# 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`](./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`](./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): 1. Stakeholder verify. 2. Prompt major assistants with fixed buyer asks; record variance. 3. Score **Presence** (identified correctly across claim surfaces and stomped copies) and **Recommendability** (named when it matters; facts in the answer match claimed truth). 4. 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](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). **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. 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) 1. Promise wording: “Am I discoverable by AI?” vs “Does AI describe me correctly?” — which better sells Presence + Recommendability? 2. Which assistants do we prompt in v1 so Recommendability measurement is honest without overclaiming? 3. How automated is “accuracy” vs owner compare-to-claim — enough to move Recommendability without fake precision? 4. Localeze / Data Axle filing: paid add-on, referral, or only via pipes the owner already has — without pretending free Lane A exists? 5. Foursquare Managed Users in v1 or later — does white-label materially improve Presence on AI-facing POI graphs? 6. 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.