence.
Without it, you get predictable gaps:
Content that ranks but doesn’t earn media mentions or AI citations
Coverage that builds awareness but doesn’t improve search visibility
AI citations and media coverage that go to competitors because they published first
Use PR Insights to Identify Emerging Content Opportunities
The biggest authority wins don’t come from PR and SEO staying in their own lanes.
They come from each team sharing insights that shape angles, assets, and placements.
For PR, this could be:
A sudden spike in journalist inquiries or media coverage around a topic
A new phrase or framing gaining traction among industry voices
Recurring themes across newsletters, conferences, or trade publications
Britt Klontz, digital PR consultant and founder of Vada Communications, says the strongest results come when PR and SEO combine their strengths at the ideation stage:
The best collaborations with SEO happen when PR is brought in early, before an asset or campaign is completed. We used to ask, ‘Can PR promote this?’ Now we ask, ‘How do we build something together that will help with search, media, and brand visibility from the start?’
To facilitate this partnership, build a regular channel for PR to flag insights to SEO.
This could be a shared Slack channel, spreadsheet, or standing agenda item.
For example, when I was the editor of the Hootsuite Blog, our PR team notified us that LinkedIn was shutting down its “Elevate” feature and suggested we should write a blog post about it.
No search volume existed yet, but we created the content anyway.
The post started gaining backlinks and driving a surprising amount of demo requests almost immediately.
Months later, the search volume appeared. And our post ranked #1.
Today, it still ranks near the top of the SERPs for terms like “LinkedIn elevate alternatives.”
AI tools like Claude also use the blog post as a top source for relevant prompts:
That’s the power of PR and SEO sharing information and acting on it quickly.
Rankings, backlinks, and AI citations that would have gone to a competitor built lasting authority for Hootsuite instead.
Use SEO Insights to Inform Content Topics
SEO has signals PR can act on too, including which topics are heating up and editorial gaps.
When conducting keyword research for PR, SEO should flag two things:
Informational gaps: Questions audiences are actively searching for, but no one is answering well yet
Trending terms in your niche: Journalists are likely already interested, which gives PR a clear opening
That’s why Rola Tfaili, communications manager for North America at Xero, brings SEO into her process from the start:
I want SEO insights — like emerging search trends, keyword gaps, and audience intent — to directly shape our PR narratives and campaign angles from the outset, before content is developed.
Here’s how you can do the same.
Not all keyword tools show you trends over time, so I’ll use Semrush for this step.
Note: If you don’t have a subscription, sign up for a free trial of Semrush One, which includes Semrush Pro and the AI Visibility Toolkit.
Search any term in the Keyword Magic Tool and look at the “SERP Features” column.
Two features in particular signal strong PR potential:
News and Top Stories: Google surfaces these for time-sensitive or trending queries — sometimes within 24–72 hours of a news event. If your topic triggers these features, journalists are actively covering it, and PR has an immediate opening.
Discussions and Forums: This signals that audiences are seeking advice or firsthand experience on the topic, which is often a sign of unmet demand and/or increasing interest
Next, use the Keyword Overview tool’s 12-month trend graph to confirm whether a topic is gaining momentum, seasonal, or fading.
A consistently rising trend is your strongest signal — media interest is likely to be building as well.
Pro tip: Don’t overlook existing topics. A trending term you already own is a valuable opportunity. PR can pitch it to journalists as a timely angle, repurpose it into new formats, or use it as a hook for a broader campaign.
For LLMs, you need a tool like Semrush’s AI Visibility Toolkit that shows actual prompt data, not just search queries.
This gives you insight into the exact prompts your competitors are earning AI visibility for, but you aren’t.
Those gaps are worth flagging to PR, especially if competitors are being cited as authorities on topics your brand should own.
Use your shared doc or Slack channel to provide real-time insights, so neither team works from stale data.
Topics that show up in both PR’s emerging trends and SEO’s keyword data are your highest-priority opportunities.
Step 2: Collaborate on AI-Ready Assets
An AI-ready asset is built to be found, cited, and trusted by search engines and AI models (while being valuable to humans).
This is also called answer engine optimization (AEO), which is the process of creating and structuring content for AI systems.
It can include optimizations like:
Headings that mirror how people search
Front-loaded key stats, details, and definitions
Sections that focus on one core idea
Bullet lists and tables that make key information more extractable
When you combine PR’s distribution power with SEO’s technical expertise, you get assets that earn visibility across search, media, and LLMs.
Original Research and Reports
Original data has long helped brands earn backlinks — now, it helps you build AI visibility too.
A collaborative workflow for this asset would look something like this:
SEO identifies the topic based on search demand and content gaps, and PR validates whether the angle is pitchable and shapes the findings into quotable hooks.
Together, they design the study so it’s structured for citations, with a clear methodology, front-loaded stats, and branded visuals that are easy to share.
SEO content teams might be tempted to create this type of asset on their own, then ask PR to pitch it.
But Britt says if PR is involved earlier, they can help answer questions like:
Is this a real story?
Is there a sharper edge here?
Do we need more reliable data?
Is there a better hook that fits the time?
Would it be more interesting if an expert gave their opinion?
That kind of information can make an asset more useful and impactful.
Pro tip: Give your asset a unique, branded name — like ‘The State of X Report’ or ‘The X Index.’ If journalists mention it without linking, people can still search for it and find you.
Don’t limit original data to a blog post.
High-value assets should have their own crawlable landing page — no gates, no PDF-only content.
Use the same URL each year for recurring assets to build authority. Then, link these pages to related content on your site (and vice versa).
This way, search engines and AI see your topical coverage as connected, not random.
Free Tools
Free tools that solve a specific pain point earn AI visibility, backlinks, and return visits long after launch.
This includes calculators, templates, checklists, and interactive assets.
The gap here is usually distribution.
SEO can build and optimize tools, but without PR’s contacts and timing, even the best ones can be limited by organic performance.
A strong hook helps, too.
Britt says an asset is easier to promote when it “blends search insights with something more personal, like proprietary data, a strong point of view, or a story angle that is relevant right now.”
The payoff is an asset that is reported on and shared widely across channels.
NerdWallet’s tariff calculator is a good example of this in action.
