Mija AI

The company appears to be a two-person team building a mobile-first assembly companion powered by AI, using GPT-5.6 for step-aware guidance and visual verification. The product is described as a lightweight PWA deployed on Vercel, with a demo mode and live AI mode. It targets users assembling flat-pack furniture, aiming to reduce confusion in manuals through structured steps and visual checks.

• The description states that Mija AI is a mobile assembly companion.
• It guides users through 12 focused steps for assembling a Nordic bedside cabinet.
• Each step includes:
  • Parts to use
  • Timing
  • Tools required
  • Exploded visual
  • Contextual help
  • Visual confidence check
• The experience is delivered via a lightweight mobile web app built with JavaScript, HTML, and CSS.
• It uses Vercel Functions for AI endpoints.
• In Live AI mode, it uses the OpenAI Responses API with GPT-5.6.
• A Demo Mode allows judges to test the full experience without an account or API key.

Note

The product is described as a prototype, not a commercial offering. No evidence of actual users, customers, or revenue is provided.

• The description states that Mija AI was inspired by turning confusing manuals into calm, checkable steps.
• It aims to reduce stress during assembly by:
  • Making the next action visible
  • Providing confidence checks
  • Keeping the builder's judgment intact
• The product is positioned as a mobile-first companion, not a replacement for human decision-making.

Inference The positioning implies a focus on usability and clarity in complex tasks, but no evidence of market validation or user feedback beyond the hackathon context.

• The description states that Mija AI is intended for users who build flat-pack furniture.
• It targets people who experience confusion with manuals, particularly around:
  • Dense diagrams
  • Unfamiliar hardware
  • Vague instructions

Note

No evidence of a defined customer persona or segment beyond the general use case of furniture assembly.

• The description does not state any pricing model or business model.
• It mentions two modes:
  • Demo Mode: No account or API key required.
  • Live AI Mode: Requires an OpenAI API key.
• There is no mention of monetization, subscriptions, or paid features.

Inference The product appears to be a prototype with no commercial model evident. The presence of two modes suggests potential for future monetization but no evidence of it.

• Built as a progressive web app (PWA) using:
  • JavaScript
  • HTML5
  • CSS3
• Deployed on Vercel
• Uses OpenAI API (specifically GPT-5.6) for AI reasoning and visual verification.
• Backend uses Vercel Functions and Supabase PostgreSQL.
• The team used Codex to accelerate development.
• No evidence of production infrastructure, scalability, or security practices beyond the demo.

Inference The tech stack suggests a lightweight prototype. No evidence of enterprise-grade delivery or long-term technical strategy.

• The project was submitted to the OpenAI 2026 hackathon.
• It includes a public repository with local setup instructions and sample data.
• Demo Mode is available for judges without account creation.
• No evidence of:
  • Customers
  • Revenue
  • User engagement metrics
  • Product usage data

Inference The project is at an early stage, likely a prototype or proof-of-concept. No signs of traction or product-market fit.

• The description does not mention any competitors.
• It implies that current solutions for flat-pack furniture assembly are confusing or lack clarity.
• No evidence of existing products in this space or competitive differentiation.

Inference There is no competitive analysis or positioning against other tools. The market context is not described.

• The product is a hackathon submission, not a commercial offering.
• No evidence of:
  • Revenue
  • Customers
  • Product-market fit
  • Scalable infrastructure
• Reliance on GPT-5.6 and external APIs may create dependency risks.
• The team size is 2, which may limit execution capacity.
• Demo Mode is a workaround for API key limitations, suggesting a prototype-level experience.

Inference The project lacks commercial viability or traction indicators. It is not evident that it has moved beyond the idea stage.

1. What is the intended path from this hackathon prototype to a product with users?
2. Are there any plans for monetization or pricing models?
3. How do you plan to scale beyond the current demo and API-based approach?
4. Have you tested the product with real users outside of the hackathon?
5. What are the long-term technical dependencies, and how will they be managed?