19 KiB
geolocal.io — Canonical Strategy
Status: Refined GM strategy (2026-07-18)
Source of truth for vision:docs/geolocal-copilot-conversation.md
This document: Condenses, stress-tests, and operationalizes that vision with current market evidence.
Supersedes: Fractured narratives in older GTM/investor/engineering docs where they conflict.
1. One-line company definition
geolocal.io is the business-owned MCP infrastructure that makes local service businesses discoverable, interpretable, bookable, and measurable inside every AI assistant — the Shopify Storefront MCP equivalent for the long tail of local services.
Not a destination site.
Not a chatbot sold to SMBs.
Not a Yelp/Google clone.
Infrastructure that AI agents use, that businesses own, that partners and tourism boards distribute.
2. The problem (evidence-checked)
2.1 Demand side: consumers already moved
- BrightLocal’s 2026 Local Consumer Review Survey: ~45% of consumers use AI tools for local business recommendations (ChatGPT is the leader among tools cited).
- BrightLocal and secondary coverage also reference a much lower prior-year figure (~6%); treat the directional leap as real, but do not overclaim a perfect YoY methodology match in investor materials without footnoting survey differences.
- X / practitioner discourse (2026): local discovery is collapsing into 1–3 named recommendations, not a page of links — “if you’re not named, you’re invisible.”
2.2 Supply side: AI is radically selective
- SOCi 2026 Local Visibility Index (≈350k locations / 2,751 multi-location brands):
- ChatGPT recommended ~1.2% of brand locations
- Gemini ~11%, Perplexity ~7.4%
- vs ~36% appearance in Google local 3-pack
- Locations recommended by ChatGPT skew high-trust (~4.3★ average).
- BrightLocal research: Yelp is a frequent source in AI local answers (~1/3 of searches in one study); listings/citations regained importance under LLMs.
Implication: Traditional local SEO is necessary but insufficient. Structured, machine-actionable presence is becoming the bottleneck.
2.3 Structural gap incumbents will not fill for SMBs
| Player | What they are building | What they are NOT building |
|---|---|---|
| Shopify | Storefront MCP live on every store (/api/mcp); UCP with Google; Catalog MCP |
MCP for non-Shopify local service businesses |
| Yelp | Official open-source Yelp MCP over Fusion AI — their data for agents | Business-owned endpoints businesses control |
| Maps Grounding / Merchant MCP (alpha); AI in Maps/Search; UCP commerce | Neutral, multi-tenant “own your AI presence” for long-tail SMBs | |
| Cal.com | Full booking MCP (create/reschedule/cancel/availability) | Discovery, story, genre primitives, tourism aggregation |
| OpenAI / Anthropic / others | Agent platforms + data licensing | Local service graph for the 98%+ long tail |
Thesis (validated): Incumbents build MCP for their platforms and their data. The long-tail service economy (Bob’s Garage, local charters, salons, tourism boards) has no Shopify-equivalent on-ramp. That vacuum is geolocal.io.
3. North Star (from the copilot conversation — preserved)
Make AI give the most relevant and helpful local recommendation — and fulfill it — for local service businesses.
Helpfulness (not mere relevance) is the optimization target: clear services, specialization, pricing signals, availability, booking path, trust, and repeatable success so AI engines prefer the same path next time.
4. Four business models (do not collapse these)
The copilot conversation defines four stacked models. Earlier repo docs mostly documented Model 1. That fracture is fixed here.
| # | Model | Who pays / who adopts | What geolocal provides | Horizon |
|---|---|---|---|---|
| 1 | Endpoint enablement | Individual SMBs (self-service) | Hosted multi-tenant MCP + /.well-known pointer + Self-Service Portal (SSP) + diagnostics + reports |
Day 0 |
| 2 | Intermediate enablement | Tourism boards, chambers, visitor bureaus | Aggregating MCP for destinations/members; member onboarding; municipal ROI narratives | V1 GTM wedge |
| 3 | Industry trust layer | Standards / partnerships / platforms | Quality primitives, decommissioning, genre norms — become a preferred discovery surface AI engines learn to trust | 12–24 mo |
| 4 | Service graph | Data products / licensing | Network of related services, specialties, demand signals, competitive intelligence (“Citysearch 2.0, AI-native”) | After critical mass |
Rule: Product decisions must state which model they serve. Do not build Model 4 features before Model 1–2 work.