It launched as tariffs dominated headlines — and earned media coverage because of it.
Podcasts
A branded podcast can generate tons of coverage, review articles, and inclusion in “best podcasts on X topic” listicles.
Getting your experts on other podcasts is also valuable for building authority and visibility.
Third-party mentions get your brand and subject matter experts into the conversation, both in search engines and LLMs.
PR typically drives guest placements, but SEO can identify which shows already rank or get cited by AI for your target topics, so you’re pitching the ones that build the most authority.
Press Releases
When published on your site and optimized properly, press releases can become standalone, crawlable assets that increase your AI mentions.
In fact, press release citations in LLMs grew 5x between July and December 2025, according to Muck Rack.
To get the most out of press releases, both teams need to contribute.
Rola has seen the benefit of this collaboration firsthand:
For key assets like press releases, we integrate SEO insights early — before content is developed — and include SEO in the review process to ensure we’re maximizing visibility.
PR shapes the story and the hook. SEO makes sure the on-site version is crawlable, optimized, backed by citable data, and linked to related assets.
So the press release doesn’t just generate buzz, it feeds your broader authority.
Explainer Content
Explainers are easy-to-digest resources (usually articles or videos) that simplify complex topics or highlight key info about your brand.
They help journalists and LLMs write accurately and consistently about you — especially if your category is niche or complex.
SEO can use keyword and prompt data to identify the questions your explainers should answer and structure them so AI can parse and cite individual sections.
PR knows which questions journalists and analysts ask most often — and where the current gaps are in how your brand gets described.
The format can vary:
One-page proof point packet with key stats and third-party validation that PR sends alongside pitches
YouTube video with citable brand facts or product details
Dedicated pressroom that organizes assets by category with founder bios and press releases
(Bonus points for all three.)
Step 3: Co-Build Your Third-Party Presence
Brands are 6.5x more likely to appear in AI answers through third-party signals than their own content, according to AirOps.
This means PR and SEO have a real opportunity to work together to build more visibility across search and LLMs.
Rola sees this as an important shift for PR teams:
When we align closely with SEO to ensure our key messages land in credible, third-party outlets, we’re not just generating press; we’re helping position the brand to appear in AI search platforms. That intersection between PR, SEO, and now AEO is where I think we’ll see the most measurable impact moving forward.
Expert Commentary
When your experts are quoted consistently — on your own site, social media, and in trusted publications — Google and LLMs begin to associate them (and your brand) with that topic.
The biggest coordination gap is knowing where to focus.
SEO has the data on which topics have the most search and AI demand — and which publications are already earning citations for them. PR knows which journalists and outlets are most receptive and what angles resonate.
Together, they can pinpoint the exact publications and topics where a placement will improve results for both teams.
Then shape the commentary accordingly.
Concrete, data-backed quotes with a specific stat or firsthand insight are far more citable than generic thought leadership — especially for AI, which favors specificity it can extract and serve directly in an answer.
Getting your experts quoted online is a strong start — but it works best when paired with the other authority-building sources below.
Review sites like G2, Yelp, Google Reviews, and Trustpilot are trusted by AI for the same reason they’re trusted by humans.
They aggregate specific, unbiased information about products from verified users.
And AI frequently cites them for product recommendations:
Reviews across multiple sites also strengthen your brand’s authority signals.
It gives AI detailed evidence of what category you belong in, your core features and pricing, and why you should be trusted.
Forums work similarly — AI pulls from Reddit threads and Quora answers when users ask for honest recommendations or firsthand experience.
Brands that show up authentically and positively in these conversations earn another layer of trust signals.
You can’t control these mentions, but consistently showing up as a helpful, knowledgeable voice in your category’s communities builds the kind of organic mentions AI models trust.
PR and SEO should jointly identify which review sites and forums matter most in your industry.
Keep review profiles current and monitor relevant forum conversations for opportunities to contribute genuinely.
A Wikipedia page gives Google and AI a neutral, third-party source of facts about your brand.
It also helps establish your brand as a recognized entity in Google’s Knowledge Graph.
It’s a common source for Google’s Knowledge Graph, and it’s baked into LLM training data.
But to qualify for a page, you need to meet Wikipedia’s Notability Criteria.
This includes having significant coverage in reliable, independent sources that address your brand directly and in detail.
PR can help you earn this kind of coverage by pitching stories about your company to journalists in reputable publications.
Once you have a page, you won’t be allowed to edit it directly, as Wikipedia’s rules prevent self-promotion.
But SEO can monitor the page for inaccuracies and flag corrections, and PR can handle reputation monitoring to keep the narrative positive.
Pro tip: Use the same brand name, category language, and positioning everywhere: across your website, social profiles, press releases, and review site listings. The more consistent your language, the more confidently AI and Google can categorize and recommend your brand.
Step 4: Unify Your Outreach Strategy
If PR and SEO know what — and to whom — each team is pitching, you avoid mixed messages and misaligned timing.
And your odds of a yes go up.
It doesn’t take much to fix. Just a shared source list, a strategy to split pitching, and a regular check-in to stay aligned.
SEO has a list of high-authority domains that show up in organic rankings and AI citations. PR has a list of journalists, analysts, creators, and publications that influence their category.
Merging these gives you a single view of every third-party source worth going after.
Build it as a shared spreadsheet with three columns:
PR Sources
SEO Sources
AI Citation Sources
Then prioritize.
Any source that appears on more than one list goes to the top. It has double (or triple) the potential to impact your authority and visibility.
Pro tip: Update your list quarterly as sources can shift fast — especially in LLMs.
Create a shared pitch doc to go with your source list. Use PR’s standard pitch brief, or if one doesn’t exist, create one. Include headline stats, agreed-upon positioning language, and target URLs.
Whoever sends the final pitch customizes it to their contact. But using the shared pitch doc as a starting point ensures your basic story stays consistent.
Split Pitching by Strengths
Many high-priority pitches will need both PR and SEO to weigh in. But not all.
Divide the work of pitching based on what each team does best.
Generally, that means structured, technical placements for SEO and editorial, relationship-based placements for PR.
