Add self-serve AI discoverability concept doc.
Capture mission, problem frame, product, channel reality, taxonomy, genre clusters, phases, and open decisions on planning-selfserve-audit.
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# Self-serve AI discoverability — concept
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**Branch:** `planning-selfserve-audit`
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**Status:** Working draft — conversation checkpoint 2026-10-01
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**Companion:** phased execution plan in [`SELF_SERVE_AI_DISCOVERABILITY_PLAN.md`](./SELF_SERVE_AI_DISCOVERABILITY_PLAN.md)
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**Related:** [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) on `planning-rev2` lineage
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This doc captures **ideas, concepts, and strategic direction** for the self-serve “Am I discoverable by AI?” wedge. It is not an inventory of old-repo artifacts.
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---
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## 1. Mission
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**How local business owners get AI to recommend them.**
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Owner-to-AI is the center of gravity. Proof (audits, scores, before/after) is a **selling point**, not the mission. The mission is recommendation in the agent era — not becoming another consumer destination, and not selling a protocol.
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---
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## 2. Problem frame
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| Layer | Stance |
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|-------|--------|
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| **Spine** | Agent-era local SMB discoverability |
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| **Pipes** | MCP (or other transports) are implementation choices, **not** the product |
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| **Northstar (not v1)** | An industry-wide graph others can use |
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| **Not** | A consumer browse site; rebuilding Calendly/Shopify; “we are an MCP company” |
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v1 earns structured, consented business truth by helping owners see and fix how AI sees them. The graph comes **after** that truth is worth composing.
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---
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## 3. Product
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A **self-service** experience — standalone web portal and/or inside a business’s own chatbot — where an owner asks:
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> “Is my business discoverable by AI?”
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### Target flow
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1. **Initiate** — Owner (or trusted helper / their AI) starts on an outcome promise, not plumbing.
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2. **Stakeholder verify** — e.g. six-digit code to a known email on file. Unverified users do not get the deep audit.
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3. **Deep audit** — Beyond NAP consistency across major providers into **categorical information** and **relevance**.
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4. **Two scores**
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- **Presence** — Are they on the map? (major providers + category consistency)
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- **Recommendability** — If they are, would an AI engine likely recommend them for genre-relevant queries?
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5. **Self-serve next steps** — Plain-language findings + ranked DIY fixes; optional re-check. Human expert path is an escape hatch, not the default.
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### Positioning
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As much business-owner-to-AI as possible. Human-in-the-loop resources exist but **will not scale** — design minimizes human dependency. Advertising and similar demand programs drive SMBs to **self-initiated** audits.
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---
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## 4. Channel reality (as of September 2026)
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Research snapshot (late Sep / early Oct 2026):
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- **No** major consumer chatbot (ChatGPT, Claude, Gemini consumer, Perplexity, M365 Copilot, etc.) will **auto MCP-handshake** with a random SMB’s server with **zero** owner/developer configuration.
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- MCP support that exists is **connector/config** (and sometimes curated directories / suggested connectors for listed partners) — not silent discovery of every business MCP as an inference engine.
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- Those same assistants **still read normal web pages** as web content. That is **not** an MCP session.
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### Product implication
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| Priority | Channel |
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|----------|---------|
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| **Highest now** | Interactive **web app** (owner completes audit in-browser) |
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| Optional parallel | Embed the **same engine** in the business’s chatbot shell |
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| **Revisit in ~3 months** | MCP / remote connectors as a distribution pipe — not the v1 product |
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Do not block the wedge on MCP adoption in consumer chat UIs.
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---
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## 5. Taxonomy stance
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There is **no** single canonical “SMB genre” taxonomy that Apple, Google, and Yelp snap to for discovery.
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| System | Role |
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|--------|------|
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| **NAICS** | Official US statistical / production classification ([census.gov/naics](https://www.census.gov/naics/)) — useful for B2B/industry codes, **not** how people search “plumber near me” |
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| **SIC** | Legacy; NAICS replaced it for federal stats |
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| **Google Business Profile** | Own category IDs (`gcid:…`); listable via Business Information API |
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| **Apple Business Connect / Maps** | Own primary + additional categories; data-spec historically published as categories CSV (docs moving under Apple Business rebrand) |
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| **Yelp** | Own hierarchical aliases; public via Fusion categories API |
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| **schema.org LocalBusiness** | Open, web-native type hierarchy — best **spine** for an AI-era product that also publishes structured web truth |
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**No official Apple ↔ Google ↔ Yelp concordance** exists. Multi-platform presence products build their own maps.
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### Recommendation (locked as direction, details iterative)
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- **Internal spine:** thin product genre layer in **owner language**, keyed to **schema.org LocalBusiness** where it fits
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- **Optional:** NAICS for statistical / B2B
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- **Outbound maps:** GBP categories, Apple categories, Yelp aliases — for presence pulls and fix guidance
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- **Do not** adopt Apple, Google, or Yelp as the internal source of truth
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**Hypothesis pending real audit data:** spine + maps quality will improve as we score real businesses; revise mappings from evidence, not from guessing.
