Large Behavioral Models
The scientific output of Behavioral AI at scale. Where Large Language Models learn from text, Large Behavioral Models learn from the world in motion.
A NEW MODEL CLASS
What the LBM is.
A Large Behavioral Model does not ask what was said. It asks what is happening. Not what an actor has done historically, but what their behavior reveals about their state, intent, and trajectory in this moment. That is a fundamentally different class of model - requiring a fundamentally different class of data.
Large Behavioral Model · noun
A model class that continuously models actors across time and context - building a live representation of what normal, expected, and anomalous behavior looks like for every entity it observes.
LARGE LANGUAGE MODELLearns from text. Predicts tokens. Trained on what was written.
LARGE BEHAVIORAL MODEL
Learns from the world in motion. Models actors. Trained on what actually happens.
ARCHITECTURESignal to output.
Layer 01Signal capture
Continuous behavioral signal from real enterprise environments. Motion, sequence, rhythm, timing, deviation. Every device, every session, every interaction.
Layer 02 Feature extraction
Raw telemetry structured into learnable temporal representations. Not data points - behavioral dynamics conditioned on context and actor continuity.Layer 03Model inference
Live behavioral context evaluated against established patterns. Trajectory, temporal dynamics, and contextual drift interpreted simultaneously.
Layer 04Adaptive output
Risk, trust, and adaptation signals delivered at the point every decision is made. Low-latency. Continuous. No batch processing.
STRUCTURAL ADVANTAGE
Data that cannot be replicated.
Behavioral signal cannot be scraped. It cannot be synthesized. It cannot be purchased or replicated. It can only be earned through deployment - through real systems, real actors, real environments, operating in real time.
"Generic data produces generic models. Synthetic data produces brittle models. Proprietary behavioral signal produces something no competitor can replicate from the outside."
Flywheel
Step 1 - Deployment generates signal
Every enterprise deployment produces proprietary behavioral signal no competitor can acquire.
Step 2 - Signal trains better models
What normal looks like for a human. What expected looks like for an agent. What anomalous looks like for a workflow.
Step 3 - Better models make deployments more valuable
More accurate. More adaptive. The gap between proprietary and generic models widens with every cycle.
Step 4 - Advantage compounds
The dataset that trains the model cannot be reconstructed. The first company to build an LBM at scale holds a structural advantage.
INFRASTRUCTURE REQUIREMENTSBuilt for real-time adaptation.
Streaming-native
Behavioral signal processed continuously. Not in batches. Behavior understood after the moment has passed is not behavior understood.
Privacy-preserving
Behavioral understanding derived without persistent personal data. On-device processing where applicable. Model-based evaluation, not raw replay.
Low-latency by design
Inference delivered at the point every decision is made. Architecture optimized for real-time deployment, not offline analysis.
Context-aware
The meaning of behavioral signal changes with context. Both evaluated simultaneously. No signal is interpreted in isolation.
Actor-consistent
Behavioral modeling maintains continuity across sessions, channels, and environments. Context, not credentials.
Composable
API-first architecture. Integrates with existing systems. Does not replace - it gives every system the understanding it has always been missing.
RESEARCH ROADMAPBehavioral modeling extends everywhere.
Authentication is the first commercial system built on zally's modeling framework. The science scales to every domain where systems decide without understanding.
Identity & Security
Continuous behavioral modeling replaces static credentialing. Authentication becomes a live signal, not a gate.
Healthcare
Clinical systems updated episodically replaced by continuous behavioral modeling of patient state in real time.
Financial services
Risk models operating on stale signals replaced by models that understand what is happening at the moment a decision is made.
Enterprise & Workforce
Workflows built on static user models replaced by systems that continuously understand how people actually work.
Autonomous systems
Machines in dynamic environments require continuous behavioral modeling to act safely and proportionally.
Agent ecosystems
As AI agents proliferate, behavioral trust becomes the defining challenge. LBMs make agent behavior observable and accountable.
FIRST DEPLOYMENTSee the LBM in action.
Continuous Authentication is the first commercial system powered by zally's Modeling Framework.