I build AI that
understands
people.
I am architecting the Large Behavioral Model (LBM) to model intent, memory, and context so AI systems can respond with human-level relevance.
The Spark
A single question launched the first causal engine for human intent prediction.
The Thesis
Can machines infer the "why" behind decisions, not just classify the action?
The Build
Engineering LBM infrastructure through Indots with safety and ethics at the core.
"The future belongs to AI that understands intent, context, and consequence, not only syntax."
LLMs model language.
LBM models real human behavior.
Multimodal Signals
Streaming behavioral telemetry, temporal events, and environmental context.
Behavioral Vectoring
Mapping latent human dimensions into high-dimensional causal space.
Intent Inference
Real-time counterfactual reasoning to predict the "Why".
Dynamic Policy
Adaptive responses that evolve with state, persona, and long-term memory.
Episodic Memory
A permanent, evolving ledger of user interactions that builds genuine long-term context.
BSV Construction
Behavioral State Vectors that encapsulate personality traits and cognitive load.
Temporal Dynamics
Analyzing how user behavior drifts and evolves across time horizons.
CBG Discovery
Cognitive Behavioral Graphs that map connections between environments and choices.
Root-Cause Inference
Going beyond correlation to identify the Causation driving behaviour
Safe RL Policy
Reinforcement Learning systems designed with ethical guardrails for human safety.
Deploying LBM across
human complexity.
Assessli
B2B SAASBehavioral diagnostics and psychometric intelligence for enterprise hiring and talent development.
VISIT WEBSITE
Dots-inDots-In
CONSUMERA personal intelligence interface helping individuals decode patterns, habits, and decision loops.
VISIT WEBSITE
Indots
NON-PROFITA research foundation advancing ethical AI behavior modeling for education and mental health systems.
VISIT WEBSITEThe Core Collective
