
The 90-Day AI Visibility Roadmap I Run for Sub-DR-20 Sites
The 90-day AI visibility roadmap I run for sub-DR-20 sites: entity-graph baseline, five canonical pages, co-mention seeding, and a citation dashboard.
Why this matters
A 90-day roadmap for sub-DR-20 operators. Weeks 1-2 baseline, 3-6 ship the entity graph and first five canonical pages, 7-10 seed co-mention surfaces, 11-13 measure and refresh. The roadmap is the difference between a GEO intention and a GEO program.
In this cluster
Cluster context
This article sits inside AI Visibility Engineering.
Entity graphs, schema architecture, and citation mechanics for sub-DR-20 sites competing on AI citations, not SERP rank.
SEO optimizes for rank. Answer engines optimize for citation-worthiness. This cluster is the engineering playbook for the second game, sized for operators, not enterprise SEO teams.
Why ChatGPT Isn't Citing Your Site: 6 AEO Factors
ChatGPT and Perplexity skip most sites for six measurable reasons. The 6 AEO factors that decide which sources get cited, and how to fix each one.
Entity Optimization for Brands in AI Search
Rank is a single-page game. Entity coherence is the compounding game. How sub-DR-20 brands engineer a Person + Organization graph that AI search engines actually cite.
Schema.org for Answer Engines, the 40 Properties That Matter
A tactical guide to the Schema.org properties answer engines actually read. Which fields move citation decisions, which are noise, and how sub-DR-20 operators compress a full JSON-LD graph into the forty that matter.
A 90-day AI visibility program for sub-DR-20 operators runs in four fixed phases: baseline and audit (days 1-14), entity graph plus five canonical pages (days 15-42), co-mention seeding (days 43-70), and measurement plus refresh (days 71-90). The phases sequence in this order because each depends on the previous one.
Every post in this cluster has argued that a sub-DR-20 brand can compete for AI citations by engineering entity coherence, publishing original data, and seeding cross-brand co-mention density. This post is the roadmap that packages those arguments into a ninety-day program an operator can actually run. It is the closing post of the cluster, and it is the entry point to doing the work. The pillar this roadmap implements is Answer Engine Optimization Explained.
The phases are fixed in order because each depends on the previous. An operator who ships canonical pages before fixing entity-graph coherence earns citations that resolve to someone else’s canonical. An operator who seeds co-mentions before the canonical pages exist wastes the seeds. The sequence is the program.
§1, Phase 01: Baseline and audit (days 01-14)
Read the current state. Run the schema-linter on every active page. Export the sameAs graph across LinkedIn, GitHub, Medium, Dev.to, YouTube, Twitter. Pull citation counts per engine on the current top five posts. Identify the named concepts the brand will own for the rest of the year. The output of phase 01 is a single document listing the drift, the missing surfaces, and the concepts.
§2, Phase 02: Entity graph plus five canonical pages (days 15-42)
Ship the Person schema with sameAs and knowsAbout. Ship the Organization and SoftwareApplication schemas with cross-referenced @ids. Publish llms.txt with an x-updated header. Publish /.well-known/llms.json. Write five canonical pages on the chosen named concepts. This is the heaviest phase, and it is the phase most operators skip by accident because the work looks editorial when the weight is actually structural.
§3, Phase 03: Co-mention seeding (days 43-70)
The phase where the moat compounds. Guest on one podcast that names the entity and the topic in the same sentence. Publish a GitHub repo with a README that includes Person schema. Comment on two Hacker News threads where the entity is technically relevant. Ship cross-brand citations in both directions. Cross-post to Medium and Dev.to with canonical URLs pointing back to chudi.dev. These seeds take four to eight weeks to surface as AI citations.
§4, Phase 04: Measurement and refresh (days 71-90)
Four readouts. Citation count per engine. Named-concept citation attribution. Entity coherence pass rate. Cross-brand co-mention density. Against each, the first refresh pass, topic drift republishes, freshness bumps, any sameAs drift corrected. By day ninety, the scoreboard is live and the next ninety-day block is planned.
§5, Why does cadence discipline beat volume?
