plate.

Inspiration

The idea for plate. started when I lived in New Zealand and saw apps like First Table offering discounted early-bird bookings. I liked the concept, it felt limited. One fixed time slot, one fixed discount, and not much flexibility for restaurants. When I came back to Lithuania, I thought: we need something like this, but smarter. Restaurants don’t just struggle at 5 PM - they struggle throughout the entire day whenever traffic drops. That’s where the idea evolved: instead of copying First Table, I thought of building a system where discounts change automatically every 10 minutes based on real demand. The inspiration came from seeing a good idea and realizing it could become something far more dynamic and effective

What it does

Plate brings true yield management and dynamic pricing to the restaurant industry - something that hotels and airlines have used for decades to maximize revenue, but restaurants have never fully adopted. In hotels and flights, prices constantly adjust based on demand, timing, and occupancy. This is why a plane seat at 6 AM costs €40 and the same seat at 6 PM costs €140. Restaurants face the very same economics: high fixed costs, limited capacity, and massive differences between peak and off-peak demand. Yet most restaurants still use static pricing, leaving large parts of the day unprofitable. Plate changes this

Using AI predictions powered by reservation patterns, POS behavior, and historical demand trends, Plate updates restaurant discounts every 10 minutes. When the restaurant is quiet, the system increases the discount to fill otherwise empty tables. When traffic rises, the discount automatically decreases to protect margins. This creates the optimal balance between occupancy and profitability. And financially, this logic is powerful: with high fixed costs, it is far more profitable to sell a meal at -40% than to sell nothing at all, because every additional customer contributes to covering overhead. Plate automates this entire process

Restaurants see a real-time dashboard where AI-driven deals adjust dynamically throughout the day. Customers browse these live offers, claim a deal, and receive a QR code they can use immediately or reserve for later by paying a small fee. When they arrive, staff scan the QR before seating, and the system applies the correct dynamic discount

The result is a smarter, hands-off revenue engine for restaurants: higher table occupancy, fewer dead hours, better margin control, and optimized revenue across the whole day - not just peak times. Plate takes the proven principles of airline and hotel yield management and finally makes them practical, simple, and automatic for restaurants

How we built it

Plate was built using Lovable, an AI-powered development platform that made it possible to turn the idea into a working prototype quickly. The core infrastructure - authentication, routing, database models, and UI components - was generated through Lovable, allowing the main focus to be placed on designing the dynamic pricing logic and the user experience for both restaurants and customers

On top of this foundation, a lightweight dynamic pricing engine was implemented. It updates discounts every 10 minutes to simulate real yield management, similar to the systems used in hotels and airlines. Although the current MVP does not yet connect to live reservation or POS data, the architecture is structured so that these integrations can be added easily in the future. This will allow dynamic pricing to eventually adjust based on real-time occupancy, historical patterns, and spending behavior

Two complete flows were built: • A restaurant dashboard that displays AI-influenced discount recommendations, allows deal management, offers basic analytics, and handles QR check-ins • A customer-facing experience that lets users browse live deals, claim a discount, and receive a QR code that can be used immediately or reserved for a later time slot by paying a small fee

Challenges we ran into

One of the biggest challenges was translating the complexity of yield management into something that feels simple and intuitive for restaurants. Dynamic pricing can easily become confusing, so a lot of effort went into making the discount logic clear, predictable, and easy to understand at a glance. Creating a convincing simulation of AI-driven pricing without live integrations was also a challenge. Because the MVP does not yet use real reservation or POS data, the system needed to behave in a way that still demonstrated how dynamic pricing would work in practice

Accomplishments that we're proud of

One of the biggest accomplishments is creating a functional end-to-end system that clearly demonstrates how dynamic pricing can work for restaurants. The prototype shows the full journey - AI-influenced discount updates, deal creation, customer reservation, QR code generation, and staff check-in - all flowing together in a simple, intuitive way. It proves that yield management, something typically used only by airlines and hotels, can be adapted into a format that restaurants can understand and actually use.

Another achievement is building both perspectives - the restaurant dashboard and the customer experience - in a way that feels realistic and aligned with how restaurants operate in real life. The QR check-in flow works smoothly, the dynamic updates feel natural, and the overall interface communicates the value of dynamic pricing clearly, even without full integrations yet

What we learned

I learned that dynamic pricing fits the restaurant industry much more naturally than I expected. Once I understood how high fixed costs are staff, rent, utilities - it became clear that filling even a few extra tables during slow periods can make a real financial impact. That helped me see why yield management works so well in airlines and hotels, and why restaurants could benefit from something similar

I also learned how important it is to make complex ideas feel simple. Dynamic pricing can easily confuse people, so I had to think carefully about how to present AI-driven discounts in a way that restaurants can understand instantly. Designing both the restaurant flow and the customer experience taught me how much good UX matters, especially when the concept itself is new

What's next for plate.

Next, I want to evolve Plate from a functional MVP into a system that uses real data to power truly smart dynamic pricing. The first step is integrating with actual reservation systems so the AI can see real-time occupancy and upcoming bookings. After that, connecting POS data will allow Plate to understand spending patterns, peak check-in times, and customer flow - all of which will make the pricing engine much more accurate

I also want to introduce a more advanced AI model that adjusts discounts not only based on demand but also on factors like day of the week, weather, location, and seasonal trends. This will bring Plate closer to the level of yield management that hotels and airlines use

On the customer side, the next step is improving the deal discovery experience, making QR reservations smoother, and integrating real payment flows for reserving time slots

Long-term, Plate could grow into a full dynamic pricing platform: a tool that helps restaurants optimize revenue automatically throughout the entire day, and a marketplace where customers can find real-time dining deals powered by real demand. The goal is to take the proven principles of yield management and make them practical, simple, and valuable for the restaurant industry

Built With

• javascript
• lovable
• next.js
• node.js
• typescript