Services / LCV

Get written into what AI knows.

Systematic placement across the high-authority corpora — Common Crawl, Wikipedia, Reddit, GitHub, and academic repositories — that language models learn from. You can't optimize what AI doesn't know. We make sure it knows.

AI can only cite what it was trained on.

Language models have a knowledge cutoff — but that cutoff is a crawl boundary, not a date. What's in the corpus matters more than when it was published. Brands that appear frequently in high-authority training sources get cited; those that don't, don't.

LCV is a systematic discipline: identifying the corpora that matter, mapping your current presence, and executing a multi-channel placement strategy that builds durable training-set visibility across model generations.

4+
high-authority corpora targeted per engagement
Multi-year
durability of corpus-level placement across model updates
10 days
to initial corpus presence audit and gap analysis
What we deliver

Six capabilities. One outcome: training-set presence.

01
Corpus Presence Audit
We map your current visibility across Common Crawl, Wikipedia, Wikidata, Reddit, GitHub, and academic databases. You see exactly where you exist and where you don't.
02
Wikipedia & Wikidata Strategy
Wikipedia is training data for every major LLM. We develop a compliant, substantive Wikipedia presence strategy — editorial quality, verifiability, and long-term maintenance.
03
High-Authority Editorial Placement
Contributed articles, cited quotes, and editorial mentions in sources that carry high corpus weight: industry publications, academic journals, and vetted news sources.
04
Reddit & Forum Strategy
Reddit data is in most LLM training sets. We develop a community presence strategy — genuine, rule-compliant, and designed to appear in category-relevant threads.
05
GitHub & Technical Corpus
For technical brands, GitHub presence and open-source contributions carry significant weight in models trained on developer data.
06
Syndication Network
Content syndicated across the right distribution channels increases the probability of corpus inclusion. We build the distribution strategy alongside the content.
Platforms & surfaces

Where training data lives.

Common Crawl
The largest open web crawl. Foundational training data for GPT, Claude, and most major LLMs.
Wikipedia
Highest-authority single source in most LLM training sets. Every model reads it.
Reddit
Included in OpenAI, Google, and Anthropic training datasets. High volume, high recency.
GitHub
Critical for technical brands. Code, READMEs, and issues appear in developer-focused models.
Related services

Build the full stack.

EKG
Entity Knowledge Graph
Build the structured entity profile that makes your Wikipedia and Wikidata entries authoritative.
GEO
Generative Engine Optimization
Translate corpus presence into active citation across deployed AI platforms.
BMA
Brand Mention Analytics
Monitor how citation presence changes across platforms week by week.
Get started

Ready to get written into what AI knows?

We start with a Corpus Presence Audit — a complete map of where you exist in the training data and where you don't.

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