LYS Labs Documentation

Introduction

LYS Labs is using cutting-edge technology such as real-time data pipelines for EVM and Solana, ontology builders, and native graph databases to solve the most complex data challenges in Web3.

The flagship product for LYS Labs is the Sandbox, a real-time Web3 data analytics and intelligence platform that enables instant, structured, and deeply contextual insights for sophisticated users such as institutions, AI developers, traders, and dApp builders. Built for high-performance blockchain data processing, Sandbox delivers ultra-low-latency access to cross-chain analytics, knowledge graphs, and predictive intelligence.

Key Features

1. Real-Time Data Pipelines

Real-time processing speed, with 4ms latency for Solana and real-time for EVM chains.
Blockchain node architecture that minimizes query delays for instant insights.
Block-by-block insights for EVM.

2. Customizable Ontologies

Users can define custom data schemas to organize and analyze blockchain events with precision.
The Sandbox comes pre-equipped with baseline ontologies.

3. Knowledge Graphs for Deep Context

Ability to process billions of nodes and edges to extract insights.
Sandbox structures blockchain data using multi-hop relationship mapping, revealing wallet clusters, liquidity flows, governance interactions, and more.
Helps users detect hidden correlations in DeFi, token movements, trading behavior, and more.

4. AI-Optimized Retrieval & Contextualization

The Sandbox integrates Ontology-Grounded Retrieval-Augmented Generation (OG-RAG) for AI model fine-tuning.
This allows agents, trading bots, and institutional models to retrieve and process highly structured blockchain intelligence efficiently.

How the LYS Labs Sandbox Works

1. Data Collection & Ingestion

Sandbox ingests raw blockchain data directly from nodes, bypassing third-party APIs for speed, accuracy, and cost efficiency.

Supports Solana, EVM, and cross-chain integrations.
Captures mempool activity, transaction data, liquidity movements, and governance updates in real time.
Reduces redundant queries to optimize efficiency.

2. Parsing & Decoding

Extracts smart contract instructions, token swaps, and governance votes.
Maps transactions using multi-layer ontologies for deep event analysis.
Example: A new token launch is linked to liquidity inflows, swap activity, and whale movements, providing full market context instantly.

3. Real-Time Indexing

Immediate indexing of every transaction, allowing users to query historical and real-time blockchain data seamlessly.
Designed for high-throughput querying, serving both high-frequency trading applications and AI models.
Graph-powered indexing enables fast, relationship-driven querying across blockchain networks.

4. Knowledge Graph Integration

Unlike SQL-based analytics platforms, Sandbox structures data using native graph-based architecture.
This enables relationship-driven insights, such as:
- Tracking whale movements across multiple wallets.
- Mapping governance voting trends to protocol stability.
- Understanding cross-chain token flows for liquidity management.

Customers & Use Cases

1. High-Frequency Traders (HFTs) & Market Makers

🔹 Why They Need LYS Labs

Speed is everything: With 4ms data latency, LYS Labs delivers market updates faster than any competitor, allowing traders to front-run inefficiencies before they are priced in.
Live Order Flow Tracking: Real-time liquidity shifts, whale movements, and large trades are instantly available.
Multi-Hop Relationship Mapping: Who is buying? Who is selling? LYS Labs maps these interactions in real time to identify trends before they play out.

💡 Use Cases ✅ Detecting Institutional Order Flow: Identify whale accumulation before price moves. ✅ Arbitrage Optimization: Compare pricing and liquidity across Solana DEXs with near-instant updates. ✅ Dynamic Spread Management: Adjust market-making spreads dynamically before volatility spikes.

2. MEV Searchers & Quant Firms

🔹 Why They Need LYS Labs

First access to block-by-block data: MEV (Maximal Extractable Value) searchers need sub-millisecond execution timing, and LYS Labs provides the fastest raw blockchain data to capture edge cases.
Transaction Ordering Analysis: Since we track block-by-block data ingestion, MEV searchers can analyze pending transactions to predict profitable reordering strategies.
Real-Time Mempool Monitoring: Get 4ms latency updates on pending Solana transactions, allowing for faster sandwiching, backrunning, and liquidation detection.

💡 Use Cases ✅ Arbitrage MEV: Identify arbitrage paths milliseconds ahead of competing bots. ✅ Liquidation Sniping: Capture undercollateralized positions the instant they become liquidatable. ✅ Sandwich Attacks & Backrunning: Leverage block sequencing insights for optimal positioning.