Your company may want to organize these tasks differently depending on industry or org structure, but here’s what I suggest:
SEO
PR
Pitch for inclusion in industry listicles
Pitch journalists and editors on newsworthy content
Fix unlinked brand mentions
Offer expert commentary to reporters
Reach out to sites with broken or outdated links
Submit to industry awards
Identify warm contacts from referring domains
Brief analysts at firms like Gartner and Forrester
Monitor AI citations for new outreach targets
Explore sponsored placements in newsletters, podcasts, and trade publications
Plan Pitching in Advance
Meet quarterly or monthly — whatever works for your schedules — to decide who is going to pitch what, to which outlets, and when.
This will help prioritize high-impact efforts and reduce accidental duplication of work.
Map outlets to objectives and target KPIs to determine ownership.
Every time you meet, review results from the last period. Prioritize more of what’s working and cut what isn’t.
PR and SEO usually track different metrics, like mentions and outlet quality vs. rankings and organic traffic.
The fix isn’t merging into a single dashboard.
It’s building a shared lens for evaluating what each asset actually did, no matter which team owns it.
Britt recommends that both teams agree on a shared set of questions to evaluate each asset:
Did it get any attention?
Did it get picked up by reliable sources?
Did it help with search goals?
Did it contribute to conversions?
Did it have results that lasted longer than a short-term spike?
As Britt puts it:
The best shared work usually helps with more than one thing at a time, like visibility, authority, discoverability, and brand credibility.
Visibility: Did We Show Up in the Right Places?
Getting in front of your audience more often — and in the places they care about — is one of the main advantages of having PR and SEO collaborate.
Track these metrics to see if it’s working:
Quality mentions in relevant outlets: Not raw mention count. A placement in a niche newsletter your buyers trust outweighs 10 mentions on unrelated blogs. PR likely already has a media monitoring tool for this.
Recurring format mentions: Listicles, comparison posts, and “best of” roundups will continue to earn backlinks and AI citations over time. They also show how your brand is positioned relative to competitors. Track these separately in your media monitoring tool or a shared spreadsheet.
Share of voice in category coverage: Report on the percentage of category coverage that mentions your brand vs. competitors. Free tools like Google Alerts and Mention’s share of voice calculator give you a general sense of how you’re doing. But paid media monitoring tools let you dig into specific platforms, outlet types, and topics.
For AI specifically, track how often your brand appears in AI answers for queries you care about.
You can manually check your top questions and prompts in LLMs to see if your brand is mentioned, but this gets tedious at scale.
The AI Visibility Toolkit is helpful here. It automates tracking so you’re not manually checking every LLM for every query.
You get an overall AI Visibility score for your brand, which measures how often you’re mentioned in AI systems compared to other brands.
The Competitor Research tool shows how your AI visibility stacks up against competitors, which is one of the clearest ways to show leadership whether you’re gaining or losing ground.
It also tracks your Share of Voice across AI platforms, a single metric that reflects the combined impact of your PR and SEO efforts.
Authority: Did We Become More Credible?
This is where you show if your brand is becoming a trusted source online.
Start by tracking new referring domains.
New backlinks matter too, but new domains are more meaningful because they represent more unique sources vouching for your brand.
Reporting on your website authority is also helpful. This is a third-party estimate of the level of trust search engines are likely to assign to your domain, based on your backlink profile and other signals.
Different SEO tools calculate it differently (and call it different things).
So, focus less on the score and more on the direction it moves over time.
Note: Meaningful changes to your Authority Score can take 3-6 months to appear.
The AI Visibility Toolkit tracks your mentions, citations, and cited pages over time, and tells you percentage increases and decreases.
When your authority score and AI mentions are both climbing, you’ll know your PR and SEO work is paying off.
Expert commentary placements, direct requests from journalists, and new journalist relationships are also worth tracking.
Increases in any of those areas are a strong signal that you’re gaining trust.
Google Alerts can catch mentions to help you track expert commentary placements, but a tool like Semrush’s Brand Monitoring gives you a more comprehensive picture.
It lets you track any query (SME names or other keywords) and provides:
Total mentions
Estimated reach
Traffic
Mentions with backlinks
Sources (Social media, news, and blogs)
Demand: Did It Help People Take the Next Step?
Did improving visibility and authority have any impact on your business goals and revenue?
PR and SEO sometimes sit at the top of the funnel, so this can be tricky to answer.
Start with these metrics to prove demand:
Referral traffic
Assisted conversions
Branded search lift
Track your referral traffic to show the number of visitors who visit your site directly from media coverage.
Even if numbers are low, they’ll tell you which topics make your audience want to know more about you. Then you can publish more on those in the future.
Tracking assisted conversions shows you conversions where organic search or referral traffic appeared somewhere in the buyer’s journey, but not necessarily as the last click.
PR and SEO content may not convert on the first visit, but it still influences the buyer’s journey.
This metric captures that concept.
Find this in GA4 under Advertising > Key event attribution paths, and switch to “Source/Medium” to see which specific outlets have the most impact.
As AI search has decreased click-through rates, branded search queries have become one of the clearest signals that your PR and SEO efforts are building real awareness.
It’s a metric Britt prioritizes for exactly this reason:
I track branded search lift because it’s a sign that coverage or visibility made someone curious enough to go look up the company by name. That matters to me because not every asset will result in direct clicks.
The metric is also important to Rola:
Branded search lift connects awareness and intent, showing how media exposure actually drives people to seek out your brand.
Google Search Console tells you how often people search for your brand by name and how many of those searches result in a click to your site.
Look for spikes around major coverage dates to directly tie increases to your PR and SEO efforts.
Turn PR and SEO Into an Always-On Authority Engine
The brands earning the most trust right now aren’t doing it with PR or SEO in siloes.
They’re showing up consistently across media, blogs, review sites, search engines, and AI because all of those channels feed the same authority signals.
That takes more than a “quick sync” before campaigns. It takes an always-on partnership.
You don’t need to overhaul everything at once.
Start small:
Co-create one high-impact asset (and keep AEO best practices in mind)
Merge your source lists
Plan 3 pitches using our PR and SEO Joint Outreach Strategy Template
When you’re ready to go deeper on how to optimize your brand’s presence in AI, check out our complete guide to AI optimization.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-26 20:56:062026-05-26 20:56:06PR and SEO: How to Build More Authority Together (5 Steps)
AI has become part of nearly every industry, integrated into apps, company processes, and everyday life. As someone who’s been doing local SEO since it became a thing, I’m seeing a major shift in how people search and the answers they get.
