AI-Powered Decentralized Reputation Framework for Web3 Fundraising
DcodeBlock is a modular AI protocol designed to bring transparency, trust, and intelligence to decentralized fundraising. It enables anonymous founders to build verifiable reputation, predicts fundraising success using multi-agent AI systems, and enhances investor decision-making — all while preserving data privacy and on-chain integrity.
- Multi-Agent AI Scoring: NLP, ML, and behavioral models score pitches, identities, traction, and investor fit.
- Modular Architecture: Each AI agent is containerized and composable across any Web3 stack.
- Privacy-First Design: No sensitive data leaves the user device unless permitted; zk and federated methods supported.
- Tokenized Reputation: Outputs can be issued as verifiable credentials or NFTs for DeFi/DAO integration.
| Agent | Functionality |
|---|---|
| Identity Agent | Validates identity using DIDs and optional zk-KYC |
| Pitch Analyzer | Uses LLMs to assess clarity, originality, feasibility |
| Fundraise Predictor | Predicts success using historical + real-time features |
| Trust Graph Agent | Maps reputation via commits, transactions, endorsements |
| Momentum Tracker | Tracks public traction (GitHub, X, Mirror, etc.) |
- Frontend: Next.js, TailwindCSS, WalletKit
- Backend: Node.js, Python (FastAPI), REST
- AI/ML: BERT, RoBERTa, XGBoost, GNNs, k-Means
- Web3: Ethers.js, DIDs, IPFS, zk-SNARKs, Ceramic
- Infra: Docker, Vercel, GitHub Actions
flowchart TD
U[Founder / Investor] --> F[Frontend]
F --> B[Backend]
B --> A[AI Agent Layer]
A --> A1[Identity Agent]
A --> A2[Pitch Analyzer]
A --> A3[Fundraise Predictor]
A --> A4[Trust Graph Agent]
A --> A5[Momentum Tracker]
B --> W[Web3 Layer]
W --> W1[DIDs]
W --> W2[IPFS]
W --> W3[zk-SNARKs]
W --> W4[Ceramic]
A --> O[Outputs - Scores, Reports, Tokenized Reputation]
O --> D[DAO / Investors]
- Founders: Build provable reputation and attract capital anonymously
- Investors: Discover vetted early-stage projects with AI-verified traction
- DAOs/Launchpads: Automate pitch screening and due diligence workflows
- Founder connects wallet and uploads pitch
- AI agents analyze and score pitch, identity, momentum
- Outputs: confidence score, scoring reports, tokenized reputation
- DAO/investors use score to decide capital allocation
Mrityunjay Dwivedi
AI Engineer & Web3 Architect | Decentralized Systems | Applied ML | Privacy Tech
“In a decentralized world, trust must be earned algorithmically.”