Add plain-English muffin-shop framing to research brief.

Pain, one-button fix, and product application in human terms for
first-time readers; regenerate research-brief.pdf to match.

docs/ALTERNATE_BUSINESS_PROPOSAL.md

@@ -89,7 +89,7 @@ We win if we are the shortest path from “AI can’t find / trust / correctly d
 
 Refocused 2026-10-01 so every section ladders to BUSINESS_PLAN North Star intent + concept Presence/Recommendability. Evidence from Localeze research and r/localseo; statistics are practitioner-reported, not independently replicated.
 
-**Research brief appended 2026-10-01; North Star preamble added same day** — verbose evidence synthesis below (section RESEARCH BRIEF). PDF export: [`docs/research-brief.pdf`](./research-brief.pdf). Does not replace North Star / Problem / Solution / Differentiators / Open questions; amends Capture with this date stamp only.
+**Research brief appended 2026-10-01; North Star preamble + plain-English muffin-shop section added same day** — verbose evidence synthesis below (section RESEARCH BRIEF). PDF export: [`docs/research-brief.pdf`](./research-brief.pdf). Does not replace North Star / Problem / Solution / Differentiators / Open questions; amends Capture with this date stamp only.
 
 ---
 
@@ -109,6 +109,24 @@ The North Star for GEOLOCAL is **agent-era local SMB discoverability**: local bu
 
 Everything below is evidence gathered **in service of that North Star** — what practitioners and vendors say moves Maps/calls vs what actually bears on AI Presence and Recommendability, what is waste, and where the proof is thin. It is not a general local-SEO encyclopedia; read every finding against “does this help an owner become discoverable and accurately recommended by agents?”
 
+### In plain English (the muffin shop)
+
+Imagine Maya runs a small muffin shop. She updates her Google listing with the new phone number and weekend hours. She thinks she is done.
+
+She is not done. Yelp still shows last year’s number. Bing Places still shows last year’s hours. A dozen smaller directories — and the big data companies that quietly feed them — still have the old copy. When someone asks an AI assistant “where’s a good bakery near me?” that assistant often leans on those scattered copies. Sometimes Maya does not show up at all. Sometimes she shows up with the wrong number. Customers lose trust; Maya never sees why.
+
+**The pain, in human terms:** fixing Google is not the same as being findable and accurately described wherever people — and AI — look.
+
+**The fix, in human terms:** one button that (1) checks what AI assistants actually say about the muffin shop, (2) finds every stale copy, and (3) files the correction on each platform automatically. Maya never fills out a directory form. She confirms the truth once; the product does the chasing.
+
+**How we apply that to the product:**
+
+1. **Lead with the AI-accuracy audit** — the hook Maya can feel. “Here’s what ChatGPT / Gemini / others say about you — found, recommended, or wrong.” That is Presence and Recommendability made visible.
+2. **Use the ticket set as the engine** — Lane A umbrellas (the data companies that fan out into many directories) plus Lane B majors (Google, Apple, Bing, Yelp, and the other big claim surfaces). The audit finds the stale copies; the tickets fix them with Maya’s claimed truth (and Google as proof when it helps).
+3. **Measure success the way the North Star asks** — is the muffin shop findable, and is it accurately represented? Re-ask the same AI questions after corrections. Do not celebrate a directory “completeness” score that Maya’s customers never see.
+
+Keep Maya in mind while reading the evidence below. The tables and studies are the receipts behind this story — not a different product.
+
 ### Pain
 
 Local owners and agencies are stuck between two scoreboards that do not agree.