PayMyDine helps restaurants automate reservations, ordering, kitchen, payments and team workflows, optimize every table and guest journey, and connect CRM and analytics in one live operating picture. AI-powered insights surface what needs attention, helping reduce manual work and wait times, speed service, improve table turnover and guest experience, and support revenue growth.
Each area solves a real restaurant workflow on its own. Connected together, reservations, tables, orders, kitchen, payments, guests, teams, analytics and integrations create the context AI can use to compare periods, flag unusual movement, support forecasts and point management toward what deserves attention next.
Use 6 AI-assisted actions: ask questions, receive a daily briefing, flag unusual movement, compare periods, support forecasts and identify the next metric to investigate.
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Monitor 6 live views: dashboard, open orders, table status, floor maps, dine-in or takeaway and current restaurant activity.
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Plan 7 reservation and seating tasks: calendar, timeline, availability, walk-ins, table assignment, guest flow and multiple floors.
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Move orders through 7 controls: waiter entry, KDS, preparation status, ready status, menu management, modifiers and sold-out items.
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Cover 8 guest-ordering and payment moments, including table QR, mobile menu, pay-at-table and 3 split methods: equally, by item or by shares.
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Track 9 decision metrics: revenue, guests, average check, table turnover, sales by time, best sellers, payment mix, forecasting and profitability.
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Coordinate 5 people controls: role workspaces, permissions, shifts, performance context and staff activity.
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Use 6 growth tools: profiles, loyalty, offers, campaigns, feedback and retention, with visit context available for relevant follow-up.
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Connect 4 system types - POS, accounting, delivery and payments - then add central reporting, shared menus, inventory, food-cost and purchasing context.
See the workflowOwners, managers, service staff, kitchen, reservations and finance use focused views while table, order, guest, timing and payment context stays connected. Management can then use PayMyDine AI to move from a signal to the source data behind it.
Start with revenue, guests, tables, reservations, kitchen status and top-selling items. Then compare periods, surface unusual movement and open the source view behind the answer.
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Profiles, loyalty, offers, campaigns, feedback and retention signals can remain connected to the visit history that created them, giving management better context for future decisions.
See the guest growth workflow
PayMyDine AI works from the operating picture already available in the platform. Ask about revenue, guests, average check, table turnover, sales timing, best sellers, payment mix, forecasts or profitability, then trace the answer back to the source data.
Move from a headline metric into the restaurant context behind it instead of manually checking separate reports for every possible explanation.
Use AI-assisted daily briefings, anomaly flags, period comparisons and forecast support while keeping the source metric, comparison window and human decision visible.
The same context follows the journey from guest or staff action through preparation, service and payment into reporting. PayMyDine AI can then help summarise the period, compare results, surface unusual movement and identify which metric or source view management should inspect next.
A guest scans a table QR, reception seats a booking, service staff opens an order or a manager changes a table.
Table, guest, order, timing, notes and permissions travel with the action instead of being entered again.
Service staff sees service work, kitchen sees preparation work and managers see exceptions and live status.
Preparation, ready status, handoff and payment continue from the same restaurant context, including three bill-split methods.
Revenue, guests, average check, table turnover, sales timing, payment mix, forecasts and profitability become available for reporting, comparisons, anomaly review and AI-assisted questions.
Guests, service staff, kitchen and payment workflows use different screens, but their activity contributes to the same operating picture. That gives management clearer source data for reporting, comparisons and AI-assisted investigation.
Guests see the amount, available method and confirmation while staff keep bill status attached to the table.
Let guests split equally, assign ordered items or divide the total by shares without starting a separate checkout.
Service staff sees service status, kitchen sees preparation status and management sees exceptions from the same ticket context.
Guests scan the table QR code to open the mobile menu, place the order and complete payment while PayMyDine keeps the table, order, bill and payment status connected for staff.
PayMyDine keeps daily interfaces practical for the people doing the work while collecting the shared restaurant context needed for reporting and AI-assisted review. The result is not another isolated chatbot — it is assistance built around the operation already happening in the restaurant.
Move from a high-level metric to the source view behind it, compare periods, surface unusual changes and decide which floor, channel, category or item needs investigation.
The table QR journey keeps guest, table, order and payment context connected through checkout. Those interactions also become structured operating data for reporting and AI-assisted review.
Start with the restaurant you already run. Choose which product areas remove duplicate work, define who can see each control and preserve the context that management needs for reporting, comparisons and AI-assisted questions.
Supported POS, accounting, delivery and payment connections can contribute useful source data to central reporting, location comparison, inventory and profitability workflows. The more complete the permitted source picture is, the more useful AI-assisted comparisons and investigations can be.
PayMyDine
Bring your roles, floor structure, guest journey, current systems and the questions management asks every day. We will map the smallest practical setup and show which signals AI can summarise, compare, flag or use to support forecasts from the data you actually have.
