Refocus alternate proposal on North Star Presence/Recommendability.
Rewrite ALTERNATE_BUSINESS_PROPOSAL.md so problem, audit, ticket set, differentiators, and open questions each ladder to discovered + recommended accurately by AI assistants — not tangential product ideas.
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# Alternate business proposal: AI-answer accuracy via ticket-set remediation
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# Alternate business proposal: North Star via accurate AI presence
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**Branch:** `planning-selfserve-audit`
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**Status:** Alternate framing — working draft 2026-10-01
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**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.
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**Status:** Alternate framing — working draft 2026-10-01 (refocused same day)
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**Relationship to SoT:** Not a replacement for [`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md). That file stays the product architecture SoT. This proposal is a sharper commercial ladder to the same North Star. Amend or kill when Ty locks or rejects it.
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---
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## Thesis
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## North Star (what every section serves)
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The highest-leverage product is **not** another citation manager or Localeze-class hub.
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From [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md): **enable local businesses to be discovered and recommended by AI assistants.**
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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.
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On this branch that intent is scored as two owner-facing outcomes in the concept SoT:
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Presence and recommendability still matter. The alternate wedge is: **wrong AI answers often come from stomped sources; fix the sources that models actually pull.**
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- **Presence** — are you on the map wherever discovery happens (majors, umbrellas, and the copies assistants lean on), with consistent identity and categories?
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- **Recommendability** — if you are on the map, would an assistant pick you for a real genre ask — and describe you **accurately**?
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Longer-term destination in the SoT (not v1): an **industry graph**. Version one earns that right by making Presence and Recommendability true in today’s assistants, with owner consent.
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This proposal’s only job: a path that moves Presence and Recommendability when AI answers are missing or wrong because **stale directory / aggregator data** feeds those answers.
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---
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## Problem statement (from practitioner pain)
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## Problem → North Star gap
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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.
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r/localseo practitioners keep hitting the same failure modes that break the North Star:
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**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.
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- **Not discovered.** Reported experiments (e.g. six engines × ~50 locals) claim most shops are invisible to most assistants even with strong Google rankings — Presence fails in the AI layer.
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- **Not recommended — or recommended wrong.** ChatGPT and Gemini pull from **different pools** (Gemini more GBP/Maps-grounded; ChatGPT more web-search fan-out). Reviews alone don’t guarantee a cite. Stale hours and wrong services from random directories show up in answers — Recommendability fails on **accuracy**, not only mention count.
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- **Owners can’t see the gap.** Tools feel unreliable; people run manual prompt grids. Without an accuracy-aware audit, they can’t tell if they are present or recommendable to AI.
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**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.
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**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.
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**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?**”
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**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.”
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That is the owner pain in one line: **“I rank / I have reviews — why does AI ignore me or lie about me?”**
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Root mechanism (same black box as the concept SoT): a cloud of unreconciled NAP copies. Assistants pick from the fog. Localeze’s paid hub and Completeness Score optimize **their** profile form — they do not measure AI Presence/Recommendability across the web.
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---
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## Proposed solution
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## Solution → how we move the North Star
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### 1. Audit for AI answer accuracy (not vanity mentions)
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### Audit: measure Presence and Recommendability where AI looks
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Owner-initiated check (web first; same engine later in-biz chat if wanted):
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Owner-initiated (web first):
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1. Stakeholder verify (e.g. code to known email).
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2. Run a small, fixed set of buyer prompts across major assistants (and record variance — AI is stochastic).
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3. Score two layers that map to the existing SoT language:
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- **Presence:** are you findable / correctly identified (NAP, categories, majors + umbrella copies)?
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- **Recommendability:** would an assistant pick you for genre prompts — and when it talks about you, are hours, services, and location **correct**?
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4. Flag **wrong-answer sources** when possible (which directory, listing, or page the answer leans on) — not only “you weren’t named.”
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1. Stakeholder verify.
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2. Prompt major assistants with fixed buyer asks; record variance.
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3. Score **Presence** (identified correctly across claim surfaces and stomped copies) and **Recommendability** (named when it matters; facts in the answer match claimed truth).
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4. Prefer “mentioned vs recommended vs **misstated**” over vanity mention counts.
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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.
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That is the North Star scoreboard applied to live AI answers — not Localeze completeness, not citation-pack vanity.
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### 2. One-click ticket set that fixes stomped sources
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### Ticket set: fix the sources that break Presence and Recommendability
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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.
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One button mints correction tickets (field / wrong / right / **GBP or claimed truth as proof**).
