Franchizor

Franchizor is an AI guidance layer that keeps franchisors on top of their network and gives franchisees the context they need to operate confidently. Inspired by midday.ai, we pair proactive copilots with minimal, branded dashboards so every unit gets the signal it needs without digging through clutter.

Why we’re building this

Most franchise systems struggle with:

• Fragmented data and slow insight cycles
• Overloaded dashboards that don’t guide users
• Generic “chat with your data” that puts the cognitive load on users
• Franchisees lacking context on how their unit compares to the network
• Playbooks that stay theoretical because insights don’t turn into action

Franchizor fixes this by making AI guidance the default, dashboards minimal and personal, and network monitoring collaborative between franchisors and franchisees.

Core team philosophy

• AI is here to guide, not to push content in users’ faces.
• Proactive assistance:
  • The AI should offer relevant suggestions proactively so users don’t have to guess what to ask.
  • Chat isn’t the primary interaction model for discovery—users often don’t know what to ask.
• Personalized, minimal dashboards:
  • Each user/company brands their own dashboard—“make it yours”—while keeping the UI minimal and non‑intrusive.

Product at a glance

• Franchisor view:

  • Pull aggregated and per‑franchise data.
  • Manage the network, roll out playbooks, and run leaderboards/benchmarks.
  • Enforce guardrails and track compliance at scale.

• Franchisee view:

  • Minimal, branded dashboard for each unit or group of units.
  • Proactive insights, tasks/quests, and collaboration with the franchisor.
  • Benchmarks against peers and shared goals.

How it works

1. Understand the franchise context

• AI parses docs, POS exports, spreadsheets, and APIs to ingest any operational signal.
• Tool-calling pipelines clean and normalize inputs so franchisors don’t babysit ETL work.

2. Decide what matters right now

• Copilots reason in small steps, asking follow-up questions instead of waiting for perfect prompts.
• The system selects the metrics, thresholds, and narratives that are most relevant for each role.

3. Generate tailored dashboards

• JSON widget specs are assembled on the fly based on what the user says is most important to monitor.
• Layout, filters, and highlight states are optimized so dashboards surface only the next best actions.

4. Deliver and iterate

• Insights arrive directly inside the dashboard or via proactive nudges, with suggested playbooks attached.
• Collaboration features let franchisors benchmark units, compare cohorts, and follow up on progress.

Example dashboard spec (AI‑generated)

{
  "theme": "default",
  "title": "Franchise",
  "logo": "franchise.svg",
  "widgets": {
    "expenses": {
      "filters": {
        "above": 100,
        "less": 1000,
        "from": "beginning of year",
        "to": "last week"
      },
      "position": { "x": 0, "y": 0 }
    },
    "income": {
      "filters": {
        "from": "beginning of year",
        "to": "last week"
      },
      "position": { "x": 1, "y": 0 }
    }
  }
}

Note: While we use JSON internally, the landing page focuses on proactive insights and the resulting dashboards—not raw JSON.

Example AI insights

• Franchisee — Reduce food cost variance: “Your COGS is 4.1% above the network median this week. Cut prep waste in the 14:00–16:00 slot or renegotiate supplier X for dairy.”
• Franchisee — Staff to demand: “Saturday 12–14h had 18% longer queue times. Adding 1 cashier yields +€1.7k/week projected revenue and 9% faster service.”
• Franchisee — Menu conversion: “Bagel add‑on acceptance is down 9% vs last month. Test a €0.50 price drop or bundle with coffee to recover margin.”
• Franchisor — Network benchmark: “12 units show >8% loyalty churn. Share the coastal region playbook (+6.1% AOV) and track uplift over 4 weeks.”
• Franchisor — Ops compliance: “6 units missed last month’s HACCP checks. Auto‑schedule refresher training and notify area managers.”
• Franchisor — Promo optimization: “Q3 promo works in coastal regions (+6.1% AOV) but lags inland. Localize creatives and adjust discount depth to 12%.”

Tech stack

• Next.js App Router, React 19
• Tailwind CSS v4, tw-animate-css
• Typography: Geist (via next/font)
• Icons: lucide-react
• UI primitives: Radix Popover
• Scheduling CTA (embed): Cal.com
• Charts: Tremor UI (planned integration)
• Database & Auth: Supabase (PostgreSQL with Row Level Security)
• TypeScript with strict mode

Project structure

• app/
  • layout.tsx, page.tsx, globals.css
  • [locale]/ — internationalized routes
  • api/ — API routes
• components/
  • header-animation.tsx — animated insights header (themed, once‑on‑view)
  • cal-cta.tsx — reusable Cal.com CTA
  • ui/ — UI primitives (Radix components)
• lib/
  • utils.ts — cn() utility
  • supabase/ — Supabase client and helpers
    • server.ts — server-side client
    • client.ts — browser client
    • middleware.ts — auth middleware
    • types.ts — TypeScript types
    • auth-helpers.ts — authentication helpers
    • franchise-helpers.ts — franchise data helpers
• public/
  • static assets

Getting started

Prerequisites

• Node.js 20+
• npm, pnpm, or yarn
• Supabase account and project

Install and run

• Install dependencies:
  • npm install
• Set up environment variables:
  • Create .env.local with your Supabase credentials:

    NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
    NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key
    

  • See SUPABASE_SETUP.md for detailed setup instructions
• Start dev server:
  • npm run dev
• Build:
  • npm run build
• Start production server:
  • npm start

Local configuration (optional)

• Cal.com:
  • The CTA component defaults to the namespace “franchizor” and link “hugo-demenez/franchizor”.
  • Override per usage:
  • UI layout defaults to month_view and can be changed via ui/config props.

Security and deployment

• Dedicated tenancy or on‑prem deployment when required by compliance-heavy customers.
• Principle of least privilege and encrypted secrets as baseline.
• Auditability and governance features planned (see roadmap).

Terminology

• Franchisor — the company or person who owns the brand, systems and grants rights to operate under those standards.
• Franchisee — the individual or company that buys the right to operate a franchised unit.
• Franchise (noun) — the business arrangement or the branded unit/chain itself. Example: “We offer a coffee franchise.”
• Franchised (adjective) — describes a business or location operating under a franchise agreement. Example: “a franchised location.”
• Master franchise — the party holding rights to sub‑franchise an entire territory.
• Area developer — a franchisee that opens multiple units in a territory.
• Franchise system / network — the whole collection of franchised units plus the franchisor’s supporting systems.

Roadmap

Near‑term

• Integrate Tremor UI and a JSON‑to‑widgets renderer.
• Expand AI insight library and suggestions framework.
• Collaboration basics: comments, mentions, shared views.
• Leaderboards and benchmark templates.

Enterprise readiness

• Tenant isolation enforcement and policy tests.
• Dedicated tenancy / on‑prem deployment path.
• SSO (SAML/OIDC), RBAC, and scoped API keys.
• Audit logs, retention, and governance hooks.

Data & connectors

• Connectors for common POS/ERP/CRM systems.
• Data quality checks and schema validation.
• Cached queries and incremental refresh.

UX & accessibility

• Reduced‑motion support and keyboard‑first flows.
• Theming and white‑label controls.
• Localization and multi‑currency.

Contributing

• TypeScript, strict mode.
• Keep the UI minimal and guidance‑first.
• Prefer composition and JSON‑driven widgets over bespoke pages.
• Document new insights: when it triggers, suggested actions, expected uplift.

License

TBD. All rights reserved for now.

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Franchizor — AI dashboards for franchises. Proactive. Minimal.