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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: North Star via accurate AI presence
Branch: planning-selfserve-audit
Status: Alternate framing — working draft 2026-10-01 (refocused same day)
Relationship to SoT: Not a replacement for 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.
North Star (what every section serves)
From BUSINESS_PLAN.md: enable local businesses to be discovered and recommended by AI assistants.
On this branch that intent is scored as two owner-facing outcomes in the concept SoT:
- Presence — are you on the map wherever discovery happens (majors, umbrellas, and the copies assistants lean on), with consistent identity and categories?
- Recommendability — if you are on the map, would an assistant pick you for a real genre ask — and describe you accurately?
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.
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.
Problem → North Star gap
r/localseo practitioners keep hitting the same failure modes that break the North Star:
- 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.
- 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.
- 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.
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.
Solution → how we move the North Star
Audit: measure Presence and Recommendability where AI looks
Owner-initiated (web first):
- Stakeholder verify.
- Prompt major assistants with fixed buyer asks; record variance.
- Score Presence (identified correctly across claim surfaces and stomped copies) and Recommendability (named when it matters; facts in the answer match claimed truth).
- Prefer “mentioned vs recommended vs misstated” over vanity mention counts.
That is the North Star scoreboard applied to live AI answers — not Localeze completeness, not citation-pack vanity.
Ticket set: fix the sources that break Presence and Recommendability
One button mints correction tickets (field / wrong / right / GBP or claimed truth as proof).
Lane A — umbrellas (stop the fog regenerating): Data Axle, Localeze / TransUnion (~$99/yr paywall; no free suggest-edit; 30–90 day monthly partners), Foursquare Places (Placemaker suggest-edit; AI-adjacent POI pipe — partnership ≠ proven ranking lever).
Lane B — majors (own databases assistants also trust): GBP (proof hub), Apple Business Connect, Bing Places, Yelp suggest-edit.
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.
Held multi-platform verification (to architect): corrections that survive the next stale feed — the moat under lasting Presence.
Success check against the North Star: re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up.”
Differentiators → only vs the North Star
| Player | Why they don’t finish the North Star alone |
|---|---|
| Localeze / TransUnion | Paid Lane A fan-out + hub Completeness Score; no AI-answer Presence/Recommendability audit; no Lane B ticket set |
| Yext / Uberall / BrightLocal | Strong pipes into publishers; still not “discovered and recommended accurately by assistants” as the measured outcome |
| Citation / AI-mention tools | Mismatch or mention counts; weak loop to tickets with GBP proof; rarely check answer accuracy |
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.”
Open questions (each must serve the North Star)
- Promise wording: “Am I discoverable by AI?” vs “Does AI describe me correctly?” — which better sells Presence + Recommendability?
- Which assistants do we prompt in v1 so Recommendability measurement is honest without overclaiming?
- How automated is “accuracy” vs owner compare-to-claim — enough to move Recommendability without fake precision?
- Localeze / Data Axle filing: paid add-on, referral, or only via pipes the owner already has — without pretending free Lane A exists?
- Foursquare Managed Users in v1 or later — does white-label materially improve Presence on AI-facing POI graphs?
- Keep this file as GTM ladder and the concept SoT as architecture — or merge and delete this alternate?
Capture
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.