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Capture Localeze/Data Axle/Yext-class fan-out caveats as architect guidance, and hyperlink Data Axle, Localeze/TransUnion, and Foursquare Places to their canonical pages in that section.
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How local businesses get AI to recommend them
Branch: planning-selfserve-audit (from planning-rev2)
Status: Working draft from conversation on 2026-10-01; cluster cut revised after GBP mapping pressure test the same day
Related: BUSINESS_PLAN.md · conventions in CONVENTIONS.md
This is the single working doc for the self-serve “Am I discoverable by AI?” idea — both the why and the phased how. Revise this file in place. Do not add a sibling PLAN or CONCEPT file for the same subject.
What we are trying to do
The mission, stated plainly: help local business owners get AI to recommend them.
That puts the owner talking to AI (or using a tool that talks to AI on their behalf) at the center. Scores, audits, and before/after proof are useful for selling and teaching — they are not the mission itself. We are not building another website where consumers browse listings, not rebuilding booking or shop software, and not defining ourselves as “an MCP company.”
The problem we are solving
Local businesses are entering an era where people ask assistants — ChatGPT, Claude, Gemini, Perplexity, and whatever comes next — for a plumber, a place to eat, or a surf rental near them. If those assistants cannot find or trust a business’s facts, the business simply does not get recommended.
That discoverability in the agent era is the spine of the strategy. How assistants connect to tools and data is just plumbing. The pipe is not the product.
Under that spine sits a quieter mess — a big black box of confusion — that most owners feel but cannot name.
The black box: a cloud of copies
A local business is not a single record on the internet. It is a cloud of copies.
Over years, real life moves. You change suite numbers. The town renumbers a road. The phone company swaps an area code. You get a new mobile for the shop. You update the website. Each of those changes is ordinary. What is not ordinary is what happens next.
Every major place that stores local businesses keeps its own copy of your name, address, and phone — the boring facts people call NAP (name, address, phone). Google Business Profile has a copy. Apple has a copy. Bing has a copy. Yelp has a copy. Foursquare’s place graph has a copy. Underneath many of them, data aggregators and syndicators (companies such as Data Axle and Localeze / TransUnion) hold yet another copy and push it out to directories, maps, navigation apps, and anything that buys “local listings” feeds.
Nobody is paid to keep all of those copies honest with each other.
So the copies drift. One still has the old area code. One still has the storefront you left three years ago. One has the right street but the wrong suite. Search engines and AI assistants do not magically know which copy is you today. They see a black box of conflicting facts and pick whatever their ranking and training happen to trust that week.
When an owner finally claims Google Business Profile and fixes it, that is powerful — GBP is usually the strongest claim surface an owner can touch. But it is not the only voice. Syndicated feeds can still broadcast the old NAP. Those stale copies can show up in search results and in AI answers alongside or even above the listing the owner just cleaned up. From the owner’s chair it feels like: “I fixed it — why does the world still lie about me?”
That is the problem in one line: the owner cannot control what the world believes about their business, because the world is not one database. It is a black box of unreconciled copies.
Opening the black box: claim once, correct outward
We flip the direction of truth.
Today, copies compete. The owner chases them. Tomorrow, the owner declares one clean NAP — a single claimed truth for who they are and how to reach them — and that declaration becomes the source of truth. The job of the system is then to push corrections outward into the aggregators and syndicated feeds, instead of letting those feeds quietly overwrite or outrank the claim.
Think of it as three frames:
BEFORE CLAIM AFTER
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ five wrong │ │ one clean │ │ copies that │
│ copies of │ ──────► │ NAP the │ ──────► │ match the │
│ the same │ │ owner just │ │ claim — │
│ business │ │ declared │ │ pushed out │
└─────────────┘ └─────────────┘ └─────────────┘
(the black box) (source of truth) (reconciled world)
Frame one — left. The black box: several disagreeing copies (Google, Apple, Bing, Yelp, Foursquare, and whatever else still carries an old feed). The owner did not ask for five versions of themselves. The market produced them.
Frame two — middle. The claim: one clean NAP the owner stands behind. That is the only version we treat as authoritative for this business. Fixing GBP still matters — it is often the best place to start the claim — but the point is the declared truth, not loyalty to any one logo.
