The Problem
Modern AI systems heavily rely on large datasets, but access to meaningful data contribution is limited to experts and centralized entities. At the same time, Web3 users are often disconnected from real value creation, interacting with systems that lack clarity, usability, and purpose. This creates a gap where AI development is not community-driven, users cannot easily contribute to AI growth, and reward systems lack transparency and engagement.
The Solution
SYNTHOS introduces a decentralized AI contribution layer where users actively participate in training intelligence models through structured data tasks. By combining intuitive UX, simulated AI processing, and blockchain-based rewards, the platform creates a seamless loop of contribution and incentive. Users connect their wallet, complete tasks, and receive token rewards, forming a scalable foundation for future AI-driven ecosystems.
Showcases
AI Dashboard & Task System Overview
This screen shows the main SYNTHOS dashboard where users can view their balance, contributions, level, and reputation in one place. It includes a dynamic task feed with different categories like sentiment, risk, tagging, and prediction.Users can select tasks based on difficulty and rewards, creating a smooth contribution flow. The layout is designed to feel like an AI control panel with real-time interaction and clean data visibility.
AI Task Execution & Reward Interaction
This screen represents the task interaction system where users complete AI-related tasks such as rating generated content. Each task is simple, fast, and designed to simulate real AI data labeling. Users can select answers, submit responses, and instantly earn token rewards. The UI focuses on clarity and engagement, making the contribution process feel smooth and rewarding.
Top Contributors & Leaderboard System
This screen highlights the leaderboard where users can see top contributors based on their performance. It shows ranking, contribution levels, completed tasks, and total points earned. The system motivates users through competition and visibility within the network. Visual progress bars and badges add a gamified experience to increase engagement.
User Reputation & Level Progression
This screen displays the user reputation system, where contributors progress through different levels. Each level unlocks better rewards and increases user credibility within the platform. It includes progress tracking, reward multipliers, and milestone achievements. The design helps users clearly understand their growth and encourages continuous participation.
About this project
SYNTHOS
SYNTHOS is a Web3 AI platform designed to make AI participation simple, interactive, and rewarding. The main idea behind this project was to transform complex AI data processes into a system where everyday users can contribute easily and earn token rewards directly in their wallet.
Instead of building a heavy and complex AI infrastructure from day one, we focused on creating a market-ready MVP that delivers a real product experience. The goal was to validate user behavior, engagement, and the reward loop while keeping development fast and scalable.
Project Goal
The primary goal of SYNTHOS was to help the client launch a Web3 AI product that:
- feels like a real AI-powered system
- is simple enough for non-technical users
- includes a working reward loop
- is ready to showcase to investors and early users
We focused on building a product that is not just functional, but also believable and engaging.
Problem Context
Most AI platforms are difficult to access and require technical knowledge. At the same time, many Web3 platforms fail to create meaningful user interaction and long-term engagement.
This creates a gap where:
- users want to participate but don’t know how
- AI systems lack accessible contribution layers
- reward systems exist but feel disconnected from real value
Our Approach
We approached SYNTHOS as a system design problem, not just a UI project.
The focus was on creating a smooth flow where users feel like they are part of an intelligent network. Every interaction was designed to simulate real AI behavior while keeping the backend lightweight.
The experience is built around a simple loop:
User connects wallet → completes task → AI processes → reward is given
This loop is the core of the product.
Product Experience
Users start on the landing page and move into the
/aiInside the dashboard, users can:
- access AI-powered tasks
- submit data contributions
- receive instant feedback
- track rewards and progress
Each action triggers a simulated AI response, supported by animations and system feedback. This makes the platform feel alive, even in MVP stage.
The design uses a technical and industrial theme with dark surfaces and neon green accents, giving it a strong AI system identity.
Key Features
- Wallet connection using WalletConnect
- AI task feed for data labeling and validation
- Simulated AI processing with real-time feedback
- Token reward system with on-chain interaction logic
- User reputation and contribution tracking
- Leaderboard and engagement system
- Responsive and animated dashboard UI
How It Works
The system is built around a simple but powerful flow:
| Step | Action | System Response |
|---|---|---|
| 1 | User connects wallet | Wallet is linked to identity |
| 2 | User selects a task | Task data is loaded |
| 3 | User submits input | AI processing is triggered |
| 4 | System validates input | Contribution is accepted |
| 5 | Reward is calculated | Tokens are assigned |
| 6 | Dashboard updates | Balance and reputation increase |
This structured flow helps users clearly understand their role in the system.
Technical Execution
The project was built using a modern Web3-friendly stack:
- Next.js for frontend and API routes
- Tailwind CSS for fast and scalable UI styling
- Framer Motion for animations and transitions
- Supabase for database and backend logic
- WalletConnect + Wagmi + Viem for wallet integration
- ERC-20 token system for rewards via public RPC
The architecture was designed to be lightweight but expandable, allowing future upgrades without rebuilding the system.
Challenges & Solutions
One of the biggest challenges was making a simulated system feel real. Since the MVP does not include a full AI backend, the experience had to rely on UI, feedback, and flow design.
We solved this by focusing on:
- strong visual feedback (animations, progress states)
- clear reward logic
- consistent system responses
- immersive design language
This created a product that feels like a working AI system, even with simplified logic.
Outcome
The final result is a high-quality MVP that successfully demonstrates the concept of decentralized AI contribution.
The platform:
- delivers a strong first impression
- creates user engagement through rewards
- validates the idea of AI + Web3 participation
- is ready for early users, demos, and investor presentations
Most importantly, it gives the client a solid foundation to scale into a full AI-powered ecosystem in the future.
Final Note
SYNTHOS is more than just a dashboard it is an early version of a system where users and AI work together.
By combining simple interactions with token incentives and strong design, we helped transform an idea into a market-ready Web3 MVP that feels both modern and meaningful.
Screenshots
Design System
Primary
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Secondary
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Accent
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Background
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