From Rank Tracking to Discovery Control Plane

As LLMs compress reputation into answers and citations, the next generation of SEO platforms must govern how companies are understood — and make the web agent‑readable again.

Many of you have seen me writing about D3C as a buyer‑native model of how decisions happen: Discovery → Create Confidence → Commit.

Discovery is the moment a buyer realizes a problem is real and begins orienting around solution space. Create Confidence is the phase where they test claims, validate fit, and reduce risk using third‑party evidence and internal proof. Commit is the decision point where organizational risk is accepted and the purchase is operationalized.

If you take D3C seriously, you run into a hard truth:

Most of what we call “MarTech” is not designed to support it.

The current stack is seller‑centric by construction. It is built to manage outbound sequencing, lead capture, attribution, conversion tracking, and pipeline mechanics. That stack assumes a world where Discovery begins when a prospect lands on your site, where confidence is created through your assets and nurture, and where the handoff to sales is the moment that matters.

That is no longer how Discovery works.

Table of Contents

1. Discovery is migrating upstream into a synthesized reputation layer
2. What Discovery infrastructure actually is
3. Agent-readable websites are not a nice-to-have; they’re a dependency
4. WebMCP is a forcing function
5. The content has to change too
6. Who builds the Discovery control plane?
7. The SEO suites and visibility platforms (mid-market and prosumer)
8. The enterprise SEO and web-optimization platforms
9. What “stepping up” actually means
10. Agent Accessibility: rebuild the web surface
11. The punchline

Discovery is migrating upstream into a synthesized reputation layer

Discovery is migrating upstream into a synthesized reputation layer: LLM answers, analyst condensation, peer community interpretation, reviews, and public narrative fragments that get recomposed into “what is true” about your company before a buyer ever engages you.

This is not a philosophical change; you can see it in traffic patterns and conversion patterns. Ahrefs’ study of 3,000 sites found that 63% of websites receive at least some AI‑referred traffic, even if the average share is still small and concentrated among a few assistants. (ahrefs.com)

The more important implication is not the current volume; it is the mechanism.

LLMs are running word of mouth at scale. They compress diffuse third‑party signal into a single narrative surface, and the buyer increasingly treats that surface as “the first pass” on what to believe.

When Discovery becomes machine‑mediated, you cannot “campaign” your way out of being misunderstood. You cannot outspend an incoherent external truth. You cannot optimize attribution around clicks while your market perception is being decided by what the ecosystem says — and what the machines can reliably retrieve and cite.

This is why D3C implies a new stack.

Not a new tactic. Not a new channel. A new kind of infrastructure.

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What Discovery infrastructure actually is

If we treat Discovery as an engineering problem rather than a marketing metaphor, it starts to look like a distributed systems problem.

You have thousands of external nodes generating signal, with wildly inconsistent schemas and incentives. You have multiple synthesizers (analysts, communities, LLMs) performing lossy compression. You have buyers sampling the compressed output and forming priors. And you have an internal organization trying to influence the system without being able to “own” the system.

In that world, the winning company is the one that builds an explicit control plane for Discovery.

A control plane does not “do the work” of engagement; it governs the system that does. It provides observability, normalization, policy, and orchestration across a messy data plane.

If you accept that framing, then the first requirement is not content production.

The first requirement is observability: you need to know how the world is describing you — and how that description is changing — before you can intervene intelligently.

That implies a Discovery stack that can continuously ingest third‑party signal, resolve entities and narratives across sources, measure how AI systems are synthesizing you, detect drift, and route remediation to the right surface with the right artifact.

It also implies a feedback loop that looks more like engineering than marketing: regression tests for your narrative, nightly prompt harnesses, change detection, and incident response when your representation breaks.

The marketing world has nothing like this today. We have dashboards for clicks and impressions, but very little for “how is my company being reasoned about when the buyer asks the machine.”

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Agent-readable websites are not a nice-to-have; they’re a dependency

Once you accept that Discovery is being mediated by machines, you also have to accept a second truth:

We’ve spent 20 years building websites for the Googlebot. Now we’re going to have to rebuild them for agents.

