Janitor price

JANITOR

#3217

Janitor performs several key functions:

• Onchain Monitoring: Tracks new token launches, wallet behavior, liquidity patterns, and transaction anomalies in real time.
• Social Signal Processing: Aggregates and interprets sentiment from social platforms, linking behavior of known accounts with token-specific or macro narrative shifts.
• Risk and Alerting Engine: Uses reinforcement learning and graph-based modeling to identify emergent risks, sudden activity changes, or coordinated behavior.
• Insight Generation: Employs language models and structured memory graphs to produce narrative summaries and forecasts about asset performance, community trends, or protocol risk.

Janitor is built to function as an autonomous agent layer that can be integrated into trading frontends, dashboards, dApps, or security systems across the BNBChain ecosystem.

Janitor aims to evolve from a passive analytics engine into an agentic coordination layer across the BNBChain ecosystem. With support for the Model Context Protocol and Agent-to-Agent messaging, Janitor will support downstream integration of autonomous agents and AI assistants that can operate across decentralized applications.

By exposing a structured intelligence layer to other dApps and services, Janitor intends to support a broader transition toward agent-native infrastructure, where reasoning, data ingestion, and decision-making are embedded into ecosystem interactions.

Janitor is currently in active development and undergoing integrations with several BNBChain-native protocols.

Janitor combines multiple AI and data processing systems into a single inference stack:

• Data Fusion Layer: Aligns chain data, address-level activity, and offchain context using cross-modal attention and feature extraction models.
• Real-Time Learning Engine: Processes live transaction and social data through streaming pipelines with incremental model updates.
• Graph-Based Risk Modeling: Uses graph neural networks to identify clusters, outliers, and emergent coordination patterns across addresses and contracts.
• Language Model Integration: Augments detection and reporting with large language models (LLMs) for semantic summarization and reasoning.
• Reinforcement Learning Module: Optimizes alert thresholds and prioritization via feedback-based tuning over time.