Frame three — right. Corrections flowing out. The system’s job is to make the other copies match the claim, including the ugly middle layer most owners never see: syndication. Aggregators and platforms that feed directories and assistants — Yelp, Bing, Foursquare, and the rest — routinely stomp on a small business’s source of truth with stale hours, old phone numbers, and leftover addresses. If you only polish the claim surface and ignore those copies, the black box regenerates overnight.
Matching NAP is the floor — presence with consistent facts. Recommendability (would an assistant pick you for a real customer question in your genre) still needs categories, relevance, and the rest of this product story. Opening the black box is how we stop the floor from moving under the owner’s feet.
Assistants answer from whatever they can find and trust. When five copies disagree, the assistant is guessing in the fog. When one claimed truth is pushed outward and the other copies are brought into line, the fog thins. Strategic bet in plain words: stop competing with your own stale twins. Claim once. Correct outward.
What we have to architect (not built yet)
The direction above is the bet. Below is the solution we still have to design and build — not work already done.
1. Every audit finding becomes a correction ticket
Today an owner who sees wrong hours on Yelp, a dead phone on Bing, and an old suite on Foursquare has to decode three different “suggest an edit” paths. That is the chore we remove.
For each platform that still shows stale or wrong data, the product turns the audit finding into a correction ticket with the same shape every time: which field is wrong, what value is showing, what the right value is, and a link to Google Business Profile (or the owner’s claimed truth) as proof. Then the owner files that platform’s own correction channel — Yelp’s suggest-an-edit page, Foursquare’s suggest-edit API (POST /places/{id}/suggest/edit), Bing Places feedback, and the same pattern for others as we map them. Claim it is wrong. Show proof. Wait.
Our side of the architecture: mint that ticket automatically from the audit — pre-filled, one click to file — instead of making the owner hunt which form goes where. The platforms still own their review queues. We own the translation from “your Presence score is broken here” into “here is the exact correction this platform will accept.”
2. Held verification across platforms is the moat
Business verification is the tax. Every platform re-verifies independently. Yelp wants its documents. Bing wants its own proof of ownership. Foursquare has its own checks. There is no shared credential and no way to say “I already proved this to Google.” Each review takes days to weeks, and without standing as a verified actor, corrections get slow-walked or quietly reverted when the next stale feed arrives.
A white-label correction service that holds verified status per platform — and files on the owner’s behalf without re-uploading a lease five times — is what makes corrections stick. That held, multi-platform verified status is the moat: the audit finds the drift; the ticket files the fix; the held verification is why the fix does not get stomped again tomorrow.
3. Two lanes, then a ranked remediation stack
Umbrella aggregators and end-user platforms are not the same job. Architecture needs two lanes:
- Lane A — umbrella submissions. Write once into Data Axle, Localeze / TransUnion, and Foursquare’s place graph so long-tail directories, libraries, and many nav / GPS consumers stop regenerating stale NAP.
- Lane B — per-platform tickets. Claim or suggest-edit on Google, Apple, Bing, Yelp, and other surfaces that keep their own databases and often prefer verified owner data over a feed.
Umbrella-only leaves the Core 3 and Yelp wrong. Platform-only leaves the black box regenerating from Layer 1 feeds. Both lanes mint the same ticket shape (field, wrong value, right value, GBP or claimed truth as proof). Rank below is most → least important for small-business local discoverability and for how hard a miss should hit the Presence / audit score. Each item is an owner-facing workflow we still have to architect — not a label and not a shipped feature.
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Google Business Profile (Lane B — claim hub / Core 3). Sign in to the GBP dashboard for that location. Open the wrong field (hours, phone, address, category). Enter the correct value and save. Attach documents only when Google asks — typically for large moves, ownership disputes, or category jumps, not for a routine hours tweak. Wait for the listing to refresh (often minutes to a day). Success: Maps / Search show the new value, and that GBP URL becomes the proof link on every other ticket.
-
Apple Business Connect (Lane B — Core 3). Sign in to Apple Business Connect, find the place card, edit the field, submit. Apple may review and email if more proof is needed. Wait for the place card to update across Maps and related Apple surfaces. Success: Apple Maps shows the corrected hours / NAP matching the claim.