5. Product definition (what we actually sell)
5.1 What we sell
AI-readiness infrastructure for local services:
- Hosted multi-tenant MCP — structured tools AI agents call (story, services, hours, booking path, related businesses, genre primitives).
- Dead-simple discovery pointer — business drops a tiny well-known JSON / path on their site that points at geolocal (Shopify pattern: endpoint on their domain, logic hosted).
- Self-Service Portal (SSP) — the real product surface for SMBs (see §5.2).
- Orchestration, not reinvention — Cal.com (booking MCP already exists), Stripe (payments). We do not replace them.
- Telemetry + optimization reports — “how often AI hit you, what they asked, what your site failed to answer, what local demand looks like.”
- Partner / intermediate surfaces — agencies, SEO shops, tourism boards, chambers.
5.2 Self-Service Portal (SSP) — primary product experience
From the founder narrative (canonical UX):
- Business lands on geolocal.io, describes business → genre-aware onboarding (auto repair vs surf shop vs charter).
- Initial site scrape → reflect back what the system already “sees.”
- Live “phone + chat” simulation — show ChatGPT-style flow: recommend the business, list services, book a time (e.g. Thursday 3pm transmission filter). Instant aha: pre-qualified, scoped, calendar-aligned demand.
- Install tiny pointer on site.
- Preflight / test run in-browser against their MCP — if AI can’t see services, they fix content or portal data before going live.
- Upstream guidance (discovery: GBP, NAP, FAQ, plain-text city+service) + downstream guidance (MCP content quality).
- Optional partner referral for website help.
- Weekly/monthly reports — interaction counts, intents, local demand signals.
Critical clarification (founder): We are not selling them a chatbot for their customers. The portal’s HTML chat is a test harness wired to their MCP so owners can see how external AIs will interpret them.
5.3 Genre-specific primitives
Generic get_business_info is MVP scaffolding only. Differentiator is genre systems:
- Auto: makes/models, specialties (Korean transmissions), emergency flags
- Beauty: services, duration, stylist notes
- Home services: service radius, emergency, estimate vs fixed price
- Tourism activities: seasonality, capacity, weather sensitivity
- Intermediate (tourism board): member directory + category routing
Roadmap must version genre packs, not only generic tools.
5.4 What we explicitly do NOT build (V1–V2)
- Consumer destination app
- Competing with Calendly/Cal.com/Square booking engines
- Competing with Stripe/Square payments
- Replacing Google Business Profile
- Building a Yelp review network
- Boiling the ocean on travel (AirBNB/Kayak space) — stay complementary
6. Positioning
6.1 Category
AI-readiness infrastructure / agentic local commerce infrastructure
Analogies that work in sales:
| Analogy | Why it lands |
|---|---|
| Shopify Storefront MCP for local services | Best single analogy; founders and partners get it immediately |
| Stripe for AI discovery/booking presence | Infrastructure, not UI |
| Twilio for local service tools | Agents call APIs; business doesn’t rebuild stack |
6.2 Messaging (use this)
- Owner: “Make every AI assistant understand and book your business.”
- Tourism board: “Turn lodging tax dollars into AI-discoverable local experiences your members own.”
- Agency/partner: “Add AI-readiness as a productized line — not another chatbot retainer.”
- Investor: “Neutral MCP layer for the 98%+ of local services AI currently skips, sold via self-serve + municipal distribution.”
6.3 Anti-messaging (do not say)
- “AI chatbot for your website”
- “Replace Google / Yelp”
- “SEO tool with AI features” (we can include discovery guidance, but category is infrastructure)
7. Go-to-market (refined)
7.1 Primary V1 wedge: tourism / destination intermediaries
Why (copilot + market logic):
- Hotel/lodging taxes create mandated promotion budgets.
- Boards often must spend and openly solicit ideas — confused buyers with money.
- One intermediate win → dozens/hundreds of endpoints (members).
- Travel has high AI referral growth narrative (industry reporting of strong AI travel referral growth 2024–2025).
- Physical brochure racks prove the intent for local discovery; geolocal is the AI-native equivalent.
Motion:
- Pick 1–2 destination markets (illustrative: coastal FL tourism board style markets already used in founder narrative).
- Sell board-level pilot: destination MCP + member onboarding SSP + simple ROI dashboard (member AI hits, category demand).
- Fund via tourism marketing budgets / innovation RFPs / “AI visitor experience” framing.
