Technology Stack

AI Insights

Real-Time Data Aggregation

Tookey processes and analyzes live data from Web2 and Web3 sources, ensuring a thorough understanding of market dynamics.

Decentralized Data Ingestion Network

A distributed network of verified data contributors provides high-integrity data inputs, reducing reliance on centralized sources and improving accuracy.

Actionable Intelligence

Tookey’s AI models detect emerging trends, market anomalies, and potential risks. They deliver structured insights in a clear format for decision-making.

Security & Privacy

Decentralized Trust Model

Tookey minimizes dependency on single-point data sources, enhancing data integrity and reducing risks, using a trustless verification framework.

Privacy-First Approach

Data processing and sharing comply with Web3 security standards, ensuring privacy and user control over sensitive information.

Seamless Integration

Cross-Platform Data Fusion

Tookey aggregates and correlates structured and unstructured data across Web2 and Web3, offering a comprehensive market perspective.

API-Driven Architecture

A flexible API layer lets enterprises and developers integrate Tookey’s analytics into their existing workflows and platforms.

Distributed Verification Network (DVN)

Node Architecture

• Consensus Mechanism: Proof-of-Stake (PoS)

• Minimum Staking Requirement: 50,000 $TOO

• Fault Tolerance: Byzantine fault resistance up to 33% malicious nodes

• Hierarchical Node Structure: Validator committees for improved efficiency

Performance Benchmarks

• Uptime Target: 99.5% (30-day rolling average)

• Latency: for the 95th percentile of queries, <500ms

• Geographic Redundancy: Nodes distributed across at least three continents

• Data Replication Factor: 3x redundancy for reliability

AI/ML Framework

Model Architecture

Tookey’s AI uses a hybrid model ensemble for multi-dimensional analysis, including:

• Transformer-based sequence models for real-time market pattern detection

• Graph neural networks for identifying on-chain relationships and network effects

• LSTM networks for trend forecasting and anomaly detection

Training & Continuous Optimization

• Dataset Size: Over 5 million labeled transactions across the top 100 protocols.

• Validation Protocol: Cross-validation with manual audit trails to improve model accuracy

• Retraining Cycle: Weekly model updates with newly indexed on-chain and off-chain data

• Confidence Scoring: An automated ranking system with human verification for unusual cases.

This architecture ensures a scalable, efficient, and adaptive AI-driven data intelligence platform for the Web3 market.