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Bing Places (Lane B — Core 3). Sign in at the Bing Places portal, open the listing, edit the field. Attach proof that matches GBP — utility bill or lease with the same business name and address when Bing asks for ownership evidence. Submit and wait for Bing’s review queue (often days). Success: Bing local (and Copilot-adjacent local answers that lean on Places) show the corrected value.
-
Foursquare Places (Lane A + B). Two paths, same outcome: (a) Placemaker suggest-edit — pick the place, choose the field, enter wrong vs right, submit; or (b) our white-label one-click that calls
POST /places/{id}/suggest/editpre-filled, with the GBP URL as the cited source. Wait for Placemaker / Foursquare resolution (status via suggest-status). Success: the FSQ place record shows the new value (and downstream licensees that refresh from FSQ can follow). -
Yelp (Lane B). Open the business page → Suggest an edit. Pick the field, type the correction, add a short note citing the GBP listing as proof, submit. Yelp reviews manually. Wait days to weeks. Success: the Yelp page shows the corrected field and stops contradicting the claim.
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Data Axle Local Listings (Lane A). Claim the business at the Data Axle Local Listings portal with a business email and / or ownership docs. Edit NAP (and hours) once in that central record. Wait for Data Axle’s verification and syndication batch (scale / API path: Local Listings Premium docs) — hours to ~two weeks for hard cases. Success: the Local Listings record matches the claim; long-tail / nav partners that consume Data Axle begin reflecting the update on their next pull.
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Localeze / TransUnion Digital Business Profile (Lane A). Claim at the TransUnion / Localeze portal, correct NAP once, complete their verification. Wait for push out to their 80–100+ publisher network (often weeks for some partners). Success: the claimed profile matches GBP; sample downstream publishers no longer show the stale twin.
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YP / Thryv (Lane B, feeds long-tail). Claim via YP / Thryv listing management, edit the wrong field, save. Wait for their network to refresh. Success: the YP listing matches the claim; long-tail sites fed by that stack stop showing the old value when they next sync.
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Tripadvisor (Lane B — vertical). Claim via Tripadvisor for Business, open the field, submit the correction for review. Wait for their moderation. Success: the Tripadvisor listing matches the claim when travel / food recommendability cares about that surface.
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Facebook / Meta (Lane B — low weight). In Meta Business Suite, open the Page → contact / location info, edit hours or phone, publish. Usually no separate proof upload for small edits. Success: the Page info card matches GBP; treat as secondary Presence, not Core 3.
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MapQuest / HERE / in-car nav / long-tail directories (Lane A by default). There is usually no owner correction form. Path: file umbrella tickets (Data Axle, Localeze, Foursquare) and monitor those surfaces after syndication settles. Open a direct ticket only if a real claim URL exists and the miss survives umbrella propagation. Success: re-audit shows the nav / directory copy matches the claim without chasing each site by hand.
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Dun & Bradstreet — Low priority for consumer local. Skip unless a B2B path is in scope; if needed, update through D&B’s business profile tools with legal-entity proof. Success: firmographic record matches when that lane matters.
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CityGrid — Legacy. Only if the audit surfaces a CityGrid-fed copy still stomping the claim; then follow whatever claim / correction URL still exists, else rely on overlapping umbrellas. Success: that copy disappears or matches.
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Yext / Uberall / BrightLocal (pipes) — Not destinations. If the owner already pays one, route the same tickets through that pipe into Lanes A and B instead of a parallel write. Success: the pipe’s dashboard shows the jobs filed and publishers reporting the new value — we still score Presence on the publishers, not on the pipe brand.
Day in the life (workflow to architect)
A muffin shop’s Tuesday hours are wrong on the public web. The owner runs our audit, sees three red rows (GBP already correct, Foursquare stale, Data Axle still syndicating the old Tuesday close), and hits one “fix these” button.
Three pre-filled things fire — not three scavenger hunts:
- GBP — if anything still drifts there, a deep link into the hours editor with Tuesday pre-filled to the claimed value; otherwise we skip and reuse the live GBP URL as proof.
