Archive position — measured, not model output
1 like on Devpost
506 of the 7,856 archived projects have more likes, and 1,758 share exactly 1 — so this project's #1,571 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
Offomo is an AI-powered shopping assistant that claims to find, verify, and share promo codes and deals to help users save time and money while shopping online. It was submitted as a project to the OpenAI 2026 hackathon.
What changed
The description provides no evidence of prior development or changes; this is a self-reported project submitted for a hackathon.
Single most important open question
Is there any evidence of user adoption, revenue, or traction beyond the hackathon submission?
The analysis is based entirely on the author-supplied, unverified project description. There is no evidence of revenue, customers, pricing, or product usage. The project appears to be in early development and lacks any demonstrated commercial traction.
What The Product Actually Is
The description states that Offomo is an AI-powered shopping assistant. It claims to find, verify, and share promo codes and deals to help users save time and money when shopping online. The author also notes it was built for the OpenAI 2026 hackathon.
Evidence
- The project is described as an AI-powered shopping assistant.
- It is said to find, verify, and share promo codes and deals.
- It targets online shoppers looking to save time and money.
- It was submitted to a hackathon.
Inference The product is likely a web or mobile application that leverages AI to surface discounts and deals for users. However, no technical details, functionality, or user interface are described.
Positioning & Claim Evolution
The description states Offomo’s tagline: “AI-powered shopping assistant that finds, verifies, and shares the best promo codes and deals, helping you save time and money every time you shop online.”
Evidence
- The positioning is centered on AI-driven deal discovery.
- It emphasizes time and cost savings for users.
- It targets online shoppers.
Inference The product positions itself as a tool to simplify shopping by automating deal hunting. However, there is no evidence of how it differentiates from existing coupon or deal sites, nor any indication of its evolution from an idea to a product.
Target Customer & ICP
The description states that Offomo helps users save time and money when shopping online.
Evidence
- It targets online shoppers.
- It aims to help users save time and money.
Inference The customer is likely a general consumer who shops online and seeks discounts. However, no segmentation or specific buyer personas are provided.
Business Model & Pricing Evidence
There is no evidence in the description of how Offomo intends to monetize its service or what pricing model it uses.
Evidence
- No mention of revenue streams.
- No pricing information provided.
Inference It is possible that Offomo may be a freemium product, or it could be monetized through affiliate commissions from deals shared. However, this is speculative and not evidenced.
Technical & Delivery Signals
The author states the project was built with: ai, application, coupon, deals, discount, flight, hotels, mobile, next.js, rag, shopping, supabase, travel, vercel.
Evidence
- Built using Next.js, Supabase, Vercel.
- Uses AI and RAG (Retrieval-Augmented Generation).
- Includes features related to coupons, deals, shopping, and travel.
Inference The product likely uses AI for content retrieval or curation. It may be a web/mobile application with backend infrastructure built on modern tools. However, no details about architecture, scalability, or delivery mechanism are provided.
Traction & Maturity Signals
There is no evidence of traction or maturity beyond the hackathon submission.
Evidence
- Submitted to a hackathon.
- Team size is 2.
- No mention of users, customers, revenue, or product usage.
Inference The project appears to be in early development. There is no indication of user adoption or commercial viability.
Competitive Context
There is no evidence provided about the competitive landscape or how Offomo compares to existing players.
Evidence
- No mention of competitors.
- No differentiation strategy described.
Inference It likely competes with existing deal and coupon sites, but no specific positioning or competitive advantage is stated.
Key Risks & Red Flags
Key Risks
- No evidence of revenue, users, or product traction.
- Team size is small (2 members), which may limit execution capability.
- No clear monetization strategy.
- Submitted to a hackathon — suggests early-stage development.
Red Flags
- Lack of any commercial evidence.
- No mention of partnerships, customers, or market validation.
- No indication of product-market fit or scalability.
Diligence Questions To Ask The Founders
- What is the current stage of development beyond the hackathon?
- How does Offomo plan to monetize its service?
- Are there any early users or pilot customers?
- What differentiates Offomo from existing deal and coupon platforms?
- How does it verify promo codes and deals?
- What is the roadmap for product development and scaling?
Investment/Partnership Verdict
Verdict Not evidenced.
The description provides no evidence of commercial traction, revenue, or user adoption. It is a self-reported hackathon project with no indication of viability or scalability. The lack of any data on users, customers, or monetization makes it impossible to assess the potential for investment or partnership at this stage.
Confidence Low. This analysis is based entirely on a thin self-description and lacks any external validation or evidence of product-market fit, revenue, or user engagement.
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.