Product Architecture
Section
Details
Core Engine
AI Research Agent with Explainable Logic
Data Layers
On-chain data, Tokenomics, Social signals, GitHub, Whitepapers
AI Stack
Data ingestion → Interpretation → Report synthesis → Reasoning layer
Interface
Web dashboard + Telegram mini-app
Output Format
Full PDF reports, Summary cards, Visual risk indicators
Customization
Expert Mode, API access, Custom strategy filters
Quantora’s foundation lies in a highly specialized AI research agent built to mirror the behavior of a human crypto analyst — but with superhuman scalability, transparency, and speed. The architecture is designed around five key pillars: data, interpretation, explanation, interaction, and intelligence delivery.
3.1. 🧠 AI Research Agent Core
At the center is an autonomous agent capable of:
Collecting data from multiple trusted sources (on-chain explorers, token contract data, social media sentiment, VC tracking tools, GitHub commits, and official documentation like whitepapers)
Structuring and interpreting the data to surface key investment indicators
Generating reports with a reasoning layer — explaining why certain forecasts or risk grades are given
Unlike most black-box AI tools, Quantora is designed with explainability in mind. Every recommendation is tied to evidence, enabling users to trust — and even challenge — the output.
3.2. 🔎 Multi-Layered Analysis Framework
Quantora combines quantitative and qualitative components, broken down as follows:
Tokenomics Analysis Token supply, inflation risk, vesting schedules, unlock timelines, and market impact modeling
VC & Insider Risk Mapping Tracks wallet activities of seed investors, early whales, and exchange addresses
Developer Activity Evaluation GitHub commit frequency, pull request activity, code forks, and recent technical updates
Community Sentiment & Momentum Scrapes and interprets social mentions, engagement patterns, and trending shifts
On-Chain Metric Trends Wallet growth, DEX volume, liquidity pool depth, token holder concentration
Each data stream feeds into the AI engine for cross-weighted scoring, and is visualized in a way that both beginners and professionals can digest.
3.3. 📊 User Flow & Experience
The product is delivered via two key access points:
Web-based dashboard Designed for in-depth exploration and visualization of reports, rankings, and filtering across projects
Telegram mini-app bot Enables fast and mobile-first analysis. Users can input a token name or contract address, and receive a summarized research card, or unlock the full PDF report using $QTRA.
A typical user experience might look like:
Enter a token name or question (e.g., “Is XYZ token safe to enter now?”)
Quantora processes real-time data + historical trends
Generates a confidence-ranked report, including price prediction, risk score, and project quality
User can share this report with friends, DAOs, or communities to earn additional QTRA
Reports dynamically update based on new market movements
3.4. 🧑💼 Expert Mode & API Integration
For professional users, Quantora offers:
Custom strategy filters (e.g., low float, high GitHub activity, unvested tokens < 10%)
Portfolio-level risk assessments
Whitelabel API access for exchanges, fund managers, and index platforms
Exportable data formats for modeling and backtesting
Quantora’s architecture is not just technical — it is strategic. By enabling both casual investors and institutional researchers to access a consistent, trusted, explainable intelligence layer, Quantora becomes more than a product: it becomes infrastructure.
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