In the good old days, the average local business could rank well by optimizing its website, optimizing its Google Business Profile, building about 50 citations, and asking for reviews. In an AI search world, those activities are table stakes.
To perform well in AI-powered local search, you also need to shape what the broader web says about your business, or, in other words, how well-known your brand is.
Think of local search as a digital “word-of-mouth” system.
What are people saying about your brand?
Are you mentioned in publications, blogs, or industry sites?
Do people talk about you on social media?
What sentiment exists around your business beyond your website and GBP?
These are the questions AI systems ask when users request local business recommendations. Here’s how to shape the reputation signals AI search engines rely on.
How to do competitor research for AI visibility
One of the first steps in an AI search strategy is identifying which brands LLMs recommend most often and finding out what they’re doing.
Identify which businesses get mentioned most in AI responses
AI responses change constantly, so you need to run the same query multiple times to study patterns.
Run your most common brand searches at least 20 times in your preferred LLM. You can do this manually or use software like Gumshoe or Waikay. These tools run synthetic prompts based on your business details and show how often you appear.
Identify the sites that AI most often cites
After identifying your competitors, look at the sources LLMs use. You can dig through the results manually or use one of the tools mentioned above.
Get your brand mentioned on those sites
Once you have that list of sites, try to get your brand mentioned on them.
If AI systems cite blogs, offer to contribute expert content. If they mention podcasts or YouTube channels, ask to be a guest. The goal is to amplify your brand.
Your customers search everywhere. Make sure your brand shows up.
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How to build reviews for AI
Since Google has been the primary discovery channel for the past decade, most businesses have focused only on getting reviews on Google. To perform well in AI results, you also need reviews on other sites.
Diversify your review strategy
Ask for reviews on a wide range of sites: Yelp, BBB, Facebook, and other review sites prominent in your industry. Frequent reviews across diverse platforms increase your brand’s visibility and can also help rankings in traditional search results.
Optimize the way you ask for reviews
Don’t ask for generic reviews. Give customers direction. Guide them toward experiences or product qualities AI searchers may ask about.
For example, if you have a plumbing company, your review request might sound like this:
Hi [Name],
Thank you for trusting us with your hot water tank repair. If you have a moment, could you please leave us a review on [Link to Platform] and tell us how we did? Some things you could mention in your reviews:
— What plumbing issue did we help you with?
— Are you happy with the quality of our service?
— Did your plumber arrive on time and have a professional attitude?
— Do you think the cost matches the quality of the service?
Your review is a big help to us and to others looking for a quality plumber.
Thank you!
[Name]
AI systems directly cite review content, so you want to make sure you’re getting detailed reviews.
Respond to all reviews
If you aren’t responding to reviews, start now. AI systems read and consider the content in review responses.
Be everywhere
AI systems often scour the web for even obscure mentions of your business and use them to build responses. Your business should be present and active across platforms, including:
YouTube.
Reddit.
Industry forums.
Social media, especially LinkedIn.
Industry publications.
Local and hyperlocal blogs.
Local news sites.
Local and industry podcasts and video channels.
Best-of lists in your city or industry.
Press releases.
Be active on the platforms your peers and customers use. A tool like Sparktoro can show where your audience is active so you can focus your efforts there.
One of Petrovic’s key takeaways is that Google prefers sentences that are semantically close to the query and early on the page.
Get straight to the point
While humans might appreciate a well-written introduction that provides context, LLMs scan pages for answers to specific questions.
Because AI systems often scan content higher up on the page, present your key points in the first paragraph. Then make sure the rest of the page supports them.
Understand what questions to answer
This goes back to keyword research and query fan-out. Identify what people type into the search bar, or AI bar, to find businesses like yours. Your website needs to become an answer engine for those prompts.
For local businesses, these are the must-answer questions:
What do you do?
What products or services do you offer?
Who are your products or services for?
What problems do you solve?
Where are you located?
What neighborhoods or cities do you serve?
Do you offer on-site services, or do customers need to visit your location?
What are your business hours?
Do you offer emergency or same-day services?
Do you work weekends or holidays?
How can customers contact you?
What’s the booking process?
Do you offer quotes or consultations?
Is your business appointment-only, or do you accept walk-ins?
Why should someone choose your business?
What sets you apart from competitors?
Do you have awards or certifications?
Are you best known for a specific product or service?
How much do your products or services cost?
Do you offer discounts or packages?
What do customers say about you?
Can you display reviews and testimonials?
Can you show case studies or before-and-after examples?
What are the answers to your most frequently asked questions?
How do you demonstrate authority and expertise?
What does your work process look like?
Do you educate people in your field through tips, guides, or blog articles?
AlsoAsked is a great tool for expanding this question-generation process.
Once you answer these questions, you can use a free tool like Qforia to do query fan-out and generate additional questions AI systems may ask in relation to users’ initial searches.
Answer these questions on your website. Then make sure your answers stay consistent across brand mentions on the web, including citations, guest articles, and press releases.
Structure your content in a machine-friendly way
Most local businesses describe their services like this: “Services we provide: plumbing, drain cleaning, pipe replacement, etc.”
You should do a better job of helping machines understand your business in a clear and concise way by using semantic triples.
A semantic triple consists of:
[Subject] + [predicate] + [object]
The subject is what you’re defining. The predicate describes the subject’s relationship to the object. The object is what defines the subject.
For example:
[Rescue Plumbing] [is] [a plumbing company in Denver].
Drop the “we” and replace it with your brand name. Machines still need clear signals, so you need to explain what your business is and what it does as clearly as possible.
Have something new to say
Information gain is essential for AI search. Your content shouldn’t reiterate existing information. It should contribute something new.
LLMs want content that enriches their knowledge about your brand, your industry, and your location.
Draw on your personal and professional experience. Answer questions that haven’t been addressed in your industry. Describe on-the-job experiences only you can speak to. This is your opportunity to surface for AI searches your competitors don’t appear in.
See the complete picture of your search visibility.
