grokbot_ty
1a912c96fc
Explain specialized terms inline, drop machine outline tone, keep mission through hypotheses in one SoT file.
140 lines
12 KiB
Markdown
140 lines
12 KiB
Markdown
# How local businesses get AI to recommend them
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**Branch:** `planning-selfserve-audit` (from `planning-rev2`)
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**Status:** Working draft from conversation on 2026-10-01
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**Related:** [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) · conventions in [`CONVENTIONS.md`](./CONVENTIONS.md)
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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.
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---
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## What we are trying to do
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The mission, stated plainly: **help local business owners get AI to recommend them.**
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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.”
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---
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## The problem we are solving
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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.
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That **discoverability in the agent era** is the spine of the strategy. How assistants connect to tools and data (for example through MCP, the Model Context Protocol — a technical way for an AI app to call outside tools) is just plumbing. We may use MCP later, or other approaches, or a mix. The pipe is not the product.
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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.
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---
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## The product
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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?”**
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The flow we are designing toward:
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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).
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We then give the owner two scores, in plain language:
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**Presence** answers: are you on the map? Are you listed with the major providers, and do your categories line up consistently?
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**Recommendability** answers: if you are on the map, would an AI be likely to recommend you when someone asks about something in your genre?
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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.
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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.
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---
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## How people will use it (channel reality, September 2026)
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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.
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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.
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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.
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---
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## How we label kinds of businesses
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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.
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There is no single industry-wide taxonomy that Apple, Google, and Yelp all use. Each platform keeps its own list.
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**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.
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**Google Business Profile** has its own category IDs (often looking like `gcid:…`), available through Google’s business APIs. **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. **Yelp** has its own tree of category aliases, published through Yelp’s developer APIs.
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**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, and Yelp categories when we check presence or suggest fixes.
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We should not treat Apple’s, Google’s, or Yelp’s lists as our internal source of truth. They drift and they disagree. Building those maps ourselves is normal; nobody publishes an official three-way concordance.
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This taxonomy approach is a **hypothesis** until we score real businesses and see where the maps break.
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---
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## Why one audit checklist is not enough
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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**.
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What actually splits the audits:
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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.
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We are proposing eight clusters as a starting cut — also a hypothesis until real audits teach us to merge or split:
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**Service-area trades** (plumber, HVAC, cleaner): 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.
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**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.
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**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 eight separate products.
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**Professional / office services** (lawyer, accountant): less weight on “open right now,” more on specialty, credentials, and clear service language.
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**Home and personal care** (salon, dentist, medspa): booking path and services on the presence side; skill match and outcome-themed trust on recommendability.
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**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.
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**Healthcare / regulated** (only if we take them on): higher bar for accurate categories and careful claims.
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**Hybrid** (dine-in plus delivery, showroom plus install): both storefront and service-area rules have to be right; the query tells you which mode matters.
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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.
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---
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## How we build it (phases)
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**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.
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**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.
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**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.
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**Phase 3 — Self-serve loop.** Results, a fix list, optional re-check, no mandatory human. Success: most people finish alone.
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**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.
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**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.
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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.
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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.
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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.
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---
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## Decisions still open
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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; 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; and what never goes to a human versus when escalation is allowed.
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---
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## What we are treating as hypotheses
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Until we score real businesses, treat these as educated bets, not finished truth: that eight clusters is the right split; that schema.org plus maps to Google, Apple, and Yelp is the right labeling spine; 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).
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Nothing in this doc is locked until Ty says so. Amend this file when decisions land — do not start a second near-named document.
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