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**Lane A — umbrellas (syndication layer)**
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Write / suggest into:
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**Lane A — umbrellas** (stop the fog regenerating): [Data Axle](https://www.data-axle.com/marketing-solutions/local-listings-management/), [Localeze / TransUnion](https://neustarlocaleze.biz/) (~$99/yr paywall; no free suggest-edit; 30–90 day monthly partners), [Foursquare Places](https://foursquare.com) (Placemaker suggest-edit; AI-adjacent POI pipe — partnership ≠ proven ranking lever).
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- [Data Axle](https://www.data-axle.com/marketing-solutions/local-listings-management/) Local Listings
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- [Localeze / TransUnion](https://neustarlocaleze.biz/) Digital Business Profile
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- [Foursquare Places](https://foursquare.com) (Placemaker suggest-edit; `POST /places/{id}/suggest/edit` when white-labeling)
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**Lane B — majors** (own databases assistants also trust): [GBP](https://business.google.com) (proof hub), [Apple Business Connect](https://business.apple.com), [Bing Places](https://www.bingplaces.com), [Yelp](https://www.yelp.com) suggest-edit.
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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.
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GBP does not outbound-syndicate NAP. Umbrella-only leaves Core 3 / Yelp wrong; majors-only leaves Layer 1 feeds stomping the claim. Both lanes exist so Presence sticks and Recommendability isn’t fed lies.
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**Lane B — majors (own databases)**
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Claim / suggest-edit as first-class workflows:
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**Held multi-platform verification** (to architect): corrections that survive the next stale feed — the moat under lasting Presence.
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- [Google Business Profile](https://business.google.com) (proof hub; fix first when wrong)
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- [Apple Business Connect](https://business.apple.com)
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- [Bing Places](https://www.bingplaces.com)
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- [Yelp](https://www.yelp.com) suggest-an-edit
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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.”
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**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.
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### 3. Why Foursquare sits in both lanes
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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.
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**Success check against the North Star:** re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up.”
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---
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## Differentiators
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## Differentiators → only vs the North Star
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| Surface | What they sell | Gap vs this proposal |
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| --- | --- | --- |
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| **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) |
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| **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 |
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| **Citation audit tools** | NAP mismatch reports | Rarely close the loop with pre-filled platform tickets + GBP proof; weak on AI answer accuracy |
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| **AI visibility tools** | Mention / citation tracking across LLMs | Early, inconsistent; practitioners still run manual prompt grids; few check **factual correctness** of hours/services |
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| Player | Why they don’t finish the North Star alone |
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| --- | --- |
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| Localeze / TransUnion | Paid Lane A fan-out + hub Completeness Score; no AI-answer Presence/Recommendability audit; no Lane B ticket set |
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| Yext / Uberall / BrightLocal | Strong pipes into publishers; still not “discovered and recommended accurately by assistants” as the measured outcome |
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| Citation / AI-mention tools | Mismatch or mention counts; weak loop to tickets with GBP proof; rarely check answer **accuracy** |
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**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.
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We win if we are the shortest path from “AI can’t find / trust / correctly describe me” to “Presence and Recommendability hold after re-check.”
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---
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## Fit with the existing concept SoT
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## Open questions (each must serve the North Star)
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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.
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This alternate proposal **emphasizes**:
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- Wrong AI answers as the emotional and commercial hook (Reddit-validated).
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- Accuracy-of-answer scoring alongside mention/recommend scoring.
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- Explicit “Localeze is paid Lane A, not a free suggest-edit.”
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- Foursquare as AI-adjacent POI pipe + Placemaker tickets.
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It does **not** redefine MCP as the product, does not replace genre clusters, and does not claim the industry graph is v1.
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---
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## Open questions (Ty)
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1. Is the public promise “Am I discoverable by AI?” or sharper: “Does AI describe my business correctly?”
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2. v1 engines: which assistants do we actually prompt (cost, ToS, variance)?
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3. How much accuracy checking is automated vs “owner compares our claim to the AI paragraph”?
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4. Do we sell Localeze / Data Axle filing as paid add-ons, partner referral, or only when the owner already has a pipe?
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5. White-label Foursquare Managed Users — build for v1 or later?
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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?
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1. Promise wording: “Am I discoverable by AI?” vs “Does AI describe me correctly?” — which better sells Presence + Recommendability?
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2. Which assistants do we prompt in v1 so Recommendability measurement is honest without overclaiming?
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3. How automated is “accuracy” vs owner compare-to-claim — enough to move Recommendability without fake precision?
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4. Localeze / Data Axle filing: paid add-on, referral, or only via pipes the owner already has — without pretending free Lane A exists?
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5. Foursquare Managed Users in v1 or later — does white-label materially improve Presence on AI-facing POI graphs?
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6. Keep this file as GTM ladder and the concept SoT as architecture — or merge and delete this alternate?
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---
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## Capture
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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.
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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.
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