3. DeFi Protocols & Liquidity Providers

🔹 Why They Need LYS Labs

Prevent Sudden Liquidity Drains: Real-time data tracks liquidity migration across DeFi protocols, helping LPs preempt major shifts.
Detect Smart Money Movements: Identify when top LPs are exiting pools, predicting future APY declines.
Risk Mitigation: Protocols can flag suspicious transactions before a major exploit unfolds.

💡 Use Cases ✅ Yield Optimization: LPs receive instant alerts when a new pool offers higher, safer yields. ✅ Governance Attack Prevention: Early detection of voting manipulations and hostile takeovers. ✅ Liquidity Pool Risk Assessment: Track how liquidity providers shift across protocols and whether a pool is whale-dependent.

4. Hedge Funds & Institutional Investors

🔹 Why They Need LYS Labs

The first institutional-grade Solana data source: No other platform delivers block-by-block, highly contextual data for smart money tracking.
Cross-Protocol Analysis: Where is capital flowing? LYS Labs provides full ecosystem insights.
Identify Hidden Market Trends: Using ontology-driven analytics, institutions can see early-stage patterns that precede market shifts.

💡 Use Cases ✅ Alpha Discovery: Backtest on-chain trading signals with structured, normalized Solana data. ✅ Tracking Whale Wallets: Use graph-based AI to identify wallet clusters before they make major moves. ✅ Multi-Chain Trend Analysis: Detect when liquidity is rotating out of Solana into other ecosystems.

5. Algorithmic Trading Firms & AI Model Trainers

🔹 Why They Need LYS Labs

Structured Data for AI Training: Unlike unstructured blockchain feeds, LYS Labs provides highly contextualized, labeled datasets—perfect for AI-driven trading strategies.
Graph-Based Feature Engineering: Identify hidden relationships between tokens, liquidity pools, and whales for predictive analytics.
RAG-Optimized Training: Our Ontology-Grounded RAGs (OG-RAGs) improve AI model efficiency by structuring historical and real-time Solana data for decision-making.

💡 Use Cases ✅ Training Price Prediction Models: Solexys AI, powered by LYS Labs, achieved 78-91% accuracy using structured Solana data. ✅ Developing Intelligent Trading Agents: AI-driven hedge funds can train models directly on our real-time knowledge graph. ✅ Automated Market Surveillance: AI models can detect anomalous trading behaviors, spoofing, and wash trading.

6. Web3 Security & Compliance Firms

🔹 Why They Need LYS Labs

Live Anomaly Detection: Detect potential exploits before they’re executed using multi-hop relationship analysis.
Wallet Clustering for AML Compliance: Identify wallet networks engaging in illicit activities and flag suspicious fund movements.
Track DeFi Protocol Vulnerabilities: Identify protocols with high governance risks or concentrated token ownership.

💡 Use Cases ✅ Early Fraud Detection: Flag potential rug pulls and contract exploits before they happen. ✅ AML & Regulatory Reporting: Provide structured, explainable transaction histories for blockchain forensics. ✅ Whale-Driven Market Manipulation: Detect coordinated token dumps or price manipulations before they impact the market.

7. Crypto Data Aggregators & Research Firms

🔹 Why They Need LYS Labs

Unmatched Data Freshness: Unlike Dune, Nansen, or The Graph, LYS Labs provides real-time updates on Solana with 4ms latency.
Cross-Chain Querying: Research firms can track DeFi trends across ecosystems, creating comprehensive crypto reports.
Customizable Ontologies: Academic researchers and hedge funds can create their own structured datasets for analysis.

💡 Use Cases ✅ Web3 Market Research: Create custom dashboards tracking Solana’s liquidity flow in real time. ✅ Historical Blockchain Analysis: Structure multi-year on-chain datasets for trend forecasting. ✅ Risk Modeling & Portfolio Stress Testing: Analyze historical DeFi crashes to refine future risk models.

Getting Started

1. Querying the Sandbox

Supports natural language querying for blockchain intelligence.
Example queries:
- “Show me the liquidity movements of all pre-graduation Pump.fun tokens in the last 24 hours.”
- “Track all wallets that interacted with a known exploit address in the past month.”
- “Detect price discrepancies across Raydium and Orca for arbitrage.”

2. Knowledge Graph Queries

Cypher-based querying is available for developers needing complex multi-hop analytics.
Supports custom ontology structuring for specialized research.

3. Step-by-step Guide

Step 1: Query Data with NLP

Step 2: Ontology Builder

Step 3: Data Transformation

Step 4: Data Science Algorithms

Step 5: Further exploration, such as anomaly detection

Step 6: Data Visualization

Security & Privacy

LYS Labs does not store private keys.
User data remains encrypted and interacts with blockchain smart contracts permissionlessly.
Transparent audit logs track all API calls and data queries.