AI Model & Data Strategy
Component
Details
Model Architecture
Hybrid NLP + Time-Series Forecasting + Explainable AI (XAI)
Data Sources
On-chain data, token contracts, GitHub, social media, news, whitepapers
Data Processing
Normalization, noise filtering, vectorization, risk scoring pipeline
Model Training
Reinforced with user feedback and actual market results (reinforcement loop)
Explainability
Transparent logic with highlighted reasoning and weighted evidence
Accuracy Feedback Loop
Community feedback + post-prediction performance tracking
Future Upgrade Plan
DAO-approved model tuning, multi-model integration, oracle-compatible output
📄 Full Text
At the heart of Quantora lies a robust AI architecture designed not only to analyze and forecast — but also to explain, adapt, and improve continuously.
Our data strategy and model development emphasize three core principles: Accuracy. Transparency. Evolvability.
9.1. 🧠 Model Architecture
Quantora uses a hybrid model structure, blending multiple AI disciplines:
NLP-based text understanding: For parsing whitepapers, social posts, GitHub commits
Time-series forecasting: For predicting token movements based on historical data + trend patterns
Explainable AI (XAI): All outputs are backed by clear reasoning — users can see why a forecast was made and what data influenced it most
We prioritize modularity — each sub-model (e.g., tokenomics evaluator, social sentiment analyzer, dev activity monitor) operates as a plug-in within the broader AI framework.
9.2. 📡 Data Sources
Quantora pulls from a wide range of both structured and unstructured data, including:
On-chain data (wallet growth, DEX volume, token holders, liquidity metrics)
Token contracts (supply, lockups, vesting schedules)
GitHub and dev activity (commit count, repo forks, pull request frequency)
Whitepapers & docs (NLP analysis of roadmap and utility claims)
Social media signals (Twitter, Telegram, Reddit, Discord)
News sentiment & narrative analysis
All data is processed in near real-time and routed through a data validation engine to ensure quality and freshness.
9.3. 🔁 Feedback & Reinforcement Loop
Unlike static analytics tools, Quantora’s model gets smarter over time.
Each prediction is tracked post-hoc for actual outcome vs. expected range
Reports include “confidence ratings” which are scored against reality
User feedback (upvotes, corrections, validations) is fed back into the training loop
This creates a reinforcement mechanism, allowing the AI to optimize weights and heuristics based on crowd intelligence
9.4. 🔍 Explainability & Trust
Quantora emphasizes transparent AI, providing:
Breakdown of input data and how it influenced the output
Color-coded confidence metrics
Natural language “rationale” text explaining AI reasoning
Traceable model logs for high-stakes forecasts
This makes the system ideal not just for users, but also for auditors, VCs, and institutions who require reliable explainability.
9.5. 🧪 Future Model Upgrades
Our long-term goal is to open-source the AI training logic, governed by the Research DAO.
Planned upgrades include:
Multi-model voting systems for higher reliability
Regionalized data processing (language-specific sentiment models)
Oracle-ready output formatting (for use in DeFi and smart contract automation)
DAO-voted hyperparameter tuning to reflect community goals
This makes Quantora not just a product — but a decentralized AI research infrastructure.
Quantora’s AI engine evolves not only from data, but also from the collective intelligence of its users — building a system that is always learning, always explaining, and always improving.
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