A mathematical simulation engine for adaptive agent training at scale. Instead of static labeling pipelines, Daisy generates intelligent, system-grounded synthetic data via applied mathematics and multi-agent simulation.
Daisy is the missing layer for LLMs, RL agents, and multi-agent systems that need real-world structure, emergent behavior, and decision-state trajectories beyond simple token prediction.
Generate intelligent synthetic data through mathematical modeling and constraint-based systems.
Use differential equations, latent state models, and Hinton-inspired capsule logic.
Export trained agents as location-aware execution systems and planning assistants.
Current pipelines rely heavily on human-supervised feedback and manual labeling processes.
Large-scale datasets lack real-world structure and contextual understanding.
Restricted real-world interaction environments create fragile, logic-poor models.
Differential equations and latent state models inspired by Hinton's groundbreaking work.
Capsule-like logic and constraint-based action systems for structured reasoning.
Mirror complex, real-world decision systems through advanced simulation.
Define domain (retail, city, fraud, logistics) → generate equations & constraints
Multi-agent environments evolve based on emergent dynamics and reinforcement feedback
Structured synthetic datasets for RL pretraining, state modeling, and LLM grounding
Export trained agents as location-aware execution agents and planning assistants
Comparing Daisy AI with Scale AI approach
Training Focus
Math Foundation
Output
Vision
System simulation + state dynamics
ODE/PDE + variational/capsule logic
Adaptive agents, structured environments
World simulator for agent intelligence
Labeling, fine-tuning pipelines
Transformer supervision tools
Refined datasets, tuned embeddings
Data refinery for LLMs and CV models