Nº 001 · The Action Layer for Multi-Location Operations

Every system measures.
Only Opaxa acts.

AI agents that turn the systems you already run into decisions, at every location, every day.

Nº 002 · The Thesis

The systems know what happened. They do not know how you operate.

Your point of sale knows yesterday's sales. Your payroll system knows who worked. Neither knows what drives your first hour, what the lease requires, what the labor contract allows, or what the manual says when equipment fails at open. That operating context is what turns a report into the right decision. It lives in documents nobody reads and in the heads of a few experienced managers, and it is only captured by doing the work inside real operations.

Nº 003 · The Proving Ground

Built inside the operation, not in a lab.

Opaxa was founded and built by operators, inside a multi-brand operator with more than 40 distinct restaurant locations across 7 major U.S. airports: demand that moves with the flight board, union labor, and a different lease at almost every location. Every agent was shaped by the managers who use it.

40+

Locations

7

Major U.S. airports

500+

Decisions a month

Above your systems, not instead of them.

Opaxa works with the point of sale, payroll, scheduling and accounting you already run. Nothing to rip out, nothing for your teams to relearn.

Nothing acts without permission.

Every agent starts at Suggest. You decide, agent by agent, what it may do on its own.

Your data stays yours.

Encrypted, used only to run your operation, and never used to train any model.

Nº 004 · The Platform

Reads everything you run. Reasons across all of it. Acts inside your rules.

Everything You Run

  • Point of sale
  • Payroll
  • Scheduling
  • Timekeeping
  • Accounting
  • Purchasing
  • Inventory
  • Distributor invoices
  • Recipes and menus
  • Vendor contracts
  • Leases
  • Equipment manuals
  • Guest reviews
  • Flight schedules
  • Event calendars
  • Weather
  • …and whatever runs your operation

Reasoning and action layer

Decisions Right Now

    Illustrative examples across operator types.

    Nº 005 · Agents

    Agents for every kind of decision.

    Demand and Labor

    The right people, at the right hour, within the rules.

    Demand

    Concessions

    A schedule change added two departures to the 6:40 am bank. Opening-hour demand revised up 18% at the three nearest locations.

    • Demand
    • Labor
    • Shift Coverage
    • Labor Compliance

    Cost

    Every dollar going out, checked and recovered.

    Menu and Cost

    Concessions

    The street-pricing cap holds the burger at a fixed price while beef rose 9%. A portion adjustment restores the cost target.

    • Menu and Cost
    • Inventory and Ordering
    • Invoice Audit
    • Vendor Credits

    Revenue

    Every dollar coming in, priced and promoted to earn.

    Pricing and Mix

    Restaurants

    The top-selling item carries a 38% food cost against a 30% target. A $0.50 increase closes most of the gap with minimal volume risk.

    • Pricing and Mix
    • Markdown and Sell-Through
    • Promotions
    • Briefing

    Controls

    Cash, payroll, equipment and obligations, kept in order.

    Cash and Loss

    Convenience

    A cash variance recurred on the same shift at one site three times this month. Transactions routed to the district manager.

    • Cash and Loss
    • Payroll Audit
    • Equipment
    • Contracts and Leases

    New agents deploy continuously. The roster grows with every operation we run.

    Nº 006 · Governance

    Autonomy is earned, not assumed.

    Suggest

    The agent recommends. A person decides. Every agent starts here.

    Confirm

    The agent prepares the action. A person approves it.

    Auto

    The agent acts inside the limits you set and reports what it did.

    An agent moves forward only after it has been measurably right on your own data, and only when you promote it, agent by agent and location by location. Every decision is logged with the data and reasoning behind it, and every correction you make shapes the next one.

    The Agentic Loop

    Every decision teaches the next.

    1. MeasureEvery recommendation is checked against what actually happened.
    2. ShareAgents learn from each other: the forecast shapes the schedule, the schedule shapes the order, and every result shapes the next forecast.
    3. ImproveThe longer Opaxa works inside your systems, the better it knows your operation and the more accurate it gets.

    Nº 007 · Where It Goes

    From better decisions to operations that run themselves.

    Today

    Agents recommend.

    Across demand, labor, menu, purchasing and cash, agents put the right decision in front of the right manager. Managers decide.

    Next

    Agents act.

    Inside limits you approve, agents carry out the routine and read the documents that run the operation: leases, contracts, manuals.

    Horizon

    Operations run themselves.

    The operation runs itself between decisions. The manager's job becomes the decisions only a person should make.

    And the manager gets back to the part of the job they love: hospitality.

    Nº 008 · Built For

    Wherever demand is shaped outside the four walls.

    Built for operators running many locations on thin margins, from the food businesses we know best to every operation that runs on locations, labor and inventory.

    Restaurants

    Multi-unit groups, franchisees, multi-concept

    Demand shaped by traffic, weather and the menu.

    Twelve locations ran over their labor target last week. Three schedules rebuilt.

    Concessions

    Airports, stadiums, arenas, parks, cinemas

    Demand shaped by flights, events and attendance.

    Two departures added to the 6:40 am bank. Opening demand revised up 18%.

    Foodservice

    Campus, corporate and healthcare dining

    Demand shaped by the calendar, office attendance and patient counts.

    Friday office attendance averages 38%. Cafe staffing reduced to match.

    Hotels

    Hotel restaurants, outlets and resorts

    Demand shaped by occupancy and group bookings.

    A 300-room group moved to Thursday. Every outlet's demand shifted a day earlier.

    Convenience

    Convenience stores with food, travel centers

    Demand shaped by road traffic, fuel and time of day.

    Hot case waste above 12% at two sites. Pars lowered and production split.

    Retail

    Specialty and multi-location retail

    Demand shaped by foot traffic, season and promotions.

    A seasonal line is 29 points behind its sell-through plan. Markdown recommended at eight stores.

    Nº 009 · Contact

    Stop measuring. Start acting.

    Tell us about your operation. We respond within two business days.

    The founders read every submission. We respond within two business days.