Get into the corpora frontier models learn from. Systematic placement across Wikipedia, Common Crawl, tier-1 publishers, Reddit, and GitHub — engineered for durable presence in the training data.
Every frontier model trains on a measurable subset of the internet — Common Crawl, Wikipedia, Reddit, Stack Overflow, GitHub, arXiv, and a short list of high-authority publishers. If your brand is absent from this corpus, it is structurally absent from the model. LLM Citation & Training Visibility is the discipline of engineering your way in.
Systematic placement across the high-authority sources frontier models demonstrably train on. Wikipedia notability work, Common Crawl-indexed publisher placements, Reddit and Stack Overflow narrative work, GitHub and arXiv contributions where category-appropriate.
Model training cycles ingest a finite, measurable corpus. A citation in Wikipedia compounds for years. A citation in a paywalled trade publication compounds for nobody. Most marketing budgets aim at the wrong sources because the discipline is new.
This is not link building. The mechanics are different, the sources are different, the half-life is different, and the measurement is different. We treat training visibility as its own practice with its own playbook.
Per-source visibility scan: Wikipedia, Wikidata, Common Crawl publishers, Reddit, Stack Overflow, GitHub, arXiv. Where you are, where competitors are, where the gap is.
Earned coverage, source-citation architecture, claim consistency. The single highest-leverage corpus surface.
Editorial coverage in the trade and national publications that land in training corpora. Real journalism, not paid placement.
Done seriously and with rigor — content participation in category subreddits with vocabulary alignment, never spam.
GitHub, HuggingFace, arXiv where category-appropriate. Authority that compounds in technical-intent training data.
Multi-source distribution of original research to maximize corpus penetration of each piece.
Map current corpus presence across 6 major training sources. Quantify gaps vs. competitors AI cites most.
Score each source by leverage coefficient. Rank candidate placements by expected citation-share lift.
Editorial outreach, Wikipedia work, narrative seeding, open-source contributions. Weekly cadence.
Track citation propagation across AI platforms in the 6–18 months following placement.
Medians across recent engagements. Outcomes vary by category, baseline, and engagement scope. Diagnostic projections are available on request during a scoping call.
Every engagement opens with a 10-day diagnostic. We score you across the 5 Layers and recommend the exact LCV roadmap for your category.