Add alternate business proposal: AI accuracy via ticket-set.

New docs/ALTERNATE_BUSINESS_PROPOSAL.md on planning-selfserve-audit; does not amend the self-serve concept SoT. Synthesizes two-lane remediation, r/localseo AI-visibility pain, Localeze paywall, and Foursquare Placemaker / Places pipe into an alternate commercial thesis.
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 # Alternate business proposal: AI-answer accuracy via ticket-set remediation
 **Branch:** `planning-selfserve-audit`  
 **Status:** Alternate framing — working draft 2026-10-01  
 **Relationship to SoT:** This is **not** a replacement for [`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md). That file remains the single working concept for the self-serve “Am I discoverable by AI?” product. This proposal explores a sharper commercial thesis that emerged from Localeze research and practitioner threads on [r/localseo](https://www.reddit.com/r/localseo/). Amend or kill this file when Ty locks or rejects the alternate bet.
 ---
 ## Thesis
 The highest-leverage product is **not** another citation manager or Localeze-class hub.
 It is an **AI-visibility audit that checks whether assistants describe the business correctly** — not only whether the name is mentioned — plus a **one-click ticket set** that treats **stale directory and aggregator copies** as a root cause of wrong AI answers.
 Presence and recommendability still matter. The alternate wedge is: **wrong AI answers often come from stomped sources; fix the sources that models actually pull.**
 ---
 ## Problem statement (from practitioner pain)
 On r/localseo, local SEO people already treat “show up in ChatGPT / Gemini / Perplexity / AI Overviews” as a live problem — and they keep hitting the same walls.
 **Invisible despite ranking.** One widely discussed experiment claimed that for ~50 local businesses asked across six engines (ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, Copilot), **about 73% were invisible to at least four of the six**, including businesses on page one of Google. Directory consistency correlated with recommendations; raw Google review volume did not reliably translate into AI recommendations. Treat the exact percentages as practitioner-reported, not audited science — the pattern matches many other threads: strong Maps / organic still lose AI answers to competitors with clearer web evidence.
 **Different engines, different pools.** Practitioners repeatedly say ChatGPT and Gemini do **not** use the same local data. Gemini is described as more **GBP / Maps-grounded**; ChatGPT more as **web-search fan-out** (directories, “best of” posts, Bing-adjacent sources, forums). Optimizing only GBP leaves ChatGPT recommending someone else; polishing only blog mentions leaves Gemini leaning on Maps.
 **Wrong facts, not just missing mentions.** Threads complain about stale hours, wrong services, and bad summaries. One accuracy-focused discussion noted AI pulling outdated details from **random directories** instead of the site or GBP. Another documented Google Maps attaching a nearby business’s **Apple Maps** source / services to the wrong shop — entity confusion that then feeds AI-facing surfaces.
 **Owners are already auditing by hand.** Tracking threads converge on fixed prompt grids (service × city), many repeats, engines kept separate — because AI visibility tools feel early and unreliable. Referral analytics from ChatGPT are noisy. People want “mentioned vs recommended” and increasingly “**is the answer accurate?**”
 **NAP helps, but is not enough.** Consensus: consistent NAP / entity identity helps models know **which** business they are looking at. Quotable FAQs, specific review language, and third-party narrative mentions help them decide **why** to recommend. Localeze-style hub completeness is not the same as “AI would recommend you.”
 That is the owner pain in one line: **“I rank / I have reviews — why does AI ignore me or lie about me?”**
 ---
 ## Proposed solution
 ### 1. Audit for AI answer accuracy (not vanity mentions)
 Owner-initiated check (web first; same engine later in-biz chat if wanted):
 1. Stakeholder verify (e.g. code to known email).
 2. Run a small, fixed set of buyer prompts across major assistants (and record variance — AI is stochastic).
 3. Score two layers that map to the existing SoT language:
   - **Presence:** are you findable / correctly identified (NAP, categories, majors + umbrella copies)?
   - **Recommendability:** would an assistant pick you for genre prompts — and when it talks about you, are hours, services, and location **correct**?
 4. Flag **wrong-answer sources** when possible (which directory, listing, or page the answer leans on) — not only “you weren’t named.”
 This is deliberately different from Localeze’s **Profile Completeness Score**, which (per their help) measures how filled the **Localeze hub profile** is for partner confidence — Primary / Discovery / Connect / Hours tabs — not whether ChatGPT’s Tuesday hours match reality.
 ### 2. One-click ticket set that fixes stomped sources
 Every material finding becomes a **correction ticket** (field, wrong value, right value, **GBP or claimed truth as proof**). One button mints a **set**, not a single fan-out hope.