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Your AI visibility to-do list
AI visibility depends on more than your website and Google Business Profile. Use this checklist to strengthen the reviews, citations, content, and brand signals AI systems rely on.
Shift your local SEO strategy. Optimize and maintain your website and Google Business Profile while cultivating broader brand visibility across the web.
Identify your competitors and study their content and citation strategies.
Identify the sources LLMs cite in relation to your industry and location, and get your brand mentioned in them.
Diversify the sites where you collect reviews, optimize your review requests, and respond to all reviews.
Build your presence across blogs, social media, forums, YouTube channels, podcasts, and the press.
Write unique, informative, and comprehensive content on your website, citations, and brand mentions across the web. Structure key information using semantic triples.
There’s much more I could write about optimizing for localized AI search, but I’ve probably already exhausted your attention span, so stay tuned for the next article.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-26 12:00:002026-05-26 12:00:00The new playbook for localized AI search optimization
AI lead generation works best as a system, not a collection of separate tools. The three core layers are data, activation, and optimization.
Traditional lead gen breaks at scale because teams fragment strategy across locations, operate in silos, and rely on manual budget decisions.
Local search carries the highest purchase intent in digital marketing. Most multi-location brands are losing those searches due to inconsistent listings and weak profiles.
AI improves lead quality, not just volume. Lead-to-close rate by location is the metric that actually matters.
You don’t need a full overhaul to start. A focused 30-day rollout can produce measurable pipeline impact.
Multi-location brands are generating more leads than ever. And yet, many are still struggling to turn that activity into consistent revenue across every market they serve.
Here’s the real problem: traditional lead gen was never built for scale. It was built for one team, one market, one campaign at a time. The moment you’re managing dozens or hundreds of locations, that model cracks. Fragmentation sets in. Quality drops. And the manual work required to hold it all together eats your team alive.
AI lead generation changes the equation entirely, but only if you use it the right way. This isn’t about automating what you’re already doing. It’s about building a system that gets smarter across every location, every market, every campaign, at the same time.
This article lays out how to actually do that.
Why Traditional Lead Gen Breaks at Scale
Multi-location lead gen has three structural failure points. Once you can see them clearly, the solution becomes obvious.
Fragmentation. Different teams run different playbooks in different markets. There’s no shared learning system, no central source of truth, and no way to know why your top location outperforms your worst one. According to NP Digital survey data, only 16 percent of multi-location businesses report “very consistent” lead quality across their locations. The majority fall somewhere between “significant variation” and “highly inconsistent.”
Inconsistent quality. High lead volume in one region doesn’t translate to high revenue. The locations that look like top performers by lead count often rank near the bottom by close rate. Without visibility into lead quality at the location level, you’re optimizing for the wrong thing.
Manual optimization that can’t keep pace. Most teams still allocate budget manually, review performance monthly, and build campaigns market by market. That cadence worked when the scale was manageable. At 50 or 100 locations, it’s a liability. Budget decisions made quarterly can’t respond to demand signals that shift weekly.
Buyers make it harder, too. By the time someone contacts your business, they’ve already researched you using search, reviews, and word of mouth. 98 percent of consumers verify an AI-recommended brand before buying, and about 65 percent of Google searches now end without a click to any website. Your presence has to be consistent, accurate, and compelling long before a lead form ever gets filled out.
The old model is broken. The fix isn’t more campaigns. It’s a better system.
The AI-Powered Lead Gen Framework
The brands scaling successfully with AI for lead generation aren’t just using more tools. They’re using tools that connect.
Most companies have pieces of the puzzle. The problem is those pieces don’t talk to each other. Paid media AI can’t access your lead scoring data, so you optimize for clicks that don’t convert. Local listing data lives in a separate system, so top-performing locations can’t surface insights to underperformers. Performance data stays siloed in individual markets and never informs the broader strategy.
The AI-powered lead gen framework has three layers:
Data Layer: Location data, CRM signals, and customer behavior. This is the foundation. If your data is fragmented or inconsistent, everything built on top of it will be, too.
Activation Layer: Ads, SEO, social, and local listings. These are your channels. The goal is to run them from a centralized playbook while adapting execution to each market’s demand signals.
Optimization Layer: AI testing, budget allocation, and personalization. This is where the system learns. It improves not just individual campaigns, but the entire operation simultaneously.
The key distinction is centralized strategy with localized execution. Brand messaging, campaign frameworks, and budget guardrails are set at the top. Creative, offers, and targeting adapt to each market’s specific signals. AI models are trained on the full dataset, not just one region, so outputs are informed by what’s actually working across your entire footprint.
This is how you stop duplicating the same campaign across 50 markets and start building something that compounds. Scale doesn’t come from more campaigns. It comes from smarter systems,
AI and Local Search: Capturing High-Intent Demand at Scale
Your next customer isn’t searching for your brand name. They’re searching “near me.” And that intent matters enormously.
“Near me” searches carry some of the highest purchase intent in all of digital marketing. The problem is that most multi-location brands lose those searches before they ever have a chance to convert. The culprits are predictable: inconsistent Google Business Profiles, weak local SEO signals, and no coherent review strategy.
NP Digital’s research found that 59 percent of multi-location businesses are not tracking their Map Pack visibility at all. You can’t optimize what you don’t measure, and you can’t win local search if you’re not paying attention to it.
AI addresses each of these gaps directly.
Automated listing optimization keeps your business information accurate and consistent across every platform and every location simultaneously. Name, address, and phone number (NAP) inconsistency is one of the most common reasons brands lose local rankings. AI can audit and sync that data at a scale no manual process can match.
AI-generated localized content means each location gets landing pages, service descriptions, and posts that reflect its specific market, without requiring a dedicated content team for every region. Add schema markup so search engines and AI tools can surface your location data in map features and AI-generated answers.
Review sentiment analysis lets you monitor feedback across every location and flag negative trends early, before they compound into a visibility or reputation problem.
The metrics that matter at the location level: local visibility share, calls and direction requests, and location-level conversion rates. Track these per location, not just in aggregate, and the gaps in your strategy become obvious fast.
Scaling Paid Media Across Locations Without Wasting Budget
Manually managing paid ads across 100+ locations is where growth breaks.
