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Signal Stack.

The combined set of digital signals that determine whether AI engines name a business in their answers.

Definition.

Signal stack is the term LawShift uses for the combined set of digital inputs that determine a business's AI visibility. It consists of five distinct layers, each addressing a different way AI search engines construct answers. The layers are multiplicative, not additive — a zero on any one layer dampens the contribution of every other layer.

The five layers.

  1. Citation engineering. The volume and authority-weighted distribution of third-party mentions of the business across sources the model trusts. The single highest-weight input. Detailed in citation density.
  2. Structured data. Machine-readable schema markup on the business's website (Attorney, LegalService, Review, Organization, FAQPage). Parsed directly by AI engines during retrieval. Absent schema produces undifferentiated text from the engine's perspective.
  3. Entity establishment. Consistent, canonical identification across the web — single firm name, consistent NAP (name, address, phone) data, unambiguous attorney profiles. Entity ambiguity is the most common reason strong firms vanish from AI answers despite robust traditional marketing.
  4. Topical depth. Content that answers narrow consumer questions in narrow practice-area-by-jurisdiction depth. Generic "personal injury" pages contribute less than specific pages on "uninsured motorist claim Florida" or "delayed-onset whiplash settlement Texas."
  5. Reputation surface. Substance of reviews more than volume. Recent, specific, sentiment-rich review language is quotable by AI engines; generic five-star ratings without commentary contribute little.

Why multiplicative.

A firm scoring high on four of five layers and zero on the fifth typically does not appear in AI answers. The model has no incentive to surface an entity that fails one of the five tests. Increasing investment on a layer the firm already passes does not compensate for the layer it fails. The implication for budget allocation: rebuild the missing layers first, then scale the strong ones.

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