 **Lane A — umbrellas (syndication layer)**  
 Write / suggest into:
 - [Data Axle](https://www.data-axle.com/marketing-solutions/local-listings-management/) Local Listings  
 - [Localeze / TransUnion](https://neustarlocaleze.biz/) Digital Business Profile  
 - [Foursquare Places](https://foursquare.com) (Placemaker suggest-edit; `POST /places/{id}/suggest/edit` when white-labeling)
 Caveats baked into the product design: Localeze monthly partners often take **30–90 days**; partners decide what to publish; Google via Localeze needs a **separate Google sync**; Localeze self-serve updates are **paywalled (~$99/year per location)** with **no public free suggest-edit** — so Lane A Localeze tickets are paid-path or “route through a pipe the owner already buys,” not a free chore we pretend is open.
 **Lane B — majors (own databases)**  
 Claim / suggest-edit as first-class workflows:
 - [Google Business Profile](https://business.google.com) (proof hub; fix first when wrong)  
 - [Apple Business Connect](https://business.apple.com)  
 - [Bing Places](https://www.bingplaces.com)  
 - [Yelp](https://www.yelp.com) suggest-an-edit  
 GBP does **not** syndicate NAP outbound to those surfaces. Practitioners on Reddit also warn: for risky GBP changes, align key web sources **before** or alongside GBP so Google’s checks “track.”
 **Held multi-platform verification** remains the long-term moat: file as a verified actor so corrections stick when the next stale feed arrives. Not built yet — architecture to design.
 ### 3. Why Foursquare sits in both lanes
 Foursquare is an umbrella (place graph → licensees) **and** a suggest-edit surface. Placemaker APIs support owner/partner corrections. Public discussion also ties Foursquare Places data into assistant ecosystems (including OpenAI / ChatGPT Places-related distribution — practitioner Reddit has **little** substantive workflow detail; treat partnership as **pipe opportunity**, not proof of ranking). Product implication: Foursquare tickets are high leverage for “AI-facing POI truth,” especially when ChatGPT-class products lean on places graphs — without claiming a secret ranking lever.
 ---
 ## Differentiators
 | Surface | What they sell | Gap vs this proposal |
 | --- | --- | --- |
 | **Localeze / TransUnion DBP** | Claim hub → 80+ publishers; ~$99/yr; unlimited updates on paid plan; Completeness Score on **their** form; Google sync bolt-on | No free correction path; no cross-platform AI-answer accuracy audit; no Lane B majors as first-class tickets; fan-out caveats (30–90 days, partner control) |
 | **Yext / Uberall / BrightLocal-class** | Paid one-to-many listing pipes | Strong productized fan-out; still not “did AI answer correctly?”; owners without a pipe still need majors + umbrellas as tickets |
 | **Citation audit tools** | NAP mismatch reports | Rarely close the loop with pre-filled platform tickets + GBP proof; weak on AI answer accuracy |
 | **AI visibility tools** | Mention / citation tracking across LLMs | Early, inconsistent; practitioners still run manual prompt grids; few check **factual correctness** of hours/services |
 **Our wedge:** audit that asks “would AI recommend you **and** get the facts right?” → ticket set (GBP proof + Lane A + Lane B) → re-prompt to prove the fog thinned. Citation management is a means; **accurate AI answers** are the outcome.
 ---
 ## Fit with the existing concept SoT
 Keep [`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md) as the product SoT: owner-to-AI mission, Presence / Recommendability, two lanes, ranked 1–14 stack, one-to-many fan-out limits, industry graph as Northstar not v1.
 This alternate proposal **emphasizes**:
 - Wrong AI answers as the emotional and commercial hook (Reddit-validated).  
 - Accuracy-of-answer scoring alongside mention/recommend scoring.  
 - Explicit “Localeze is paid Lane A, not a free suggest-edit.”  
 - Foursquare as AI-adjacent POI pipe + Placemaker tickets.
 It does **not** redefine MCP as the product, does not replace genre clusters, and does not claim the industry graph is v1.
 ---
 ## Open questions (Ty)
 1. Is the public promise “Am I discoverable by AI?” or sharper: “Does AI describe my business correctly?”  
 2. v1 engines: which assistants do we actually prompt (cost, ToS, variance)?  
 3. How much accuracy checking is automated vs “owner compares our claim to the AI paragraph”?  
 4. Do we sell Localeze / Data Axle filing as paid add-ons, partner referral, or only when the owner already has a pipe?  
 5. White-label Foursquare Managed Users — build for v1 or later?  
 6. Does this alternate become the GTM story while the concept SoT stays the architecture bible — or do we merge the thesis into the SoT and archive this file?
 ---
 ## Capture
 Drafted 2026-10-01 on `planning-selfserve-audit` from Localeze help/pricing research, concept SoT Lane A/B architecture, and r/localseo AI-visibility + aggregator threads. Practitioner statistics cited as reported in-community, not independently replicated.