- Foursquare — a one-click Placemaker suggest-edit (
POST /places/{id}/suggest/edit) with field = hours, wrong = old Tuesday, right = claimed Tuesday, source = GBP URL (white-label Managed User when we file for them). - Data Axle — a Lane A claim / update ticket at Local Listings with the same NAP + hours payload so the umbrella stops stomping the claim on the next syndication cycle.
The owner attaches proof once where a portal demands it, then waits on each queue. Re-audit later is the success check: Tuesday matches everywhere that mattered. That is the workflow to architect — audit → tickets → wait → prove — not a pile of unlabeled “go fix Yelp” links.
4. One-to-many fan-out — useful lane, not the whole backbone
True one-to-many correction exists, but nothing is reliable enough to be the only spine of a one-click product. Research takeaways to architect around:
Localeze / TransUnion markets claim-once → 80–100+ publishers. In practice publishers decide what and when to show: many partners sit on a monthly publish cycle (~30–90 days); sync partners can be faster; Localeze does not control partner acceptance. Fast Google updates via Localeze require a separate Google verify/sync inside their product — not the feed alone. Some partners need extra OAuth (e.g. link Facebook). Distribution status reporting exists because fan-out is partial, not guaranteed.
Data Axle Local Listings / Local Listings Premium is the same shape: one verified submit → search, directories, libraries, nav, assistants. Processing can be hours to ~two weeks when human verification kicks in. Publisher lists overlap Localeze on many big names but are not identical (citation vendors still sell both). Treat them as complementary Lane A tickets, not duplicates.
Other fan-out: Paid listing managers (Yext-class — often 200+ direct publisher APIs with per-publisher status) are the strongest productized one-edit→many-majors path — route through them when the owner already pays (rank 14). Google Business Profile does not syndicate NAP outbound to Apple/Yelp/Bing. Apple Business Connect writes the Apple ecosystem (and partner APIs into Apple), not rival platforms. Foursquare Places suggest-edit updates FSQ’s graph (and later licensees), not a general directory blast.
Ceiling: One Localeze (or Data Axle) correction may move a fraction of consumer-visible surfaces in week one and more over 1–3 months; Core 3 claim surfaces often stay wrong without Lane B. So one-click means one button that mints a ticket set (GBP proof + Lane A umbrellas + Lane B majors) — not one submission that rewrites the internet.
Longer term, the Northstar — the destination we aim at but do not build first — is an industry graph: a shared, structured map of local businesses that many products and assistants could use. That is not version one. Version one earns the right to that graph by helping owners see how AI sees them today and fix what is broken, with their consent.
The product
A self-service experience — first as an interactive website, and later possibly inside a business’s own chatbot using the same engine — where an owner can ask: “Is my business discoverable by AI?”
The flow we are designing toward:
Someone who claims the business starts the check. We verify they are actually a stakeholder — for example by sending a six-digit code to an email already on file for that business — so random people cannot run deep audits on someone else’s shop. Once verified, we run a check that goes further than the old local-SEO habit of matching name, address, and phone (often called NAP) across directories. We also look at categories (what kind of business the listing says it is — plumber versus restaurant versus hotel) and relevance (whether those facts would help an assistant pick this business for a real customer question).
We then give the owner two scores, in plain language:
Presence answers: are you on the map? Are you listed with the major providers, and do your categories line up consistently?
Recommendability answers: if you are on the map, would an AI be likely to recommend you when someone asks about something in your genre?
Finally we show what to fix, ranked so the owner can do it themselves, and let them re-check later. A human expert can be an escape hatch. It must not be required for the product to work — humans will not scale to every small business.
Demand should mostly come from owners who start themselves (including from ads that send them to the tool), not from sales calls for every audit.
How people will use it (channel reality, September 2026)
We checked how major consumer chatbots behave with MCP as of late September / early October 2026. None of the big ones will automatically “handshake” with a random small business’s MCP server if nobody configured a connector. ChatGPT, Claude, consumer Gemini, Perplexity, Microsoft 365 Copilot, and similar products either need someone to add a connector or they only suggest connectors that are already in a curated directory. That is setup work — not something a typical shop owner gets for free by putting an MCP URL on the internet.
Those same assistants can still open and read a normal web page as text. Reading a page is not the same as speaking MCP to a server.