Even when the HTML is crawlable, it is rarely optimized for machine extraction, citation, and grounding. It is full of layout noise, repeated UI fragments, ambiguous claims with no canonical backing, and pages that are designed to persuade rather than to be referenced.

The early evidence on “LLM‑specific” web conventions supports the idea that superficial hacks are not the answer. Search Engine Land tracked ten sites and found that implementing llms.txt rarely correlated with measurable improvement, and where improvements occurred they were attributed to other work. (searchengineland.com) Search Engine Journal summarized a broader dataset analysis that likewise found no clear relationship between llms.txt presence and citations, with low adoption overall. (searchenginejournal.com) OtterlyAI’s log‑based 90‑day experiment reported just 84 visits to /llms.txt out of 62.1K total AI bot hits in their sample. (otterly.ai)

The takeaway is not “use this file or that file.” The takeaway is that we need agent‑readable web surfaces that are intentionally designed for extraction and grounding: canonical definitions, stable URLs, explicit entity framing, machine‑friendly structure, and an architecture that minimizes ambiguity.

This is what I mean by Agent Accessibility: not accessibility as a compliance checklist, but accessibility as agent legibility — can an AI system reliably parse, quote, cite, and act on what your site says?

And this is where the DOM layer stops being “implementation detail” and becomes strategy.

The DOM layer is becoming the new battleground

In the old world, technical SEO was about crawlability, indexation, and performance.

In the agentic world, the DOM becomes a semantic contract. If your DOM is a swamp of div soup, duplicated fragments, and unclear hierarchy, agents will extract the wrong thing, fail to ground claims, misquote you, omit you, or route the buyer elsewhere.

So “fixing the DOM layer” isn’t dev hygiene. It is Discovery infrastructure.

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WebMCP is a forcing function

This is where Google’s emerging WebMCP conversation matters.

Chrome’s team has published an early preview of WebMCP, positioning it as a standard for exposing structured tools so AI agents can interact with a website more reliably and precisely — rather than trying to infer actions from raw DOM manipulation. (developer.chrome.com) The WebMCP proposal is explicitly about exposing “tools” — callable functions with schemas and descriptions — through browser surfaces like navigator.modelContext. (github.com)

One line in the WebMCP repository is quietly profound: web pages using WebMCP can be thought of as MCP servers implemented client‑side (tools exposed in the page, not just on the backend). (github.com)

This is not about “agents buying things for people” as a gimmick. It is about the web becoming programmable again — but this time for agents.

If Discovery is mediated by agents, then agent‑readable and agent‑operable websites become part of Discovery infrastructure, because they determine what is legible, what is retrievable, and what is actionable.

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The content has to change too

If Discovery is becoming machine‑mediated, the content that wins is not the content that persuades a human reader most emotionally. It is the content that is easiest for machines to retrieve, ground, and cite in a synthesized answer.

The published GEO/AEO case studies (even if you discount the most optimistic claims) converge on a consistent pattern: fact‑dense, structured, modular content anchored in authority performs better in AI‑mediated environments. Go Fish Digital describes this explicitly in a GEO case study, framing the work around how LLMs retrieve, ground, and cite content (including “cornerstone” assets and structured presentation), with reported gains over a three‑month period. (gofishdigital.com) Verkeer’s case study similarly frames LLM‑ready strategy around structured data, updating older pages, and machine‑readability, reporting a lift in ChatGPT referral traffic in their example. (verkeer.co)

The deeper implication is not “write differently.” It is that the content engine has to become part of the stack — instrumented, tested, and governed like software.

Which brings us to the real question.

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Who builds the Discovery control plane?

If D3C implies new Discovery infrastructure, the natural question becomes: who is structurally positioned to build it?

It will not be CRM vendors, because CRM begins too late and sees too little of the external ecosystem. It will not be marketing automation, because automation assumes the buyer has already opted in.

