FORKOFF LLM SEO is a corpus-engineering service that grounds AI and Web3 founders inside the source graph the four major LLMs ingest from. Wikidata entity ID, Wikipedia disambiguation, GitHub seeding, arxiv cross-linking, Reddit AMA cycles, and a 12-surface corpus map decide whether the model can ground the brand entity at all before any citation work can land.
Five corpus-engineering gaps we audit before any citation work runs. Wikipedia, Wikidata, arxiv, GitHub, Reddit; the surfaces LLMs ground from before they can cite anything. Each row is the FORKOFF fix. Read it before you apply for the engagement.
No Wikipedia article on the brand or category. No Wikidata entity ID. LLMs cannot ground the brand-entity reliably, so the model substitutes whichever competitor has cleaner metadata when a buyer query lands. The single largest invisible gap in LLM SEO and almost no agency works it.
Wikipedia disambiguation page drafted with secondary-source citations. Wikidata entity ID requested with sameAs linkages to LinkedIn, Crunchbase, GitHub, and the founder profile. Knowledge-graph parity check on every deploy so the entity grounds before the corpus seeding ships.
Technical buyers ask Claude or ChatGPT about your category and the LLM grounds on GitHub READMEs, arxiv preprints, and Stack Overflow answers. Corpora the marketing team has never touched. The brand stays absent because the marketing site is not where the model sources from for technical queries.
GitHub READMEs and discussions seeded with canonical category language. arxiv preprint cross-linked to the owned site. Stack Overflow seeded answers under question patterns the LLM has indexed. Developer-corpus parity audited monthly.
Conversational LLMs weight Reddit threads, AMAs, and niche-forum discussions when buyers ask informal queries. A brand absent from r/<category> threads inherits whatever competitor seeded the discussion first. Most agencies treat Reddit as paid promotion and never seed organic signal.
AMA cycles on r/<category>, r/SaaS, r/<vertical>. Niche-forum participation under the founder profile, not anonymous PR accounts. Quote-mined founder content published on Reddit with canonical back to the owned site. Per-thread citation tracking.
The brand cites itself on its own blog and that is the entire corpus footprint. LLMs retrieve from a 12-source diversity map; if a brand has presence in only one domain, the model treats the citation as low-confidence and substitutes a competitor with broader source mix. Single-source authority is no longer enough.
12-source corpus map locked in week one (Wikipedia, Wikidata, arxiv, GitHub, Stack Overflow, Reddit, Medium, dev.to, HackerNoon, Substack, G2, Capterra). Source-diversity score calculated weekly; gap-filling sprint runs until brand presence holds across at least 8 of the 12 surfaces.
Competitor parasite content (Medium articles, dev.to posts, HackerNoon listicles) cites the brand more than the brand owned content does. LLMs treat the third-party canonical as authoritative, the brand becomes a footnote on its own category, and the retrieval graph compounds against the brand.
Reverse the asymmetry. Brand-authored parasite content (founder byline, not ghostwriter) shipped to the same hosts where competitors seed. Quarterly source-citation balance audit; rebalance triggers gap-fill sprint when competitor citations exceed owned-source citations on the priority query set.
The FORKOFF LLM SEO retainer runs on a single 90-day operating system. Source-map locked in week 2. Wikidata entity ID + Wikipedia + GitHub seeded by week 6. Reddit AMA cycles + parasite ladder running by week 8. Source-diversity holds above 8 of 12 surfaces by week 12.
AEO agencies engineer the citation surface that LLMs repeat. FORKOFF engineers the corpus they ground from in the first place. Wikipedia, Wikidata, arxiv, GitHub, Stack Overflow, Reddit are the sources LLMs trust most heavily on entity and category queries; a brand absent from those surfaces never reaches the citation surface at all. Cluster hub sits at Answer Engine Optimization; per-platform sharpening lives at ChatGPT SEO and Perplexity SEO.
12-domain corpus footprint mapped, gap list locked.
Brand entity grounded, technical corpus seeded.
Brand presence across 8+ of 12 corpus surfaces.
Source-diversity holds, drift recovered inside 7 days.
Real corpus grounding holds when four signals are live every week: Wikidata entity ID accepted, technical corpus seeded (arxiv + GitHub + Stack Overflow), conversational corpus seeded (Reddit + parasite ladder under founder byline), and source-diversity holding above 8 of 12 surfaces. Single-domain footprint or anonymous-PR Reddit accounts never count on the ledger.
01 WIKIDATA
Brand-entity grounded with Wikidata ID and Wikipedia disambiguation.
Wikidata entity ID requested with sameAs linkages to LinkedIn, Crunchbase, GitHub, founder profile. Wikipedia disambiguation drafted with secondary-source citations. Knowledge-graph parity audited on every deploy. The LLM grounds on the brand-entity reliably instead of substituting a competitor with cleaner metadata.
rule · Brand-entity grounded with Wikidata ID and Wikipedia disambiguation.
arxiv preprint cross-linked to the owned site. GitHub README seeded with category-canonical language and topic tags. Stack Overflow seeded answers under buyer-query patterns. Technical buyers asking Claude or ChatGPT find brand-grounded answers instead of competitor-grounded ones.
rule · arxiv, GitHub, and Stack Overflow seeded with brand-canonical language.
