I models learn from a fixed set of high-authority sources. If your brand lives on those sources, models recall you even without searching the live web. That is the difference between being retrieved and being remembered. Corpus seeding puts your brand in the second group.
We identify the publications, databases, and communities that shape model training. Then we build a presence for your brand across them. The goal is simple: when a model answers from memory, your name is part of that memory.
Retrieval can be turned off. Training data cannot. We place your brand on the sources LLMs learn from, so your visibility survives every model update and every change in how AI tools search.
We map the sources that influence each major model. Wikipedia, industry databases, authoritative publications, and the communities models sample from.
We earn mentions and coverage on those sources through digital PR, data contributions, and expert content. Real placements, not paid links.
Training Data presence compounds. Every placement strengthens how models describe your brand, and that memory persists across model versions. Competitors who start later inherit the gap.
We test what models say about your brand with retrieval disabled. That shows what the model actually knows, not what it looks up.
We align how your brand is described across seeded sources, so models learn one clear story instead of conflicting fragments.
Rankings fluctuate. Retrieval rules change. But a brand embedded in training data stays recommended through all of it. Corpus seeding is the most durable form of AI visibility available.
It is the practice of placing your brand on the high-authority sources AI models learn from during training, so models recall your brand from parametric memory.
The tactics overlap, but the targeting is different. We choose placements based on their influence on model training data, not just referral traffic or domain rating.
Public training data research, model citation patterns, and our own recall testing. We keep this source map updated as models evolve.
Longer than retrieval-based work. Placements land in weeks, but models absorb new training data on their own update cycles. Treat this as a 6 to 12 month asset, not a quick fix.
No one can control model outputs directly. What we control is your presence on the sources models learn from, which is the strongest lever available.
Hundreds of brands are already getting recommended by AI. When will you?
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