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---
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## 6. Genre clusters (audit complexity)
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**One dual scoreboard, cluster-variant rubrics.** A plumber running from a truck and a surfboard rental for tourists are different products of “on the map” and “would AI recommend.”
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### Dimensions that split audits
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- **Where value is delivered** — customer comes to you vs you go to them vs hybrid vs mobile/seasonal spot
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- **What “place” means** — fixed storefront / hidden service-area address / destination landmark / multi-location
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- **Decision data** — menu, services list, inventory, booking, hours-open-now, price band, amenities
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- **Demand shape** — emergency, same-day convenience, planned occasion, tourist/trip, B2B research
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- **Trust signals** — review volume/velocity, photos, licenses, brand vs indie
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- **Seasonality / hours rigidity**
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- **B2C vs B2B** — who the AI is helping choose
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### Proposed clusters
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| # | Cluster | Presence emphasizes | Recommendability emphasizes |
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|---|---------|---------------------|----------------------------|
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| 1 | **Service-area trades** (plumber, HVAC, cleaner) | SAB vs fake storefront; service areas; category/services; no walk-in theater | Job-type relevance; coverage of searcher’s area; reliability/license themes |
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| 2 | **Storefront retail** | Address/signage/hours; category; attributes; multi-location | Assortment fit; open-now; real stock photos; neighborhood fit |
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| 3 | **Food service** (restaurants, cafés, bars, trucks) | Cuisine/format category (not generic Restaurant); hours; menu freshness; dine-in/takeout/delivery; truck location/events | Cuisine + occasion intent; price/ambiance; dietary clarity; review recency; open-now |
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| 4 | **Professional / office** | Category depth; service pages; citations; weaker “open now” weight | Specialty match; credentials; authority language |
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| 5 | **Home & personal care** (salon, dentist, medspa) | Booking path; services; hours; category | Skill match; booking friction; outcome-themed reviews |
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| 6 | **Hospitality & tourism** (hotel, surf rental, tour) | Lodging/activity category; amenities; destination geo; seasonal hours; OTA vs owned facts | Trip-intent fit; amenity truth; landmark relevance; seasonal availability |
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| 7 | **Healthcare / regulated** (if in scope) | Category + attribute accuracy; hours; locations | Condition/specialty match; acceptance; careful claims |
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| 8 | **Hybrid / multi-mode** | Storefront **and** service-area rules both correct | Mode implied by query; flag mismatches |
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**Restaurant sub-genres** (fine dining vs QSR vs food truck, cuisine types) are **parameters inside food service**, not separate products.
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### v1 pack order
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Start with **two** cluster packs that prove the split:
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1. Service-area trades
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2. Food service
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(If the commercial wedge is tourist SMBs, hospitality/tourism may swap with food service as the second pack.)
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**Hypothesis pending real audit data:** these eight clusters and their weight packs will be validated or merged once we score live businesses; do not treat the table as final ontology.
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---
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## 7. Phases (0–5)
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Aligned with [`SELF_SERVE_AI_DISCOVERABILITY_PLAN.md`](./SELF_SERVE_AI_DISCOVERABILITY_PLAN.md):
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| Phase | Focus | Success |
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|-------|--------|---------|
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| **0** | Lock promise, wedge genre, shell (web first) | Promise in one breath without naming a pipe |
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| **1** | Trust gate (claim + email code verify) | Verified only reach deep audit |
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| **2** | Audit engine — Presence + Recommendability | Repeatable scores; owner understands why |
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| **3** | Self-serve loop — findings, DIY fixes, recheck | Majority finish without human |
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| **4** | Demand — ads to self-initiated audits | Funnel rates known |
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| **5** | Toward industry graph | Only after consented structured truth is worth composing |
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**Build order:** promise → verify → presence → recommendability → results/fixes → recheck → then ads. Do not wait on the graph or MCP.
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---
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## 8. Open decisions
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Still for Ty to lock:
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- First **v1 genre / cluster pack** (trades vs food vs hospitality)
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- **Shell:** web-only v1 vs web + chatbot embed
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- Which **providers** feed Presence
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- How **Recommendability** is measured without overclaiming (heuristics vs live assistant checks)
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- Source of **email on file** for verify
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- **Free vs paid** audit
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- What **never** goes to a human; when escalation is allowed
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---
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## Hypotheses (explicit)
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Mark as **hypotheses pending real audit data** — revise from scored businesses, not from debate alone:
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1. Eight genre clusters are the right split (may merge/split).
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2. schema.org LocalBusiness + outbound maps to GBP/Apple/Yelp is the right taxonomy spine.
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3. Two v1 packs (service-area trades + food service) are enough to prove cluster-variant rubrics.
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4. Interactive web app outperforms MCP-first distribution for SMB owners in the near term (~3 month revisit).
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---
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## Capture note
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Drafted from strategy conversation 2026-10-01 (voice + chat). Push to `planning-selfserve-audit` so concepts are durable alongside the execution plan. Nothing is locked until Ty says so.
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