Autonomous AI Agents.
Intelligent Trading.
Build, backtest and deploy quantitative trading strategies with a visual DAG editor — Built on Sui.
Built for explainable agentic trading
Data, signal, graph, regime, risk, and execution agents collaborate in real-time under an explainable, auditable co-pilot framework.
Six agents. One decision trail.
The system's decision loop combines specialized analytics: Data, Signal, Graph, Regime, Risk, and Execution agents vote in sequence before writing a traceable consensus receipt.
Hard vetoes before exposure.
Position sizing, VaR, drawdown, liquidity, correlation, VIX, and custom crisis rules can immediately intercept and block any automated order signal.
Build with agent data
Combine visual DAG blocks or Python DSL with live sentiment, regime, and institutional flow.
strategy.signal("SUI")ONE ENGINE. MANY EXECUTION RAILS.
Execute Synapse Commands
Simulate high-concurrency neural compute loops. Type optimization and allocation calls directly into the sandbox terminal below.
High-Contrast
Efficiency Matrix.
By segmenting processing operations into strict visual and functional stages, the Xorr methodology isolates workload overheads and accelerates throughput.
Decentralized Compute Pools
Instantly delegate computation queries across a secure peer cluster without centralized bottleneck latency.
Quantum Secured Handshakes
Leverage state-of-the-art encryption tokens guaranteeing bulletproof request provenance on every execution pass.
Cognitive Agent Assemblies
Chain and compile complex LLM agent instructions, executing them locally within hardware-accelerated shells.
“Migrating our latency-critical execution loops to Xorr cut node failure rates to 0% while bringing sub-millisecond dispatch metrics back to main production pipelines.”