TapWait AI: Smarter Restaurant Operations and Marketing with Open-Weight AI
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle What I Built Every restaurant has two important jobs: delivering excellent customer service and attracting customers. But managing service requests, coordinating waitstaff, and creating engaging promotions can b
Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 馃捀
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
Every restaurant has two important jobs:
delivering excellent customer service and attracting customers. But managing service requests, coordinating waitstaff, and creating engaging promotions can be challenging when these activities rely on disconnected tools and manual processes.
TapWait AI brings restaurant operations and AI-powered marketing together in one connected hospitality platform.
Built for restaurants, bars, caf茅s, and food service businesses, TapWait AI combines a Flutter mobile application for waitstaff, a web dashboard for managers, real-time backend services, and AI-assisted promotional campaigns.
Waiters use the Flutter mobile application to receive and manage customer service requests. Requests move through a defined workflow, allowing staff to track their status and respond to customer needs.
Socket.IO supports real-time communication, while Firebase Cloud Messaging supports push notifications, including background notifications where supported.
The goal is to help restaurant teams coordinate service more effectively and reduce the friction between receiving a request and responding to it.
The web dashboard gives restaurant managers a central place to oversee service requests, monitor their progress, and coordinate restaurant operations.
Instead of depending entirely on verbal communication, managers can use a connected digital workflow to stay informed about service activity.
The dashboard also provides the foundation for managing AI-generated promotions and marketing campaigns within the same platform.
MenuBoost AI: Turn Restaurant Menus into Marketing Opportunities
A restaurant's menu is more than a list of dishes. Every menu item and special offer can become an opportunity to attract customers.
MenuBoost AI uses Google's hosted Gemini API to generate promotional content based on saved menu items, prices, selected offers, marketing tone, and target platforms.
Restaurant managers can create content for channels such as WhatsApp, Facebook, and Instagram without having to start from a blank page.
By grounding generated content in restaurant-specific information, MenuBoost AI aims to make promotional creation more relevant and practical for businesses without dedicated marketing teams.
WhatsApp Marketing and Customer Engagement
TapWait AI incorporates WhatsApp Cloud API workflows to support restaurant messaging and promotional campaign management.
The vision is to help businesses turn menu updates and special offers into engaging customer communications while managing campaign workflows through the platform.
Campaign Guard: Combining Gemma with Practical Validation
Generating attractive promotional content is only part of the challenge. Restaurants also need to ensure that promotions reflect their actual menus and offers.
Campaign Guard explores using Gemma 3 270M through Ollama to review saved promotional content against its associated menu and offer information.
The workflow retrieves the saved promotion, supplies its content and source information to the local model, and returns a structured review containing a summary, potential issues, and suggested corrections.
For stronger reliability, the broader validation approach combines model-assisted review with deterministic application logic. Prices, discounts, and offer conditions should be checked against trusted data and explicit business rules rather than relying on a language model alone.
This creates an opportunity to explore how lightweight open-weight AI models can support practical quality checks within an existing business application.
Step 1: Customer Calls a Waiter Step 2: The Request Reaches the Restaurant's waiters
Step 3: The Waiter Receives and Accepts the Request
Step 4: The Customer Receives Assistance
Step 6: AI-Powered Marketing with MenuBoost
Experience TapWait AI: A connected hospitality platform bringing customer service, waiter coordination, management visibility, and AI-powered marketing together.
Mobile App: https://github.com/Y-elv/tapwait-waiter BAckend: https://github.com/Y-elv/tapwait-waiter-api
The platform is evolving through integration testing, workflow improvements, and feedback from potential users. My focus is on making its core features dependable and useful in real hospitality environments.
Open innovation gives developers the flexibility to explore different AI models, combine approaches, and choose suitable deployment strategies for specific tasks.
For TapWait AI, this means exploring a hybrid AI architecture:
This is particularly interesting for hospitality businesses because AI should be useful, adaptable, and grounded in actual business information. A local model can also provide an alternative inference path when the application is configured to run it locally, although its suitability depends on hardware, latency, and review quality.
The wider opportunity is to make AI-assisted business workflows accessible to organizations that need practical tools rather than complex, disconnected systems.
The business opportunity is to offer a connected platform that helps restaurant teams manage service requests, coordinate staff, prepare promotions, and organize customer engagement without juggling multiple disconnected applications.
Potential business models include monthly subscriptions, tiered plans based on business needs, and higher-tier offerings for multi-location operators and additional integrations.
The focus is on creating measurable value through more organized workflows, easier campaign preparation, and better visibility into restaurant operations, rather than promising unverified revenue increases.
A neighboring startup has expressed interest in using TapWait AI and has been encouraging about the idea.
That response has motivated me to keep improving the platform and focus on practical business needs rather than building features without a clear purpose.
My next step is to move toward structured real-world use, gather feedback, observe how the workflows perform, and identify where TapWait can save time or make restaurant service and marketing easier.
This feedback will help guide product improvements and validate the platform's value in an actual business setting.
TapWait AI explores using Gemma 3 270M through Ollama for Campaign Guard, a local AI review workflow within a broader restaurant marketing platform.
The feature retrieves a saved promotion and compares its generated content with the associated menu and offer information. Gemma produces a structured review intended to highlight potential inconsistencies and suggest corrections.
The approach combines open-weight AI experimentation with application-level validation, recognizing that prices, discounts, and explicit offer rules require reliable checks against trusted data.
The broader idea is to demonstrate how a lightweight local model can complement a hosted generation model within a practical business workflow.
TapWait AI's vision is simple: connect restaurant service, intelligent marketing, and practical AI in one platform built for the needs of modern hospitality.