- Members get free/discounted endpoint for pilot period → convert to paid.
7.2 Parallel motion: self-service SMB endpoint
- Verticals for first genre packs: auto repair, beauty, home services (home services align with high-intent AI queries; tourism activities if destination wedge).
- Price band (direction from copilot pricing discussion, infrastructure framing):
- Starter ~$49/mo
- Core ~$129/mo (anchor)
- Pro ~$249/mo
- Position above DIY “chatbot” tools; below enterprise multi-location AI visibility platforms (SOCi class).
7.3 Secondary motion: partners (agencies, SEO, web shops)
- Not the product owner narrative; they are distribution and implementation.
- Partner marketplace when SSP shows “your website needs work — self-serve or partner?”
- Commission / wholesale pricing TBD after first 50–100 endpoints.
7.4 Explicit non-wedge
- Do not lead with “compete in national multi-location brand AI visibility” (SOCi’s world).
- Do not lead with pure e-commerce (Shopify already owns that MCP).
- Do not lead with pure B2B / non-local.
8. Competitive strategy
8.1 Who fills the vacuum?
| Actor | Likely path | Our response |
|---|---|---|
| Yelp | Become default AI source of truth via MCP + licensing | Offer business-owned data + booking path Yelp doesn’t control; complementary where agents need owner truth |
| Maps + Merchant + UCP | Stay complementary; deep GBP guidance; don’t fight Maps for UI | |
| Shopify | Expand agentic commerce | Partner pattern, don’t compete; copy their well-known endpoint on merchant domain UX |
| Cal.com / Square / Vagaro | Booking MCPs | Integrate; own discovery + genre + intermediate + graph |
| AI visibility SaaS (SOCi, BrightLocal AI tools) | Measure/optimize multi-location | Different buyer (enterprise brands); we own long-tail endpoints + tourism |
| Local agencies | Manual AEO/GEO retainers | Make them partners; productize what they can’t scale |
8.2 Moats (in order of build)
- Endpoint density in target geos/genres (critical mass for Model 3–4)
- Genre primitive quality AI engines prefer (helpfulness repeatability)
- Telemetry flywheel (demand signals → better recommendations → more attach)
- Intermediate contracts (tourism/chambers lock distribution)
- Trust/quality enforcement (decommission bad actors; freshness)
Freshness moat vs Foursquare: demand is continuous booking/intent from the service economy, not social check-ins.
9. Pricing principles (SMB self-serve)
| Tier | Monthly | Includes (V1 intent) |
|---|---|---|
| Starter | ~$49 | MCP endpoint, SSP test page, monthly report, basic diagnostics |
| Core | ~$129 | Full MCP, live SSP preflight, weekly reports, local demand insights, partner access |
| Pro | ~$249 | Multi-site, advanced telemetry, competitor signals, priority support |
Add-ons later: partner install, advanced analytics, multi-location.
Tourism board: separate enterprise/pilot pricing (per-destination + per-member), not SMB list price.
Do not price as chatbot usage; price as infrastructure + BI.
10. Success metrics (what GM tracks)
10.1 90 days
| Metric | Target (planning bar) |
|---|---|
| Public multi-tenant MCP (HTTP) live | Yes |
| SSP MVP (onboard + scrape reflection + preflight sim + pointer install) | Yes |
| Live endpoints | 100 |
| Tourism board / destination pilots | 1 signed LOI or pilot |
| Genre packs | 1–2 (e.g. auto + tourism activity) |
| Booking path | Cal.com link or Cal.com MCP orchestration (not rebuild) |
| Paying | First $ ARR even if small — proves willingness |
10.2 180 days
| Metric | Target |
|---|---|
| Endpoints | 1,000 |
| Genres | 3 |
| Related-businesses handshake in production | Yes |
| Intermediate dashboards | 3+ boards/chambers active |
| Agent telemetry usable for reports | Yes |
10.3 360 days
| Metric | Target |
|---|---|
| Endpoints | 10,000 |
| Major CoC / tourism partnerships | 3+ |
| Early DaaS / anonymized demand product | In market or beta |
| Evidence AI engines prefer geolocal paths in pilot geos | Case studies |
11. Product / technical priorities (strategy-level only)
Aligned to copilot “user stories before architecture”:
P0 — Prove Model 1
- HTTP MCP transport (agents are remote; stdio is not the product)
- Multi-tenant routing by business slug/domain
- SSP: signup → scrape → simulation → pointer → preflight
- Manifest / well-known generator
- Seed genres + real pilot businesses
- Cal.com path (redirect first; API/MCP next)
P1 — Prove Model 2
- Intermediate (tourism) MCP + member aggregation
- Board dashboard: members, hits, categories
- Pilot paperwork + data agreements
P2 — Feed Models 3–4
- Telemetry pipeline + owner reports
- Related businesses tool + quality rules
- Genre pack expansion
- Partner marketplace
Explicit defer: white-label everything, OAuth complexity beyond need, public developer platform, full DaaS, WordPress plugins (until attach rate demands them).