So for version one, the highest priority is an interactive web app the owner completes in a browser. Embedding the same engine in a business chatbot can come later. We should revisit MCP as a distribution pipe in about three months — not block the product on it now.
How we label kinds of businesses
A taxonomy here means the set of categories used to say what kind of business something is — plumber versus restaurant versus hotel — so listings, maps, and assistants know what queries it belongs to.
There is no single industry-wide taxonomy that Apple, Google, Bing, Yelp, and the listing aggregators all use. Each keeps its own list (or feeds someone else’s).
NAICS (North American Industry Classification System) is the U.S. government’s statistical system for classifying businesses by what they produce. It is good for reports and some B2B software. It is not how someone searches “plumber near me” on a map. SIC is the older system NAICS replaced for federal stats.
Google Business Profile has its own category IDs (often looking like gcid:…), available through Google’s Business Information API (categories.list, region US / language en). That official list is a flat catalog of roughly 4,050 leaves — not a hierarchy that matches our genre parents. Google does not publish a downloadable tree that says “these GCIDs belong under service-area trades.” A public September 2026 compilation of 4,053 US English GCID names (with third-party industry buckets used only for structure) is useful for research; those buckets are not Google’s official parents. We use GBP GCIDs as outbound presence-check maps and fix suggestions, not as our spine. In practice GBP is usually the richest owner-facing surface (categories, services, attributes, photos).
Apple Maps / Business Connect has its own primary category (one main label) plus optional extras; the public docs have been shifting as Apple folds Connect into a broader “Apple Business” offering. Owner-facing field labels are documented; partner API schemas are often gated. Mid richness versus GBP — good for place card truth, weaker 1:1 parity on things like GBP serviceItems.
Bing Places for Business is Microsoft’s local listing surface (feeds Bing / Microsoft local experiences). Categories use Bing’s own IDs (BPCategoryId + name); owners can attach multiple categories with one primary. The trusted-partner API is certificate-gated, not a public crawl API — field maps on this branch already treat Bing as the thinnest of a “Core 3” with GBP and Apple (docs/product/genre-packs/). Bing still matters for Presence: wrong or missing Bing categories are a real “are you on the map?” miss, especially where Copilot / Microsoft surfaces lean on Bing Places data. Do not pretend Bing’s category tree equals Google’s.
Yelp keeps its own hierarchy of category aliases (developer APIs expose the tree). Yelp is both a discovery surface consumers still use and a signal assistants and third parties often scrape or cite. Alias mismatch (e.g. cuisine or trade labels that do not line up with GBP) is a classic consistency failure. Yelp is not a map pin in the same way as GBP/Apple/Bing, but it belongs in outbound maps when we score “would an AI trust this entity’s type?”
Data aggregators / syndicators sit underneath many of those surfaces. Companies such as Data Axle (local listings / formerly Infogroup-class syndication), Localeze / TransUnion digital business profiles, Foursquare Places (place graph / POI metadata), and similar players push NAP and rich fields out to search engines, directories, navigation apps, and assistants. Owners often never touch them directly — a “listing management” vendor or the platform itself does. For our product that means: Presence is not only “did you claim GBP?” It can also be “does the aggregator-fed copy of you disagree with the claim you just made?” Aggregators are not a ninth genre parent; they are a distribution layer for facts. Which aggregators we check in v1 (if any) is still open — but ignoring them entirely would understate how local data actually spreads.
schema.org’s LocalBusiness types are an open vocabulary used widely on the web for structured data (the markup that helps search engines understand a page). That is the best spine for an AI-era product that also wants to publish clear web facts: owner-friendly genres inside our product, keyed to schema.org where they fit, optional NAICS when a statistical code helps, and outbound maps — translation tables — from our genres to Google, Apple, Bing, Yelp, and (where useful) aggregator category codes when we check presence or suggest fixes.
We should not treat any single platform or aggregator list as our internal source of truth. They drift and they disagree. Building those maps ourselves is normal; nobody publishes an official multi-way concordance. On this branch, genre-pack work already sketches a Core 3 field map (GBP richest → Apple mid → Bing thinnest); extend that pattern to Yelp aliases and aggregator feeds only where Presence scoring needs them.
This taxonomy approach is a hypothesis until we score real businesses and see where the maps break.
