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Legal marketing • AI search visibility SaaS • Ongoing internal venture

LawFirm AI Visibility

Internal Venture Case Study

Launching a vertical AI-visibility tracker from zero to indexed authority in a category that didn't exist 18 months ago.

0 → 4

AI assistants citing the brand

ChatGPT, Gemini, Perplexity, and AI Overviews

60+

Indexed pages

from a zero-history domain at launch

< 60 days

Time to first AI citation

from initial publish to first quoted answer

Productized

Playbook reuse

now the basis of our AI visibility audit service

Overview

LawFirm AI Visibility is an internal Search Brilliance venture that tracks how law firms appear inside ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We used it as a live laboratory for the same generative-engine optimization playbook we run for clients: publish entity-dense, citable content, earn third-party mentions, and measure whether AI systems actually repeat the brand.

The challenge

  • A brand-new category with almost no existing search demand to capture — the queries had to be created, not harvested.
  • AI assistants had no entity understanding of the product, so it was never surfaced in answer sets.
  • Legal is a YMYL vertical where AI systems weight source trust heavily, raising the bar for citations.
  • No domain history, no backlinks, and no brand mentions at launch.

Our approach

1

Entity-first content architecture

Built a topic graph around how AI systems reason about legal visibility — definitions, comparisons, and measurable methodology pages — so each URL answers one question completely and can be quoted verbatim by an assistant.

2

Citation and mention acquisition

Ran a digital PR and listicle program aimed at the sources large language models actually retrieve from, rather than chasing raw domain-rating metrics.

3

Schema and machine-readable surfaces

Deployed a connected @graph of Organization, WebSite, SoftwareApplication, and FAQPage schema plus an llms.txt manifest so crawlers and AI agents can parse the offering without guesswork.

4

Closed-loop measurement

Prompted the major assistants on a fixed schedule and logged whether the brand was cited, which page was quoted, and how the answer changed after each content release.

"We built it on ourselves first. Every tactic we sell for AI search visibility was proven on a domain with no history, no links, and no brand equity."

— Jeremy Osborn, Founder, Search Brilliance

The playbook

✓Entity-first content architecture for generative engines
✓Citation acquisition targeted at LLM retrieval sources
✓Connected @graph schema plus llms.txt for AI crawlers
✓Scheduled assistant prompting as a measurement loop

Services delivered

AI Visibility Audit →AI Digital PR Campaigns →Schema Implementation →

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