12. Risks (steelman)
| Risk | Severity | Mitigation |
|---|---|---|
| Yelp/Google become “good enough” AI local sources | High | Own business truth + booking + genre depth; intermediates; owner control narrative |
| SMBs don’t care until bookings prove ROI | High | SSP aha demo; tourism funded pilot; reports show demand; partner installs |
| MCP / discovery standards shift | Medium | Follow Shopify/Yelp well-known patterns; multi-path discovery |
| Market stats overstated in pitch | Medium | Cite SOCi/BrightLocal carefully; separate “multi-location study” vs “all SMBs” |
| Scope creep into chatbot / marketplace | High | North Star discipline; weekly “are we infrastructure?” review |
| Technical overbuild before SSP | High | No new tools without SSP path to value |
| Tourism procurement slow | Medium | Parallel self-serve SMB + short pilot contracts |
13. Document governance (end the fracture)
| Document | Role |
|---|---|
docs/geolocal-copilot-conversation.md |
Foundational founder narrative (historical truth) |
docs/strategy/CANONICAL_STRATEGY.md (this file) |
Operating strategy — team rallies here |
NORTH_STAR.md |
Short public/internal compass — must match this file |
docs/gtm/* |
Execution detail — must not invent a different company |
docs/investors/* |
External packaging of this strategy |
docs/engineering/* |
Build plan for P0–P2 above |
code/ |
Implementation of P0 |
Rule: If a doc conflicts with this file, this file wins until the GM revises it.
14. Near-term decisions for the leadership team
- Adopt this document as the single operating strategy.
- Rewrite NORTH_STAR.md to include four models + tourism wedge + SSP (not partner-dashboard-first only).
- Retire or mark “stale” any GTM doc that makes partner dashboard the only product or collapses four models to one.
- Lock V1 markets: choose destination pilot shortlist + 1 SMB vertical for genre pack.
- Lock pricing experiments: Core $129 anchor A/B later; don’t block launch on pricing perfection.
- Engineering charter: 30-day sprint = HTTP MCP + SSP spine + 10 real pilot endpoints (not more roadmap prose).
- Legal: tourism pilot data agreement + business ToS + MCP telemetry privacy (lightweight now, not full DaaS).
15. GM summary
The copilot version is the right company. The repo version got flattened into “partner MCP for SMBs with Cal.com” and lost the multi-model ambition, the SSP product, the tourism wedge, and the service-graph endgame.
Market evidence in 2026 supports the urgency:
- Consumers are using AI for local discovery at scale.
- AI recommendation is extremely selective.
- Shopify proved the pattern for commerce MCP on merchant domains.
- Yelp/Google/Cal.com prove MCP is real — and each protects their layer.
- Nobody is Shopify for local services’ AI presence.
Our job as a funded planning team: freeze this strategy, realign all docs, and execute the SSP + tourism pilot path until the vacuum starts filling under our brand — not Yelp’s.
Research sources (selected)
- BrightLocal LCRS AI trust / local consumer research (2026) — consumer AI usage for local recommendations
- SOCi 2026 Local Visibility Index / PR coverage — 1.2% ChatGPT recommendation rate, selectivity vs Google 3-pack
- Shopify Storefront MCP docs / industry writeups — merchant-domain MCP, agentic commerce
- Yelp Fusion AI MCP (github.com/Yelp/yelp-mcp) — official local data MCP
- Google Cloud / Merchant API MCP materials — Maps grounding, Merchant MCP
- Cal.com MCP docs — booking lifecycle tools for agents
- X discourse (2026) — agentic commerce, AI local discovery, SMB invisibility narrative
Stats should be re-verified at each external pitch; do not invent precision beyond sources.