Why one audit checklist is not enough
A plumber who works out of a truck and a surfboard rental that serves tourists are both “local businesses,” but “are you on the map?” and “would AI recommend you?” mean different things for each. The product can keep one shared scoreboard — Presence and Recommendability — and change the rubric (what evidence counts and how heavily) by genre cluster.
What actually splits the audits:
Where the work happens (customer comes to you, you go to them, both, or a moving spot). What “place” means (a storefront with a public address, a service-area business that hides its address, a tourist landmark, or many locations). What decision data matters (menu, service list, booking, hours open right now, amenities). What kind of demand it is (emergency, same-day convenience, a planned night out, a trip, or B2B research). How trust shows up (reviews, photos, licenses). Seasonality. And whether the AI is helping a consumer or a business buyer.
We are proposing eight top-level clusters plus one cross-cutting flag as a starting cut — also a hypothesis until real audits teach us to merge or split. A GBP category pressure test (flat GCID catalog + third-party industry buckets for structure only) pushed three corrections: split bring-to-shop trades away from service-area trades; narrow beauty away from medical; demote hybrid from a parent to a flag.
Service-area trades (plumber, HVAC, cleaner, landscaper, pest control): the provider goes to the customer. Presence cares about looking like a real service-area listing rather than a fake storefront, having coherent service areas and categories; recommendability cares about matching the job type and the customer’s area, plus reliability signals. Do not put auto repair here.
Destination workshop (auto repair, tire shop, furniture repair, shoe repair, and similar bring-the-item-in trades): the customer brings the asset to a shop with a public address. Presence is storefront-like (real pin, hours, specialty categories); recommendability is trade-like (job match, make/model or specialty, reliability). GBP catalogs Automotive separately from Construction & Trades / Home & Garden — that split supports this parent.
Storefront retail: presence cares about address, hours, and category truth; recommendability cares about whether the assortment fits the ask and whether the shop is actually open.
Food service (restaurants, cafés, bars, food trucks): presence cares about specific cuisine or format categories (not a vague “Restaurant”), hours, and a current menu; trucks also need “where are you today.” Recommendability cares about occasion (date night versus quick lunch), price and vibe signals, dietary clarity, and fresh reviews. Fine dining, quick service, and trucks are parameters inside this cluster, not separate products.
Professional / office services (lawyer, accountant): less weight on “open right now,” more on specialty, credentials, and clear service language.
Beauty (salon, barber, day spa, nails — formerly the non-medical slice of “home and personal care”): booking path and services on the presence side; skill match, portfolio, and outcome-themed trust on recommendability. Dentist, medical spa, and other clinical work belong under healthcare, not here.
Hospitality and tourism (hotel, surf rental, tour): destination and amenity truth, seasonal hours, and conflicts between the business’s own facts and big travel sites; recommendability is trip-intent fit (“family,” “near the break,” pet-friendly) and seasonal availability. This stays a rubric parent even though GBP will not hand it to us as one bucket — lodging leaves (hotel, motel, B&B) group fairly cleanly; tourism activity scatters across Sports, Education, Arts, and related leaves. Our audits still need one trip-intent rubric.
Healthcare / regulated (only if we take them on — including dentist and medical spa): higher bar for accurate categories and careful claims. Veterinary is an open decision: in the GBP research dump, pets/vets sit in a separate Pets neighborhood (~59 leaves), not inside Health & Medical — decide whether vets ride with healthcare/regulated or get their own call if they are in scope.
Hybrid / multi-mode is not a ninth parent. It is a cross-cutting flag that can sit on any cluster (dine-in plus delivery on food service; showroom plus install on retail or destination workshop). Presence and recommendability apply the relevant mode slices; the query tells you which mode matters. Google encodes many of these as specific leaves (delivery restaurant, mobile caterer), not as a top-level grouping.
For version one, start with two cluster packs that prove the split matters — service-area trades and food service. If the commercial wedge is tourist businesses, hospitality can swap in for food service as the second pack. Destination workshop is the natural pack when the wedge is auto repair (as in the older UC-01 example).
How we build it (phases)
Phase 0 — Lock the promise. One sentence an owner gets without jargon. Who we speak to first. What “recommendable” means in plain words. Confirm the first shell is the web app. Success: we can say the promise in one breath without naming MCP or any other pipe.