The player that wins is the one that already sits closest to the open‑web visibility substrate — the place where the “Googlebot era” was governed — and can credibly evolve from “SEO tooling” into “Discovery governance.”

So here’s the challenge I want to put on the table:

The SEO suites are the companies that need to step up and build this layer. Not as a feature. As infrastructure.

And many of them have already started shipping pieces of it:

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The SEO suites and visibility platforms (mid-market and prosumer)

These companies already have distribution, practitioner mindshare, and the day‑to‑day workflows where “Discovery governance” would naturally live:

• SE Ranking (including an MCP Server that connects AI assistants to live SEO data and project APIs). (seranking.com)
• Semrush (AI Visibility Toolkit, prompt‑level tracking, competitive monitoring for AI answers). (semrush.com)
• Ahrefs (AI visibility guidance + Brand Radar / prompt tracking direction). (ahrefs.com)
• Moz (surfacing AI Overviews presence inside keyword workflows, per their guidance). (linkedin.com)
• Similarweb (AI Brand Visibility and AI Search Intelligence positioning around mentions, prompts, citations, and sentiment). (similarweb.com)
• SISTRIX (AI Overviews tracking and AI/chatbot visibility direction in their product updates). (sistrix.com)
• Serpstat (LLM Brand Monitor positioning around how top models describe your brand). (serpstat.com)
• Ubersuggest (AI Search Visibility as a product module for tracking presence and changes over time). (ubersuggest.zendesk.com)
• Mangools (AI Search Watcher tracking brand mentions across AI search engines). (mangools.com)
• SpyFu (pushing into AI‑assisted workflows via GPT‑style integrations and AI features, per recent coverage). (techradar.com)

Some of these are “early.” Some are “real.” But the direction is obvious: the suite vendors are becoming AI‑visibility vendors because the surface area of Discovery moved.

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The enterprise SEO and web-optimization platforms

These companies already sell into the governance layer (workflows, approvals, enterprise reporting). That makes them natural candidates to become the control plane rather than “another dashboard”:

• Conductor (positioning itself around “Get found in AI search” and enterprise AEO workflows). (conductor.com)
• BrightEdge (AI Catalyst for unified visibility across AI engines and optimization guidance). (brightedge.com)
• seoClarity (AI Overviews tracking and AI Search Visibility reporting as a product surface). (seoclarity.net)

These platforms have a shot at becoming “Discovery governance” because they already live where large teams manage process, risk, and accountability — i.e., where “Create Confidence” actually gets operationalized.

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What “stepping up” actually means

If you are one of the platforms above, “stepping up” is not shipping a prompt tracker. It is building an explicit control plane.

At minimum, that means committing to four capabilities as first‑class product primitives:

1. Observability
   How are we represented across AI answers, citations, sources, sentiment, and narratives — over time?
2. Diagnosis
   Why are we represented that way? Which sources, entities, claims, and content formats are driving it?
3. Remediation
   What should we change — in content, technical structure, authority building, and distribution — and who owns each fix?
4. Verification
   How do we regression test our representation and catch narrative drift like an incident, not a surprise?

And then there’s the piece most “AI visibility” tooling still treats as an afterthought:

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Agent Accessibility: rebuild the web surface

If the web is becoming agentic, then the stack must eventually govern the website as both a human interface and a machine interface.

That means DOM legibility. It means canonical knowledge surfaces. It means structured grounding. And increasingly, it likely means preparing for standards like WebMCP — where the browser becomes the tool layer and sites expose capabilities explicitly instead of hoping agents interpret the DOM correctly. (developer.chrome.com)

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The punchline

Seller‑centric tools cannot solve buyer‑centric Discovery.

D3C demands a new stack, and Discovery is the layer that has changed most dramatically. If that’s true, then the next generation of “SEO platforms” will look less like rank trackers and more like Discovery infrastructure:

A control plane that measures, governs, and improves how your company is understood by machines before the buyer ever arrives.

And the companies that should build it are hiding in plain sight.

They are the SEO and visibility platforms we’ve all used to build websites for Googlebot.

Now they need to help rebuild them for agents.

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