Budget gets spread evenly across markets regardless of demand. Creative runs until someone manually pulls it. Performance gets reviewed monthly, by which point underperforming campaigns have already wasted weeks of spend. No one is learning what actually works in each market, because the data stays local.
AI fixes all three. Here’s how it works in practice:
Performance Max runs across Search, Display, YouTube, Maps, and Discovery from a single campaign structure. Rather than building separate campaigns for each location, you set the inputs and let AI distribute across channels based on where demand is showing up.
Dynamic creative optimization means AI is testing headline, image, and call-to-action combinations by market automatically. Creative adapts to what resonates locally, rather than running a single approved version everywhere.
Demand-based budget reallocation is the biggest unlock. NP Digital’s research shows that only seven percent of multi-location businesses use AI or automation to guide budget allocation. The majority allocate manually or based on historical performance. That means most brands are treating their best markets the same as their worst ones.
AI shifts spend toward the locations showing real-time opportunity signals. Same total budget, redistributed by what’s actually working right now. The result: the same dollar goes further because it’s going where it’s most likely to convert.
For more on building a paid strategy that generates more leads without inflating spend, this post breaks down the fundamentals.
Personalization Across Markets: Why One Message Doesn’t Fit All
Customers in Phoenix don’t behave like customers in New York. Generic messaging across locations produces low engagement and lower conversion rates.
NP Digital’s Personalization Maturity by Location data tells the story: 62 percent of multi-location brands are still “mostly standardized” in how they reach customers across markets. Only three percent are fully customized per location. The gap between standardized and partially customized is where most of the conversion lift is hiding.
AI enables three things that manual personalization can’t deliver at scale:
Location-based messaging adjusts the content, offers, and tone of your campaigns based on where a user is and what that market’s demand signals look like. A promotion that converts in one region might be irrelevant in another. AI can surface those distinctions without a marketer manually monitoring every market.
Behavioral personalization goes further. Rather than one-size-fits-all follow-up sequences, AI can trigger personalized responses based on how a specific lead has interacted with your content. The follow-up feels timely and relevant because it is.
Localized ad creative adapts headlines, images, and calls-to-action by market automatically. What works in a competitive urban market is often different from what converts in a suburban or rural one.
Each location also needs its own landing page with unique copy, local reviews, and the specific services offered there. Region-specific pages aren’t just an SEO play. They’re what closes the gap between click and conversion.
Relevance drives conversion. AI delivers relevance at scale.
Lead Quality Over Lead Volume: What AI Actually Optimizes For
More leads does not mean more revenue, especially across locations where quality varies wildly by region.
The metric most multi-location teams are missing is lead-to-close rate by location. It tells you which markets actually convert customers, not just which ones fill the top of the funnel. Without it, you’re optimizing for activity, not revenue.
NP Digital’s data shows that only 22 percent of companies can accurately track lead-to-close by location. Another 32 percent say they can’t do it at all. That means two-thirds of multi-location brands are flying blind on the metric that matters most for growth.
Three metrics separate volume from value:
Lead-to-close rate by location. Which markets are actually converting? This is the signal that tells you where to invest more and where to pull back.
Cost per qualified lead. Not cost per lead. Cost per lead that had a real chance of closing. The difference often reveals which channels are generating noise and which are generating pipeline.
Pipeline contribution. Which locations, channels, and campaigns are directly tied to revenue? This is the number that justifies more investment, and the one most teams can’t answer accurately.
AI addresses each of these through lead scoring models that evaluate more variables per lead than any human team can process manually, smart routing that gets the right lead to the right team within minutes based on location, service type, and availability, and predictive conversion optimization that improves over time as the system learns which signals actually predict a close.
For teams looking to build better systems for nurturing leads once they enter the funnel, that post covers the mechanics in detail.
The 30-Day AI Lead Gen Rollout Plan
You don’t need a full transformation to start seeing results. A focused, four-week rollout can produce measurable pipeline impact, and it gives your team a framework to build on.
Week 1: Audit location data and identify top performers. Pull all location data into a single view: listings, lead volume, close rates, and ad performance. Flag any locations with inconsistent or outdated NAP data. Rank locations by revenue contribution, and identify your top 10 percent and bottom 10 percent. The gap between them is your opportunity map.
Specifically: go into your Google Business Profile dashboard and note which locations are incomplete, missing photos, or haven’t had a review responded to in more than 30 days. That list becomes your Week 2 priority.
Week 2: Launch AI-driven campaigns and optimize listings. Launch Performance Max campaigns targeting your highest-opportunity locations first. At the same time, fully optimize Google Business Profiles across all locations, including photos, services, FAQs, and hours. Set up dynamic creative testing so ad variations can start adapting by market automatically. Fix the listing inconsistencies flagged in Week 1.
Week 3: Implement personalization and start lead scoring. Deploy location-based messaging on your top landing pages. Set up AI lead scoring to prioritize high-intent leads over raw form fills. Build region-specific landing pages for your highest-traffic markets. Automate lead routing so every inbound lead reaches the right team within minutes, not hours.
Week 4: Measure pipeline impact and reallocate budget. Pull lead-to-close rates by location and compare against your Week 1 baseline. Identify which campaigns and channels are driving qualified leads. Shift budget toward the markets and formats showing real pipeline contribution. Cut what isn’t working.
Small AI implementations compound quickly. The goal of this rollout isn’t to solve everything at once. It’s to build a feedback loop that makes your system smarter every week.
For teams that want to layer in automation across the nurturing side of the funnel, lead nurture automation is worth reading before you get into Week 3.
FAQs
How to use AI for lead generation?
Start with the data layer: consolidate your location data, CRM signals, and customer behavior into a unified view. From there, activate AI across your paid campaigns, local listings, and content. Use the optimization layer, AI testing, budget reallocation, and personalization, to improve performance across all channels simultaneously rather than one at a time.
How does AI lead generation work?
AI lead generation uses machine learning to identify high-intent prospects, score and route leads based on conversion likelihood, personalize outreach by market, and reallocate budget toward the channels and locations showing the best performance in real time. The key is building a system where these tools share data, rather than operating in separate silos.
How can AI agents boost lead generation and sales?