Phase 1 — Trust gate. Claim the business, verify the stakeholder (email code first), keep data light, consent clear, and abuse limited. Success: only verified people reach the deep audit.
Phase 2 — Audit engine. This is the product: Presence and Recommendability with a thin, explainable rubric and cluster-specific weights — not a black box. Success: running the same business twice gives roughly the same scores, and the owner understands why.
Phase 3 — Self-serve loop. Results, a fix list, optional re-check, no mandatory human. Success: most people finish alone.
Phase 4 — Demand. Light campaigns that send owners to start their own audit. Track start → verify → complete → recheck. Success: we know acquisition cost and completion rate well enough to improve.
Phase 5 — Toward the graph. Only after audits produce structured facts owners consented to share that are worth composing into something bigger. The graph stays the Northstar; it does not gate version one.
Ship in this order: promise and wedge, verify gate, presence score, recommendability score, results and fixes, recheck, then ads. Do not wait on the industry graph or on a single technical protocol.
Version one is succeeding when an owner can finish alone, sees two clear scores with a real explanation, gets fixes they can do themselves, can re-check, and ads can point at the tool without a sales call on every lead.
Once Phase 0 is clear, work can run in parallel streams: promise and wedge language; trust and verify; presence checks; recommendability rubric; the web (and later chat) shell; growth metrics; and only later, graph composition.
How demand arrives (acquisition, not taxonomy)
This section is about who brings owners to the tool, not about which genre parent an audit uses. Genre clusters (destination workshop, beauty, hybrid as a flag, and the rest) stay a product-rubric question. Demand is orthogonal.
On main, distribution is framed as the hard part of local SaaS (docs/gtm/channel-strategy.md, echoed in NORTH_STAR.md and Canonical Strategy §10). The primary one-to-many wedge there is tourism / destination intermediates — tourism boards, DMOs, and visitor bureaus: one board contract unlocks many member endpoints, often funded by lodging-tax or visitor-experience budgets, upgrading board sites from weak link lists into structured destination surfaces (UC-02). A parallel path is self-serve SMBs in high-intent verticals. Chambers of Commerce show up as a secondary one-to-many path (CoC-branded portals, member-benefit framing). Agencies and SEO consultants are a partner layer that implements and sells — not the product center. BBB (Better Business Bureau) does not appear anywhere on main in that research pass; do not invent it into this SoT.
Soft link only: a tourism-board wedge prefers shipping the hospitality / tourism pack; an intermediate still needs category routing across members, which uses taxonomy as wiring, not as the definition of our parents.
This doc’s product flow already assumes many owners start themselves (including from ads). Main’s board-and-partner motion can coexist with that, but “ads / self-start only,” “intermediate-first,” or “both” is a real decision — not something the genre list answers.
Decisions still open
Ty still needs to lock: which cluster pack we ship first; whether version one is web-only or also embeds in a chatbot; which providers feed the Presence score (Core 3 GBP/Apple/Bing, plus Yelp and/or aggregators such as Data Axle / Localeze / Foursquare — or claim-only first); how we measure Recommendability without overclaiming (simple heuristics versus live checks inside assistants); where the “email on file” for verification comes from; free versus paid; what never goes to a human versus when escalation is allowed; whether veterinary rides with healthcare/regulated or gets its own call if in scope (GBP research dump: separate Pets bucket, ~59 leaves, not folded into Health & Medical); and whether v1 demand is ads/self-start only, intermediate-first (tourism boards / chambers / partners), or both.
What we are treating as hypotheses
Until we score real businesses, treat these as educated bets, not finished truth: that eight top-level clusters plus a hybrid flag is the right split (including destination workshop separate from service-area trades, and beauty separate from healthcare); that schema.org plus outbound maps to Google, Apple, Bing, Yelp, and selected aggregators is the right labeling spine — not any one platform’s leaf list as internal SoT; that two version-one packs are enough to prove cluster-specific rubrics; and that a web app beats an MCP-first distribution story for owners in the near term (revisit in about three months).
Nothing in this doc is locked until Ty says so. Amend this file when decisions land — do not start a second near-named document.