AMA cycles on r/<category>, r/SaaS, r/<vertical>. Founder-byline parasite content on Medium, dev.to, HackerNoon, Substack. Brand presence holds across at least 8 of the 12 corpus surfaces, not 1 or 2. Conversational LLMs ground on community-cited brand language.
rule · Reddit, niche forums, and parasite ladder seeded under founder profile.
12-source corpus map; target 8+ surfaces. Source-citation balance compares owned-source vs competitor-parasite citations on the priority query set. Wikipedia notability monitoring, GitHub deprecation tracking, Reddit moderator-turnover signals all logged. Drift recovery sprint inside 7 days when policy shifts.
rule · Source-diversity score recalculated weekly with competitor balance audit.
Three engagements across AI infra, B2B SaaS, and a Web3 protocol. LLM SEO retainers that landed Wikidata entity IDs, drafted Wikipedia articles, seeded GitHub + arxiv + Stack Overflow, and reported a weekly source-diversity score the founder could read in two minutes. Pair the retainer with AI Search Optimization for top-of-funnel coverage or AI SEO for the full-stack wrapper.
Source-diversity score on an AI infra brand after 90 days. From 2-source footprint to 9-source corpus saturation across the 12-surface map.
Days from Wikidata entity-ID request to acceptance on a B2B SaaS brand. Knowledge-graph grounding shipped before any retrieval-side work began.
Monthly retainer floor. 90-day minimum, capped at 5 engagements per quarter.
You keep the Wikidata entity ID, Wikipedia draft, GitHub seeds, parasite content, and the source-diversity ledger.
Two operator-grade routes plus DIY. Match the engagement to the layer you actually need: corpus engineering at the source surface (LLM SEO) vs citation engineering at the answer surface (AEO). LLM SEO is upstream; AEO is downstream.
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| Feature | FORKOFF LLM SEOCorpus engineering · 12-source map · Wikidata grounding | AEO citation agencyCitation surface only · query bench, no corpus seeding | DIY in-houseOwned blog only · single-source corpus footprint |
|---|---|---|---|
| Surface focus | Corpus graph first. Wikipedia, Wikidata, arxiv, GitHub, Stack Overflow, Reddit. Citation surface is downstream of the corpus, not the engagement focus | Citation surface only. Optimizes the structured-answer the LLM cites once it has already retrieved | Owned blog. Whichever the founder is comfortable writing this week |
| Wikipedia and Wikidata grounding | Wikipedia disambiguation drafted, Wikidata entity ID with sameAs linkages, knowledge-graph parity audited every deploy | Treated as out of scope; LLM grounds on whichever competitor has metadata | Never touched |
| Technical corpus (arxiv, GitHub, Stack Overflow) | arxiv preprint cross-linked, GitHub README seeded with category language, Stack Overflow answers under indexed query patterns | Out of scope for marketing-only agencies | Engineering team writes README; marketing never coordinates with technical-corpus surfaces |
| Conversational corpus (Reddit, niche forums) | Founder-profile AMAs on r/<category>, niche-forum participation, quote-mined founder content with brand byline | Treated as paid promotion only; no organic seeding | Avoided because the marketing team is not Reddit-native |
| Source-diversity score | 12-source corpus map, target 8+ surfaces, recalculated weekly with competitor balance audit | Measures citation count on the LLM, not source breadth at retrieval | Single-domain footprint (owned blog only) |
| Drift and policy resilience | Wikipedia notability watch, GitHub deprecation tracking, Reddit moderator-turnover monitoring; recovery sprint inside 7 days on platform-policy shifts | Pattern drift triggers a citation re-engineering cycle, not a corpus-policy review | Founder hopes nothing changes |
| Pricing model | $1,500 sandbox audit · retainer by application · outcome-priced milestones | Hourly retainer or per-citation pricing | Founder time, opportunity cost, no cap |
12-surface corpus footprint mapped: Wikipedia, Wikidata, arxiv, GitHub, Stack Overflow, Reddit, Medium, dev.to, HackerNoon, Substack, G2, Capterra. You get the source-diversity score, competitor balance audit, Wikidata + Wikipedia readiness assessment, and gap-fill plan in 5 business days. If FORKOFF cannot find actionable corpus gaps, the $1,500 gets refunded. Retainer kicks in by application after the audit lands.
LLM SEO is corpus engineering: optimizing the source surfaces the four major LLMs ingest from at training time and ground from at retrieval time. Wikipedia, Wikidata, arxiv, GitHub, Stack Overflow, Reddit, Medium, dev.to, HackerNoon, Substack, G2, Capterra. AEO (Answer Engine Optimization, /services/answer-engine-optimization) is citation engineering: optimizing the structured-answer the LLM cites once it has already retrieved. LLM SEO is upstream of AEO. The corpus must hold the brand entity before the citation surface can repeat it.
Wikidata entity ID, Wikipedia disambiguation, GitHub README seeding, arxiv cross-linking, Reddit AMA cycles, and the 12-surface parasite ladder shipped in the first 90 days. Weekly source-diversity ledger. Outcome-priced. Scaleable up or down at quarter end. Pair the retainer with AEO, GEO, or ChatGPT SEO depending on the LLM that wins your buyer.
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