AI agents can handle the repetitive, data-intensive work that slows human teams down: monitoring listing consistency, running creative tests across hundreds of markets, scoring inbound leads, and routing them to the right sales rep within minutes. That speed and precision at scale is what produces conversion lift.
Conclusion
The brands that win won’t just generate more leads. They’ll generate better ones, faster, and across every market they serve.
Multi-location complexity is only going to grow. New locations, new markets, more channels, more data. The gap between brands that build AI systems now and those that wait will widen quickly. The difference between a system that scales and one that fragments under pressure isn’t budget; it’s infrastructure.
Start with the audit. Build the connective tissue between your data, activation, and optimization layers. And measure at the location level, because that’s where the real signal lives.
If you want support building out that system, NP Digital’s consulting team works with multi-location brands on exactly this. If you want deeper insights on this topic, check out the full webinar as well.
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Google also announced new search agents, including information agents and new agentic capabilities within Google Search. The information agent will continue to scan the web to find and monitor changes to your tasks and help you along your tasks journey.
“We’re entering the era of Search agents, where you can easily create, customize and manage multiple Al agents for your many tasks, right in Search,” Liz Reid, the head of Google Search said.
Information agents. The information agents will help you stay on top of your questions and tasks. Google said the agent will “intelligently look across everything on the web, like blogs, news sites and social posts, plus our freshest data, such as real-time info on finance, shopping and sports, to monitor for changes related to your specific question.”
The information agent will then send you “an intelligent, synthesized update, with the ability to take action.”
The example. Here is the example Google provided:
“So if you’re apartment hunting, you can brain dump all of the exact requirements you’re looking for, and your agent will continuously scan for you, notifying you when listings meet your needs. Or if you want to know the instant any of your favorite pro athletes announce a sneaker collab, your agent will let you know when a new drop lands so you don’t miss out.”
Availability. This will first roll out in the summer to Google Al Pro & Ultra subscribers.
Agentic experiences. Google is also expanding its agentic booking capabilities in Google Search to handle new tasks including things like local experiences and services. So if you want to find a place that has a private karaoke room for a specific time and night, that also serves specific food, you can use Google Search to book that place for you.
Google will pull together the latest pricing and availability with direct links for your to purchase it.
This works across home, repair, beauty or pet care and will roll out this summer in the U.S.
Personal intelligence expanding. Google also announced it is expanding Personal Intelligence in AI Mode to about 200 countries and territories and 98 languages.
Google is now letting searchers build their own apps directly into Google Search. This enables searchers to set up a search feature that delivers the information they need, in the format they want, from the sources they want.
Liz Reid, the head of Google Search, announced at Google I/O, “Search can build the ideal response, in the right format for your question – completely on the fly. So you can get custom generative Ul, including visual tools and simulations, tailored precisely to your needs.”
Examples. Here are a few examples of what you can code yourself into Google Search:
(1) Whether you want to wrap your mind around astrophysics or visualize how your watch works, Search can design custom layouts, assembling components (like interactive visuals, tables, graphs or simulations) in real-time, Google wrote.
(2) Ongoing tasks widgets, like planning a wedding or managing a home move. Search can go a step further, building you custom dashboards or trackers that you can continue to come back to and make progress on. You can think of these like mini apps for your own specific tasks, Liz Reid explained.
(3) Fitness tracker in Search, where you can ask Google Search to build you a custom fitness tracker. Search will code it for you, tapping into fresh, real-time sources including reviews, live maps and local data like the weather, so you get a tracker that works for you, helping you stay on track week after week.
What it looks like. Here are some examples of what this looks like in Google Seaerch.
Generative UI example:
Custom tracker example:
Availability. The generative Ul capabilities will be available for everyone in Search this summer, free of charge.
The custom experiences with Antigravity, like mini apps, right in Search in the coming months, starting first for Google Al Pro and Ultra subscribers in the U.S.
Why we care. Google Search will not just answer your questions but you can code your own mini apps within Google Search to give you the answers you want, in the format and style you want.
This is really a unique way for search and likely can only be done with generative-AI features and tooling.
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Google also announced some new agentic commerce features today in Google Search including Universal Cart, expanding Universal Commerce Protocol and Agent Payments Protocol (AP2).
Plus, Google’s Shopping Graph now contains 60 billion product listings, which is up from 50 billion from earlier this year, announced Vidhya Srinivasan, VP/GM Ads & Commerce.
Universal Cart. Google announced what it is calling the Universal Cart, where you can put products and items from multiple retailers into one single Google Universal Cart and check out on all those items with your Google Wallet with the click of a button.
As you are on Google Search, you can add items directly to your Google Universal Cart without having to go to a specific retailer’s website. This will work across Google Search, Gemini, YouTube and Gmail, so just keep throwing items in your cart – across Google interface and retailer and the cart will maintain your list.
Here is a screenshot of Universal Cart showing multiple retailers:
Google will find the best prices and deals, including which retailer has it in stock and let you check out with your preferred retailer.
Plus, Google said Universal Cart will “anticipate your needs and help solve problems before they.” Google’s example:
“Say you’re building your first custom PC and add a few parts from several retailers to your cart. Your cart will proactively flag any product incompatibilities and suggest alternatives. Since the cart was built on Google Wallet, it understands your payment method perks, loyalty information and merchant offers to help you choose. This lets you quickly find opportunities for hidden savings or points without having to remember them yourself.”
Merchants. Google listed a number of merchants that support this, including Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify merchants such as Fenty and Steve Madden.
Availability. This is available in Google Search and the Gemini app in the U.S. starting this summer and with YouTube and Gmail later on.
UCP and AP2. Google expanded the Universal Commerce Protocol on Google to Canada and Australia in the coming months and in the U.K. later on. UCP will also be coming to YouTube and more Google verticals including hotel booking and local food delivery.
Agent Payments Protocol (AP2) helps agents make payments for you, securely and with accountability, Google said. “Just tell your agent the specific brands and products you want and how much it can spend, and the agent only makes the purchase when your criteria are met,” Google explained.
Google will launch AP2 to Google products in the coming months, starting with Gemini Spark.
For a long time, SEOreporting revolved around dashboards. When a meeting was on your schedule, you’d spend your day preparing by exporting data from Google Search Console, cleaning it in spreadsheets, and layering charts into Data Studio.
