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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.
9.8 KiB
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. 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. 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):
- Stakeholder verify (e.g. code to known email).
- Run a small, fixed set of buyer prompts across major assistants (and record variance — AI is stochastic).
- 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?
- 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 Local Listings
- Localeze / TransUnion Digital Business Profile
- Foursquare Places (Placemaker suggest-edit;
POST /places/{id}/suggest/editwhen 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 (proof hub; fix first when wrong)
- Apple Business Connect
- Bing Places
- Yelp 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 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)
- Is the public promise “Am I discoverable by AI?” or sharper: “Does AI describe my business correctly?”
- v1 engines: which assistants do we actually prompt (cost, ToS, variance)?
- How much accuracy checking is automated vs “owner compares our claim to the AI paragraph”?
- Do we sell Localeze / Data Axle filing as paid add-ons, partner referral, or only when the owner already has a pipe?
- White-label Foursquare Managed Users — build for v1 or later?
- 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.