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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
Branch: planning-selfserve-audit
Status: Working draft — conversation checkpoint 2026-10-01
Companion: phased execution plan in SELF_SERVE_AI_DISCOVERABILITY_PLAN.md
Related: BUSINESS_PLAN.md on planning-rev2 lineage
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.
1. Mission
How local business owners get AI to recommend them.
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.
2. Problem frame
| Layer | Stance |
|---|---|
| Spine | Agent-era local SMB discoverability |
| Pipes | MCP (or other transports) are implementation choices, not the product |
| Northstar (not v1) | An industry-wide graph others can use |
| Not | A consumer browse site; rebuilding Calendly/Shopify; “we are an MCP company” |
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.
3. Product
A self-service experience — standalone web portal and/or inside a business’s own chatbot — where an owner asks:
“Is my business discoverable by AI?”
Target flow
- Initiate — Owner (or trusted helper / their AI) starts on an outcome promise, not plumbing.
- Stakeholder verify — e.g. six-digit code to a known email on file. Unverified users do not get the deep audit.
- Deep audit — Beyond NAP consistency across major providers into categorical information and relevance.
- Two scores
- Presence — Are they on the map? (major providers + category consistency)
- Recommendability — If they are, would an AI engine likely recommend them for genre-relevant queries?
- Self-serve next steps — Plain-language findings + ranked DIY fixes; optional re-check. Human expert path is an escape hatch, not the default.
Positioning
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.
4. Channel reality (as of September 2026)
Research snapshot (late Sep / early Oct 2026):
- 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.
- 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.
- Those same assistants still read normal web pages as web content. That is not an MCP session.
Product implication
| Priority | Channel |
|---|---|
| Highest now | Interactive web app (owner completes audit in-browser) |
| Optional parallel | Embed the same engine in the business’s chatbot shell |
| Revisit in ~3 months | MCP / remote connectors as a distribution pipe — not the v1 product |
Do not block the wedge on MCP adoption in consumer chat UIs.
5. Taxonomy stance
There is no single canonical “SMB genre” taxonomy that Apple, Google, and Yelp snap to for discovery.
| System | Role |
|---|---|
| NAICS | Official US statistical / production classification (census.gov/naics) — useful for B2B/industry codes, not how people search “plumber near me” |
| SIC | Legacy; NAICS replaced it for federal stats |
| Google Business Profile | Own category IDs (gcid:…); listable via Business Information API |
| Apple Business Connect / Maps | Own primary + additional categories; data-spec historically published as categories CSV (docs moving under Apple Business rebrand) |
| Yelp | Own hierarchical aliases; public via Fusion categories API |
| schema.org LocalBusiness | Open, web-native type hierarchy — best spine for an AI-era product that also publishes structured web truth |
No official Apple ↔ Google ↔ Yelp concordance exists. Multi-platform presence products build their own maps.
Recommendation (locked as direction, details iterative)
- Internal spine: thin product genre layer in owner language, keyed to schema.org LocalBusiness where it fits
- Optional: NAICS for statistical / B2B
- Outbound maps: GBP categories, Apple categories, Yelp aliases — for presence pulls and fix guidance
- Do not adopt Apple, Google, or Yelp as the internal source of truth
Hypothesis pending real audit data: spine + maps quality will improve as we score real businesses; revise mappings from evidence, not from guessing.
6. Genre clusters (audit complexity)
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.”
Dimensions that split audits
- Where value is delivered — customer comes to you vs you go to them vs hybrid vs mobile/seasonal spot
- What “place” means — fixed storefront / hidden service-area address / destination landmark / multi-location
- Decision data — menu, services list, inventory, booking, hours-open-now, price band, amenities
- Demand shape — emergency, same-day convenience, planned occasion, tourist/trip, B2B research
- Trust signals — review volume/velocity, photos, licenses, brand vs indie
- Seasonality / hours rigidity
- B2C vs B2B — who the AI is helping choose
Proposed clusters
| # | Cluster | Presence emphasizes | Recommendability emphasizes |
|---|---|---|---|
| 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 |
| 2 | Storefront retail | Address/signage/hours; category; attributes; multi-location | Assortment fit; open-now; real stock photos; neighborhood fit |
| 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 |
| 4 | Professional / office | Category depth; service pages; citations; weaker “open now” weight | Specialty match; credentials; authority language |
| 5 | Home & personal care (salon, dentist, medspa) | Booking path; services; hours; category | Skill match; booking friction; outcome-themed reviews |
| 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 |
| 7 | Healthcare / regulated (if in scope) | Category + attribute accuracy; hours; locations | Condition/specialty match; acceptance; careful claims |
| 8 | Hybrid / multi-mode | Storefront and service-area rules both correct | Mode implied by query; flag mismatches |
Restaurant sub-genres (fine dining vs QSR vs food truck, cuisine types) are parameters inside food service, not separate products.
v1 pack order
Start with two cluster packs that prove the split:
- Service-area trades
- Food service
(If the commercial wedge is tourist SMBs, hospitality/tourism may swap with food service as the second pack.)
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.
7. Phases (0–5)
Aligned with SELF_SERVE_AI_DISCOVERABILITY_PLAN.md:
| Phase | Focus | Success |
|---|---|---|
| 0 | Lock promise, wedge genre, shell (web first) | Promise in one breath without naming a pipe |
| 1 | Trust gate (claim + email code verify) | Verified only reach deep audit |
| 2 | Audit engine — Presence + Recommendability | Repeatable scores; owner understands why |
| 3 | Self-serve loop — findings, DIY fixes, recheck | Majority finish without human |
| 4 | Demand — ads to self-initiated audits | Funnel rates known |
| 5 | Toward industry graph | Only after consented structured truth is worth composing |
Build order: promise → verify → presence → recommendability → results/fixes → recheck → then ads. Do not wait on the graph or MCP.
8. Open decisions
Still for Ty to lock:
- First v1 genre / cluster pack (trades vs food vs hospitality)
- Shell: web-only v1 vs web + chatbot embed
- Which providers feed Presence
- How Recommendability is measured without overclaiming (heuristics vs live assistant checks)
- Source of email on file for verify
- Free vs paid audit
- What never goes to a human; when escalation is allowed
Hypotheses (explicit)
Mark as hypotheses pending real audit data — revise from scored businesses, not from debate alone:
- Eight genre clusters are the right split (may merge/split).
- schema.org LocalBusiness + outbound maps to GBP/Apple/Yelp is the right taxonomy spine.
- Two v1 packs (service-area trades + food service) are enough to prove cluster-variant rubrics.
- Interactive web app outperforms MCP-first distribution for SMB owners in the near term (~3 month revisit).
Capture note
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.