Protocol treasuries hold billions of dollars in tokens as CAC but allocate these highly ineffectively.

Optimized Airdrops
Stop rewarding tokens blindly. Reward the right users at the right time at the optimal token amount.
Monitize AI's Onchain LTV Score calculates the potential Web3 LTV of a given user for a given protocol based on historical onchain behavior.

Dynamic Token Emissions

PARTNERS
Comprehensive Evaluation
The Onchain LTV Score evaluates users based on their on-chain activities across multiple blockchains and off-chain market movements, integrating both on-chain and off-chain data to provide a comprehensive view of the potential value of any given user.
WHALE
LOYALTY
SENTIMENT
LONGEVITY
ENGAGEMENT
Tailored Score
We tailor the Onchain LTV Score for each category (Gaming, DeFi, L1, DePin, Infra, Social, Memes, etc.) to help protocols acquire, retain, and reward the ideal users for their specific communities.
GAMING
DeFI
L1
RWA
INFRA
SOCIAL
Hierarchical AI Agents
We develop, train, and test multiple competing ML models to achieve maximum predictive power and out-of-sample performance. We further create hierarchical AI agents to manage data perturbations, noise, and tuning effectively.
SCORE
Large Scale Model Training
The competing models are trained on a massive on-chain dataset spanning multiple blockchains and corresponding transactions. These figures continue to grow daily, enhancing the accuracy of our model and compounding the sophistication of our ML algorithms.
Dynamic Supervision
The score is dynamic, and continuously updated via simple reflex AI agents with new data from on-chain and off-chain sources. Given the volatile user behaviors, the adaptive and evaluative supervision of the models ensures that the score accurately reflects each user's current state.
TEAM
PROTOCOL FEATURES
Empowering scalable, composable, and highly specialized AI Agent systems.

Composable Multi-Agent System
Develop AI Agents that can be composed like building blocks to create sophisticated Multi-Agent Systems.
Agents are transparently scored and dynamically composed to maximize performance, efficiency and accuracy.

Collaborative Agent Development

The protocol automatically combines specialized Agents to form the most efficient Multi-Agent System for any given problem.
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