Five canonical pages shipped on schedule across the ninety days outperform twenty pages shipped in a burst. AI crawlers apply a spam heuristic to velocity spikes. A steady cadence reads as an active editorial source; a burst reads as automated content generation, which many crawlers discount or delay. The schedule in phase 02 is the schedule, not an aspiration.
§6, What does “complete” mean at day ninety?
The roadmap is completed at day ninety, not finished. The scoreboard continues. The refresh cadence continues. The entity graph stays in drift-watch. What changes at day ninety is that the operator has a measurable program, not an intention. The delta between intention and program is the entire value of the ninety days.
§7, Why this is the cluster’s closing post
Every other post in the cluster argues for one piece of the work. This one sequences the pieces. An operator who reads only this post gets the program. An operator who reads the whole cluster understands the reasoning behind each phase. Both are valid entry points.
Bridge
citability.dev is the instrumentation for phase 04 and the early-warning system for the decay patterns from the previous post. The roadmap is the thinking. The scorer is the scoreboard. Sub-DR-20 brands that win this game ship both at once.
· Frequently asked
FAQ
How long does it take to see AI citations from this roadmap?
Co-mention seeds take four to eight weeks to surface as AI citations, which is why seeding starts at day 43. Ninety days is the minimum window for a measurable citation lift; a shorter run measures noise.
Can I skip a phase or run the phases in a different order?
No. Each phase depends on the previous one. Canonical pages shipped before the entity graph is coherent earn citations that resolve to someone else's canonical, and co-mentions seeded before the canonical pages exist are wasted seeds. The sequence is the program.
Why only five canonical pages instead of publishing more?
Cadence discipline beats volume. AI crawlers apply a spam heuristic to velocity spikes, so a steady schedule reads as an active editorial source while a twenty-post burst reads as automated content generation and gets discounted or delayed.
What do I measure at day ninety?
Four readouts. Citation count per engine, named-concept citation attribution, entity coherence pass rate, and cross-brand co-mention density. The roadmap is completed at day ninety, not finished; the scoreboard and refresh cadence continue.
· Sources & further reading
Sources & Further Reading
Further reading
- Schema.org for Answer Engines, the 40 Properties That Matter /blog/schema-org-answer-engines-guide A tactical guide to the Schema.org properties answer engines actually read. Which fields move citation decisions, which are noise, and how sub-DR-20 operators compress a full JSON-LD graph into the forty that matter.
- Entity Optimization for Brands in AI Search /blog/entity-optimization-brands-ai-search Rank is a single-page game. Entity coherence is the compounding game. How sub-DR-20 brands engineer a Person + Organization graph that AI search engines actually cite.
- Originality Signals and Citation Patterns /blog/originality-signals-ai-citation-patterns AI engines deprioritize pages that look like everything else. The originality signals that move a post from the summary layer into the quote layer, and why recap content is the new thin content.
- Perplexity vs ChatGPT: Different Citation Rules /blog/perplexity-vs-chatgpt-citation-rules Perplexity quotes liberally. ChatGPT quotes selectively. The engine-level differences in citation behavior that change what a sub-DR-20 brand should optimize for, engine by engine.
- Why ChatGPT Isn't Citing Your Site: 6 AEO Factors /blog/aeo-answer-engine-optimization-explained ChatGPT and Perplexity skip most sites for six measurable reasons. The 6 AEO factors that decide which sources get cited, and how to fix each one.
Reading Path
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Contextual next reads
Why ChatGPT Isn't Citing Your Site: 6 AEO Factors
ChatGPT and Perplexity skip most sites for six measurable reasons. The 6 AEO factors that decide which sources get cited, and how to fix each one.
Entity Optimization for Brands in AI Search
Rank is a single-page game. Entity coherence is the compounding game. How sub-DR-20 brands engineer a Person + Organization graph that AI search engines actually cite.
Schema.org for Answer Engines, the 40 Properties That Matter
A tactical guide to the Schema.org properties answer engines actually read. Which fields move citation decisions, which are noise, and how sub-DR-20 operators compress a full JSON-LD graph into the forty that matter.
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