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 #3,689 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
Deeper Look - Food Scanner is a self-reported mobile and web application that allows users to scan food products and receive personalized recommendations based on their dietary needs, preferences, and values. The app evaluates product composition across multiple dimensions (nutrition, ingredients, processing, environment) and presents results transparently, without reducing them to a single score.
What changed
The author reports that the project was extended from an existing native Android/iOS application into a functional browser-based Progressive Web App (PWA) during a hackathon. This involved adapting the shared Kotlin Multiplatform codebase for web compatibility while preserving native functionality.
Single most important open question — the commercial due-diligence read
Is there any evidence of user adoption, revenue, or customer traction beyond the author’s own development and self-reported product demonstration?
What The Product Actually Is
The description states that Deeper Look is a food scanner app built with Kotlin Multiplatform and Compose Multiplatform. It supports Android, iOS, and web platforms through a shared codebase. Users can scan barcodes or upload images to search for products, configure personal preferences (dietary restrictions, allergens, nutritional goals, environmental values), and receive personalized evaluations based on these criteria.
The app integrates data from Open Food Facts and OpenPrices, evaluates several product dimensions independently, and shows which criteria matched or failed to match user preferences. It also includes features like history tracking, favorites, multilingual support, and tools for reporting incomplete information.
Evidence
- Built with Kotlin Multiplatform and Compose Multiplatform
- Supports Android, iOS, and web (PWA)
- Integrates Open Food Facts and OpenPrices data
- Evaluates nutrition, ingredients, processing, environmental info, and user preferences separately
- Allows users to define personal dietary profiles and receive tailored recommendations
Inference
- The app is designed for personalization rather than generic scoring.
Positioning & Claim Evolution
The author claims the app offers a more transparent and personalized approach compared to typical food scanners that reduce complex products to one score. It evaluates each dimension separately, adapts results to user preferences, and explains reasoning behind recommendations.
The name “Deeper Look” reflects the idea of looking beyond surface-level product information. The logo symbolizes complexity hidden inside everyday products, referencing dimensions such as Health/Nutrition, Environment, Processing, and User preferences (H/E/P/U).
Evidence
- Tagline: "Food scanners often give everyone the same opaque score. Deeper Look instead evaluates each dimension separately and gives a personalized, transparent recommendation."
- Name derives from idea of looking deeper into product composition
- Logo represents Mandelbrot-inspired fruit with H/E/P/U labels
Inference
- The positioning is centered on transparency, personalization, and user control over evaluation criteria.
Target Customer & ICP
The description does not explicitly define a target customer segment or ideal customer profile (ICP). It implies that users are individuals seeking personalized food insights based on dietary needs, allergies, ethical values, or nutritional goals. However, no specific demographic, persona, or market segment is identified.
Evidence
- Users configure profiles including diets, allergens, intolerances, nutrient goals, and environmental values
- App supports multilingual use
Inference
- Likely targets health-conscious consumers, those with dietary restrictions, or people interested in sustainable eating.
Business Model & Pricing Evidence
There is no evidence provided regarding a business model or pricing strategy. The description does not mention monetization methods, subscription plans, advertising, or any commercial structure beyond the app’s functionality.
Evidence
- No mention of revenue streams, pricing tiers, or monetization strategies
Inference
- Business model remains unknown; likely unproven at this stage.
Technical & Delivery Signals
The project is built using Kotlin Multiplatform and Compose Multiplatform, enabling cross-platform sharing of logic and UI. During a hackathon, the author extended the existing native app to support web via PWA, integrating features like camera scanning, browser storage (Room/SQLite), CORS handling, service workers, analytics, and responsive design.
Key technical challenges included platform-specific differences in camera preview, barcode detection, lifecycle behavior, navigation, persistent storage, and network security policies. AI tools such as Codex and GPT-5.6 were used to assist with architecture planning, debugging, and iterative development.
Evidence
- Built with Kotlin Multiplatform, Compose Multiplatform
- Supports Android, iOS, and web (PWA)
- Uses Open Food Facts and OpenPrices APIs
- Implements camera scanning, barcode detection, browser storage, analytics, and feedback tools
- Leveraged AI tools like Codex and GPT-5.6 for development
Inference
- Technical execution shows strong engineering capability in cross-platform development.
Traction & Maturity Signals
There is no evidence of user traction, customer adoption, or revenue generation. The project is described as a functional prototype developed during a hackathon, with no mention of active users, downloads, or market engagement.
Evidence
- Project submitted to OpenAI 2026 hackathon
- App is described as a real browser product but not yet launched publicly
- No data on user base, retention, usage metrics, or monetization
Inference
- Product is early-stage and lacks measurable traction.
Competitive Context
The description does not provide information about competitors or the competitive landscape. It does not reference other food scanning apps, nutritional tracking tools, or marketplace platforms that might compete with Deeper Look.
Evidence
- No mention of existing competitors or market positioning
Inference
- Competitor analysis is missing; unclear how this app differentiates in a crowded space.
Key Risks & Red Flags
Several key risks and red flags are evident:
- No Traction or Revenue: The product has no demonstrated user base, sales, or monetization.
- Unproven Market Demand: While the concept is described as useful, there’s no evidence of market validation.
- Limited Team Size: Only one team member (Alexis Cassion) is mentioned, raising concerns about scalability and execution capacity.
- Dependence on Public Databases: Reliance on Open Food Facts and OpenPrices means data quality and availability may be inconsistent or limited.
- Unclear Commercial Viability: No business model or pricing strategy is evident.
Evidence
- One-person team
- No revenue, customers, or traction data
- Relies on public databases with uncertain completeness
Inference
- High risk of failure without further validation and product-market fit.
Diligence Questions To Ask The Founders
- What is the current stage of user testing or feedback?
- Have you validated demand for this type of personalized food evaluation tool?
- How do you plan to monetize the app, if at all?
- What are your long-term goals for scaling beyond a single developer?
- How do you intend to ensure data accuracy and consistency from Open Food Facts/OpenPrices?
- Are there any legal or compliance considerations related to personalized dietary recommendations?
Investment/Partnership Verdict
There is no evidence of revenue, customer traction, or commercial viability beyond the author’s own development efforts. The project appears to be an early-stage prototype built during a hackathon, with no indication of market validation, user adoption, or monetization strategy.
Verdict Not evidenced as a viable investment or partnership opportunity at this time. Requires further validation of demand, traction, and business model before any strategic move can be considered.
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.