DISCOVERY STACK FRAMEWORK
Discovery Stack is a framework for B2B companies building durable discovery in the AI era. It combines four pillars — Mentions, Earned, Platform-Specific, Consensus — with thirteen operational components delivered across a twelve-week implementation program. The framework names what classical SEO no longer covers: the architecture of signals AI systems actually use when they choose which brands to cite.
CLASSICAL SEO HAS STOPPED BEING ENOUGH. THE EVIDENCE.
OVERLAP
Between Google Top 10 and AI citations in 2026. A year earlier the same measure stood at 76%. (Ahrefs, 863K queries, 2026)
B2B BUYER JOURNEYS
Now include at least one touchpoint inside a language model — ChatGPT, Perplexity, Claude, or Google AI Mode. (McKinsey, 2026)
AI CITATIONS
Originate from earned media — third-party publications, communities, review platforms — not from company-owned blogs. (PRNews, 2026)
BACKLINK PREDICTIVE POWER
That is the share of AI citation behavior explained by backlinks alone. (DigitalApplied, sample of 5K prompts, 2026)
Conclusion: the classical SEO playbook — technical hygiene plus content plus link acquisition — is no longer sufficient. Discovery in the AI era requires a rebuild of the stack, not the optimization of a single channel. The four pillars below describe what that rebuild looks like.
THE FOUR PILLARS OF DISCOVERY STACK
The four pillars are not channels. They are categories of signal that AI systems weight when they decide which brands belong inside an answer. Each pillar carries its own measurement logic, its own components, and its own failure mode when ignored. A company strong on one pillar but blank on another will still struggle to appear in AI answers.
MENTIONS
Mentions outperform backlinks.
Brand mentions — references to a company in independent sources, with or without a link — correlate with AI citability roughly three times more strongly than classical backlinks. The PR layer now has measurable value for AI visibility, a value that legacy SEO metrics simply do not see. A press piece without a link, a podcast appearance with no follow-up URL, a Reddit thread that names the brand: each contributes to the signal AI uses to decide whether a company belongs in a category answer. Backlinks remain useful as a secondary indicator, but they have moved from primary driver to one input among several.
→ We monitor mentions cross-source — media, RSS, podcasts, YouTube, communities — segmented by sentiment and source type.
EARNED
85% of AI citations come from earned media.
Company blogs are almost invisible to AI when measured at scale. The citations that AI systems surface come from authoritative industry publications, Reddit and Quora threads, LinkedIn posts, Wikipedia entries, and review platforms like G2 and Capterra. The hierarchy of preferred sources differs per AI surface — ChatGPT leans on Bing's index and Wikipedia, Perplexity pulls heavily from Reddit, Claude looks for cross-platform consensus. What unites them is a clear preference for third-party validation over owned channels. Publishing into your own blog in isolation is, for AI discovery purposes, close to publishing into a void.
→ We build presence in earned media rather than publishing into the void on a company blog.
PLATFORM-SPECIFIC
A single AI optimization does not exist.
ChatGPT pulls roughly 87% of its citations from the top of Bing's index, with Wikipedia appearing in 47.9% of its answers. Perplexity weights Reddit at about 24% and rewards content freshness — articles updated in the last ninety days appear at a meaningfully higher rate. Google AI Mode leans on classical SEO ranking signals. Claude searches for consensus across multiple independent sources before it commits to a citation. Each surface treats authority differently, sources content differently, and rewards different formats. Treating "AI optimization" as one workstream collapses these distinctions and produces work that pleases no surface fully.
→ We adapt content and positioning to the algorithmic preferences of each major AI surface separately.
CONSENSUS
AI cites brands when it sees consistency.
Language models do not cite a brand on the strength of a single source. They search for consensus — the same positioning, the same category framing, the same core claim appearing across multiple independent platforms. A brand that describes itself one way on G2, another on LinkedIn, a third on its own site, and a fourth in industry media will fail the consensus check. The model has no stable signal to anchor to, so it picks a competitor with cleaner alignment. Consensus is not about repeating a tagline. It is about the same underlying claim being readable across every surface where the brand appears.
→ We audit cross-platform positioning consistency and align the core statement everywhere the brand appears.
THIRTEEN COMPONENTS DELIVERED IN TWELVE WEEKS
The framework names what and why. The components name what we actually build. Each block maps to a module inside the twelve-week Execution program. The list describes operational outcomes — not the procedure used to produce them. Procedure stays inside the engagement.
↳ EACH COMPONENT IS A SEPARATE MODULE INSIDE THE EXECUTION PROGRAM. OPERATIONAL DETAIL — ONLY DURING IMPLEMENTATION.
WHAT PEOPLE ASK BEFORE WE START A CONVERSATION
Q · 01How is Discovery Stack different from SEO?
SEO is optimization for a search engine. Discovery Stack is optimization for the entire discovery layer in 2026 — in which the search engine is one of four pillars, alongside Mentions, Earned, and Consensus. SEO remains a foundation inside the stack as part of the content engine component, but it sells less on its own each year. Overlap between Google Top 10 and AI citations sat at 38% in 2026, down from 76% the year before. Discovery Stack does not replace SEO. It places SEO inside a larger system that reflects how buyers actually research now.
Q · 02How is Discovery Stack different from GEO?
GEO — Generative Engine Optimization — focuses narrowly on citability inside AI surfaces. Discovery Stack combines GEO with three additional pillars: Mentions, Earned media, and Consensus signals. GEO is one of the pillars inside Discovery Stack, not the whole framework. A company that wins on GEO but fails on consensus will still see its AI citations decay as soon as positioning across other surfaces drifts. The framework treats AI citability as one output of a coordinated system, not as the system itself.
Q · 03When do first results appear?
First AI citations typically emerge eight to twelve weeks after the content engine, Authority Graph, and LinkedIn rhythm are in place. Branded search grows more slowly — first clear movement appears at four to six months of consistent presence. Discovery Stack is not a fast hack. It is a system designed for the next five to ten years of the discovery layer. Companies expecting thirty-day results from a system of this kind are signalling a different problem — usually a product or positioning issue that no discovery work will fix.
Q · 04Does this work for every B2B category?
It works wherever the buyer researches before deciding — which covers nearly all B2B. The clearest effects appear in SaaS, professional services, and technology products with research-heavy buying cycles. B2C e-commerce works differently because marketplace platforms absorb most of the discovery layer; Discovery Stack is designed for B2B. Categories with very short purchase cycles or impulse buying are not the right fit. Categories with long, considered, multi-stakeholder buying are where the framework shows its largest effect.
Q · 05How much does implementation cost?
Three tiers. Diagnosis is an entry product — a two-page analysis of where the company stands today in AI answers, delivered inside one to two weeks, starting at $3,500 USD. Execution is the full twelve-week implementation program, starting at $40,000 USD, capped at three companies per year as an operational constraint, not a marketing scarcity tactic. Reporting is a monthly retainer for ongoing measurement and quarterly recalibration, starting at $3,500 USD per month. Exact scope is set during a qualification call before either side commits.
FRAMEWORK UNDERSTOOD. WHAT COMES NEXT?
Reading the framework is the easy part. Knowing where your company sits inside it — which pillars are strong, which are blank, which surfaces cite you and which never will — that is the work. The Diagnosis is the entry point. The Execution program is the implementation.