AI sales dashboard for an operations team

Realeza Pets handles a high volume of recurring services (baths, grooming, packages) and already had a robust ecosystem (WooCommerce + Bookings + BuddyBoss + AI). The challenge was not a lack of data. It was a lack of commercial activation of that data in day-to-day operations.

Problem:

• Data scattered across orders, pets, schedule and points
• An operations team with no clear view of sales opportunities
• Upsell and retention happening manually and inconsistently
• No prioritization: who to approach, when and with which offer

Solution:

We built a system of smart commercial widgets inside the dashboard, turning the admin panel into a decision center.

• An interface designed for quick use in daily operations
• Widgets fed by data from WooCommerce, Bookings, GamiPress and AI
• Automatic prioritization of revenue opportunities

Technologies used:

• WordPress (Core)
• WooCommerce + Bookings
• GamiPress
• ACF
• Custom plugin: Realeza Customizations
• AI system (Realeza AI)

A commercial architecture built around opportunities

The solution was designed as a layer on top of the existing system. It doesn't replace tools, it orchestrates scattered data into clear commercial actions. I structured the logic around four pillars:

• Recurrence → identify inactive customers
• Ticket expansion → suggest an upsell at the right moment
• Predictability → the day's schedule as a sales driver
• Package conversion → identify customers who are ready for a bundle This avoided building new, complex flows and made use of the whole existing base, which lowered technical cost and sped up adoption. WordPress dashboard with the widgets

A simplified dashboard focused on sales

The focus was not a "pretty dashboard" but decisions in seconds. I removed the need to jump between screens (orders, bookings, profile). Anyone who works with WordPress and more complex systems knows what it's like to navigate across many tabs. I consolidated the customer's context at the moment of use and applied "next best action" logic instead of raw data. For example, when the day opens, the attendant already sees:

• who is coming in
• how much they have spent so far
• what they might buy today No manual digging required. Daily schedule window, already showing the customer profile and recommendations

Commercial features implemented

1. Inactive customers widget

• Identifies customers without recent recurrence
• Prioritizes reactivation (retention)
• Based on order history and frequency 2. Daily schedule with commercial intelligence
• List of the day's appointments
• Enriched with:
  • purchase history
  • pet data (weight, coat and so on)
  • owner context
• Includes automatic upsell suggestions via AI 3. Product recommendations (AI)
• Based on:
  • the pet's profile
  • consumption history
  • context captured by the AI
• Uses a semantic product index for performance 4. Package buyer identification
• Detects customers with multiple one-off purchases
• Filters out those who don't yet have active credits
• Flags a clear bundle sales opportunity 5. Smart customer context (AI)
• A consolidated snapshot of:
  • behavior
  • preferences
  • history
• Used to personalize the sales approach 6. Integration with the booking flow
• During booking:
  • shows the points balance
  • suggests add-ons (grooming, pickup and drop-off)
• The sale happens inside the operation, not outside it

Results

• The operations team became an active sales channel
• More upsell opportunities in daily service
• Less dependence on outside campaigns for revenue
• A scalable, data-driven commercial decision system
• Next steps:
  • automate contact triggers (WhatsApp / CRM)
  • evolve customer scoring
  • close the loop between recommendation and conversion