Now, AI coding agents are changing that workflow. Instead of the manual work that would previously take hours, you can use tools like Claude Code to surface customized data with polished visuals in just minutes.
Here’s how to turn Google Search Console data into custom reports and speed up your reporting workflow.
What Claude Code can do with GSC data
Claude Code isn’t the same as using Claude in a browser tab. The standard Claude.ai interface works like a regular chatbot. Claude Code, on the other hand, is Anthropic’s terminal-based AI coding assistant.
It still feels conversational, but instead of living in a browser tab, it can interact directly with files, folders, spreadsheets, and scripts on your machine. It can read exported GSC CSV files, process large datasets locally, generate charts and summaries, analyze trends across pages and queries, and ultimately create structured deliverables from raw data.
Claude Code isn’t simply generating text responses like a chatbot. Instead, it’s creating a local reporting environment that behaves like a lightweight software project.
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There’s a learning curve
Before you can start building beautiful, custom reports, you’ll need to set up Claude Code. If you’re not an engineer or developer, this process can feel overwhelming at first. There is a learning curve, but don’t give up.
Setup is actually the most time-intensive piece of the process, but it’s a one-time process. Depending on your technical experience, the initial setup may take a couple of hours.
The “reports in minutes” concept really applies after the environment is configured. Once you’re past the initial setup and Claude is connected to GSC, you can run any custom SEO report you want in a matter of minutes.
If you’re in an enterprise environment, this setup process can go faster with a little help from the tech team. If you’re an agency or an SEO consultant, you can always lean on the expertise of in-house developers or engineers or an outside contractor.
Getting started
If you don’t already have one, create an account atClaude.ai. You can sign up with Google, email/password, or enterprise SSO.
Most SEOs using Claude Code for reporting have a paid plan or use Anthropic API access. But you can use a free plan at the time of writing.
Install Node.js
Claude Code runs locally on your machine, so you’ll first need Node.js installed. You can also use it on a Chromebook by activating the Linux subsystem.
For the purposes of this tutorial, I used a Mac.
Next, download the current LTS (Long-Term Support) version. Once installed, you’ll have access to npm, which is used to install Claude Code.
To verify the installation, open Terminal (Mac/Linux) or PowerShell (Windows) and run:
node -v npm -v
If both commands return version numbers, you’re ready to continue.
Install Claude Code
Next, install Claude Code globally:
npm install -g @anthropic-ai/claude-code
Once the installation finishes, start Claude Code by running:
claude
The CLI will walk you through authentication and connect to your Anthropic account. After that, Claude Code can work directly with local project folders containing exported SEO data, scripts, spreadsheets, and reporting templates.
At this point, you’ll be able to interact with Claude Code in the terminal using commands much like you would with an AI chatbot.
To kick off the workflow, I gave Claude a prompt:
“I have a marketing meeting coming up, and I want to show our performance from Google Search Console.”
One benefit is that Claude now becomes an onboarding assistant. Claude will ask a handful of clarifying questions to get started. For example, during the setup process, Claude asked:
Whether to use a service account or OAuth credentials to access the Google Search Console API.
Which reporting views or marketing priorities mattered most.
Where the reporting project should live locally on the machine.
Which Google Search Console property to connect to.
Claude also asked where the reporting project should live locally.
(As an aside, we prefer to store it inside a dedicated code directory rather than a standard Documents folder because development projects can sometimes run into file permission or syncing issues when stored inside cloud-synced folders like Documents or Desktop.)
Next, I established how the visuals will be built before connecting to GSC.
We like using Observable Framework, an open-source framework for building data apps, dashboards, and reports.
You don’t necessarily need to follow this exact structure; Claude Code is highly customizable, and you’ll settle into what works for you.
And remember: if you’re unsure about any next steps, you can just ask Claude, and it will help guide the setup.
Connecting to GSC
Before Claude Code can start generating reports from live GSC data, you’ll need to connect it to the Search Console API.
This is another technical part of the process, but the good news is that Claude can walk you through much of the setup interactively.
To establish the connection, you’ll need to create a Google Cloud Project (GCP) and configure API credentials.
That setup process typically includes:
Creating a Google Cloud project.
Enabling the Search Console API.
Generating OAuth credentials or API secrets.
Adding those credentials to a local environment file.
In larger organizations, your IT or development team may already manage this infrastructure.
If not, you can still configure it yourself using a standard Google account or Google Workspace account.
Generating reports
Once you’ve finished connecting to GSC, congratulations! You made it through the hardest part. Once setup is complete, your reporting process changes entirely.
You can now focus on the reporting views you want to create, such as:
“Show me the top 10 landing pages that gained traffic this month.”
“Create a chart of declining nonbrand queries over the last 90 days.”
“Compare CTR trends by device type.”
“Show me the top-performing pages from New York last month.”
Claude is now like an on-demand reporting assistant. You simply open the project folder, launch Claude Code, and ask for the charts you need.
In addition, you can be more dynamic in your meetings.
Instead of building a rigid dashboard ahead of time and hoping stakeholders ask predictable questions, you can generate new views dynamically as questions come up.
That means you can walk into a meeting, ask Claude for a completely new chart or segmentation, and generate it in minutes rather than rebuilding an entire dashboard manually.
Now let’s look at some reports you might quickly run before your next meeting.
Here’s an example of a custom SEO performance dashboard generated from Google Search Console data.
While some of these metrics are available inside GSC, building your own report gives you much more flexibility in how trends, comparisons, and supporting metrics are visualized together.
You could also generate a bar chart with YoY rankings, or a heat map of rankings for keywords by month. Both examples are below.
What we like to include in our reporting is a combination of scorecards, time-series charts, year-over-year bar chart comparisons, and heat maps that break down the key drivers behind a metric.
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Claude Code completely transforms SEO reporting
SEO reporting has always been a push and pull between speed and flexibility.
Dashboards are fast once they are built, but they are often rigid. Custom analysis is powerful but historically has been time-intensive.
Claude Code changes everything.
Now you can interact with your GSC data more dynamically, explore new questions as they arise, and create reporting views that would have previously taken hours to build manually.
Once the initial setup is complete, reporting becomes far more adaptable to the needs of you and your stakeholders.
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