Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #7,495 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
Vaise is a self-reported project submitted to the OpenAI 2026 hackathon. The description states it enables guests at restaurants to scan a QR code, chat with an AI waiter, order food naturally, and track their meal. Kitchen and staff receive live table-specific orders, improving speed and accuracy.
What changed
This is a hackathon submission; there is no evidence of prior development or commercial activity beyond the project description provided by the author.
Single most important open question
Is there any evidence of actual customer adoption, revenue, or traction that would suggest this is more than a proof-of-concept?
Analysis basis
The entire analysis is based on the self-reported, unverified description supplied by the caller. No third-party verification, archived history, or independent sources are available.
What The Product Actually Is
The description states that Vaise allows guests to scan a QR code, chat with an AI waiter, order naturally, and track their meal. Kitchen and staff receive live table-specific orders, improving speed and accuracy.
- Product functionality: Guests use a QR code to interact with an AI waiter for ordering and tracking.
- Technology stack: The project is built with React, Next.js, Node.js, TypeScript, Tailwind CSS, Vercel, OpenAI (GPT-5), and PWA capabilities.
- Delivery method: It appears to be a web-based application or progressive web app.
Note
No further technical details are provided beyond the technology stack and high-level functionality. The description does not clarify whether this is a full platform, MVP, or prototype.
Positioning & Claim Evolution
The author states that Vaise enables guests to scan a QR code, chat with an AI waiter, order naturally, and track their meal. Kitchen and staff receive live table-specific orders, improving speed and accuracy.
- Positioning: A restaurant ordering solution using AI and QR codes.
- Claim evolution: The project is positioned as a way to improve restaurant efficiency through automation and natural language interaction.
Note
There is no evidence of prior positioning or evolution in claims. This is a single self-reported statement from the hackathon submission.
Target Customer & ICP
The description states that Vaise is for guests at restaurants who scan a QR code, chat with an AI waiter, order naturally, and track their meal.
- Primary customer: Restaurant guests.
- Secondary users: Kitchen staff and restaurant operators who receive live table-specific orders.
Note
No evidence of segmentation or targeting beyond the general use case. No indication of specific verticals, geographies, or customer personas.
Business Model & Pricing Evidence
The description does not include any information about pricing, monetization, or business model.
- Business model: Not evidenced.
- Pricing: Not evidenced.
Note
There is no mention of how the product would be sold, who pays, or what revenue streams are envisioned.
Technical & Delivery Signals
The project is built with:
- Frontend: React, Next.js, Tailwind CSS
- Backend: Node.js
- AI integration: OpenAI (GPT-5)
- Hosting: Vercel
- Other: PWA, QR code support, TypeScript
- Delivery method: Progressive Web App (PWA) with QR code functionality.
- AI integration: Uses GPT-5 for conversational AI.
Note
No evidence of production deployment, scalability, or performance metrics. The project is described as a hackathon submission.
Traction & Maturity Signals
The description does not include any evidence of traction, adoption, or maturity.
- Traction: Not evidenced.
- Maturity: Not evidenced.
Note
No mention of users, customers, revenue, or usage data. The project is described as a hackathon submission.
Competitive Context
The description does not provide information about competitors or market context.
- Competitive landscape: Not evidenced.
- Market positioning: Not evidenced.
Note
There is no evidence of awareness of existing solutions in the restaurant tech space.
Key Risks & Red Flags
- Unverified claims: All statements are self-reported and unverified.
- No traction or revenue: No evidence of adoption, users, or monetization.
- Hackathon project: The project was submitted to a hackathon, suggesting it may be a prototype or proof-of-concept.
- Limited team size: Only two team members are mentioned.
Note
There is no indication of risk mitigation strategies or business sustainability beyond the project description.
Diligence Questions To Ask The Founders
- What is the current stage of development, and how far along is this from a production-ready product?
- Have you conducted any user testing or gathered feedback from restaurant owners or guests?
- Are there any plans to monetize or scale this solution beyond the hackathon?
- How do you plan to differentiate Vaise from existing restaurant ordering platforms?
- What are your go-to-market strategies and customer acquisition plans?
Note
These questions are based on the limited information provided in the description.
Investment/Partnership Verdict
The description states that Vaise is a project submitted to the OpenAI 2026 hackathon, with no evidence of traction or commercial viability beyond its self-reported nature.
- Investment potential: Not evidenced.
- Partnership opportunity: Not evidenced.
Note
This is a very early-stage concept. There is insufficient evidence to assess investment or partnership viability. The project lacks any demonstration of real-world adoption, revenue, or scalability.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.