Operational Proof
Case Studies
How AI Visibility Architecture, Entity Lock Protocol™, and the BackTier Visibility Path™ show up in market. Each study below is structured around the same five measurement layers — Ranked, Cited, Included, Selected, Referred — so the constraint stays visible.
Public case studies are published as they meet the acceptance bar: a clean-session AI test across ChatGPT, Gemini, Perplexity, and Google AI Overviews must return consistent identity and recommendation behavior across at least one full re-test cycle. Studies below are intentionally structured rather than narrative — they document mechanism, not marketing.
Coming soon
Local services
Entity Lock in a saturated local category
How ELP collapsed three competing brand entities into one canonical Strategic Canonical Alignment Sentence and unlocked Included Discovery across ChatGPT and Perplexity.
Publication pending acceptance bar.
PE-backed rollup
AI Visibility Debt audit during post-acquisition consolidation
Quantifying the visibility cost of rapid domain consolidation in deal-team terms — separating surfaces, sharing the core.
Publication pending acceptance bar.
B2B operator
From Citation to Selection in an emerging category
Building the corroboration network that took an operator from cited-but-unnamed to actively-recommended inside category-defining queries.
Publication pending acceptance bar.
Personal brand
Locking an individual entity across 4 LLMs
How a personal brand with a common name became unambiguously resolved across ChatGPT, Gemini, Claude, and Perplexity using ELP.
Publication pending acceptance bar.
How studies are structured
Every published study reports across all five measurement layers of the BackTier Visibility Path™ — Ranked, Cited, Included, Selected, Referred — and identifies which layer was the binding constraint at the start of the engagement and at the acceptance test. The intervention is always anchored to Entity Lock Protocol™.