Description
https://about.doordash.com/en-us/news/introducing-ai-and-agent-powered-voice-ordering
Short term Goal is to show our national partners how we can take phone orders using AI with a human fallback element.
Great example!! Must try!
https://foreva.ai/
Possible flow chart: https://lucid.app/lucidchart/dd444fe8-75ff-446f-a747-9fa39314a3e7/edit?viewport_loc=-345%2C-849%2C2601%2C1353%2C0_0&invitationId=inv_12e8b589-0ae8-4103-9e6e-2e724b77f2c3
Important:
Think of quickest way to show proof of model, just placing an order for takeout. This means it could be direct through RDS logic and then we will refine to be stand alone product. (El sarape)
Ensure entire API driven.
Ensure that this can be used as a entirely separate product that can be hosted outside RDS logic, but RDS logic can be brain.
Think that we should be able to tie into any system and use this system, like a seperate management widget and everything. Like someone could integrate their own POS and connect their own phone lines etc.
Can we use existing calls and orders to train the model quickly? https://docs.google.com/document/d/1j6Mit8qbKeHfqr4akfoEgcM-FNFooA6-C-mfOrGwpCA/edit?usp=sharing
Do we create the entire order and then text a link to customer with cart built and they can pay easily on phone or select to pay in store?
Build an app in zapier for our clients to use. Connect their own twilio account and add numbers?
Incorporating the time of day for orders and an enhanced conversational AI that collects options, sub-options, and requirements dynamically as the customer speaks, the layout of the AI phone ordering system would be as follows:
Use existing calls and orders to map things out and train the model? We may be able to use the existing calls to formulate the entire model and structure.
AI Phone Ordering System Pricing Options or DL/AM clients
Introduction:
Introduce a monetization strategy for the AI phone ordering system utilizing a Software as a Service (SaaS) model for clients.
Provide clients with the flexibility to monetize the system in various innovative ways.
Direct to Restaurant Approach:
Minimal Overhead: Focus on charging only for credit card processing fees while leveraging software tips as the main revenue stream.
Comprehensive Support Services: Offer to manage customer support and chargeback processes for a fee or by utilizing customer tips, providing a seamless experience for restaurants.
Targeted Strategy: Specifically designed for direct dealings with restaurants and national partners, avoiding the traditional fast business model complexities.
Strategic Highlights:
The AI phone ordering system's first pricing strategy is tailored for client flexibility within a SaaS framework, allowing them to monetize effectively.
The second strategy emphasizes direct relationships with restaurants, offering a streamlined and cost-efficient model with minimal charges outside of credit card fees.
Allow us to set up restaurants to incentivize other merchants to refer, so sub affiliate paying. List a referrer that will then have all orders tagged and set a pay per order price. This will allow us to build reports to pay out referrer.
SaaS Model for Clients:
Flat Fee Approach: Clients are charged a standard fee for each AI phone order, enabling them to set their own pricing strategies for end users.
Diverse Revenue Options for Clients:
Option to charge end customers a percentage of their order total.
Ability to include a software tip, which could be added to taxes and fees.
Freedom to implement a flat fee per order.
Possibility to use customer tips as a form of payment for accessing active menus and services, benefiting both restaurants and national partners directly.
Allow for clients to set up restaurants to incentivize other merchants to refer, so sub affiliate paying. List a referrer that will then have all orders tagged and set a pay per order price. This will allow us to build reports to pay out referrer.
Objectives:
To ensure a profitable and adaptable monetization framework for the AI phone ordering system that benefits our clients, restaurants, and national partners.
Aim to provide a range of monetization options that are both user-friendly and tailored to meet diverse client needs.
Building System as its own stand alone product Concept (OrderBuy.ai
This is the overall structure of how the service should be created
Technical Architecture Explanation
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"Orderbuy.ai" is designed as a scalable, modular system that enables voice ordering capabilities for merchants, integrating seamlessly with various Point of Sale (POS) systems like Toast POS, Clover POS, and Square POS, DeliverLogic, Chowly, Checkmate, Olo. At its core, the architecture facilitates easy adoption by third-party platforms and their merchants, supporting multiple integration methods similar to Stripe Connect's model.
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