The World's Only
Large Behavioural Model

A foundational AI system that models who you are — not just what you do. Continuously learning, causally reasoning, privacy-preserving.

200K+
USERS
1T+
DATA POINTS
230M+
CAUSAL EDGES
500B
PARAMETERS
Research Abstract

A new class of AI for human understanding

Current AI systems — from large language models to recommendation engines — operate on surface-level patterns. They predict your next word or next click, but fundamentally cannot model why you behave the way you do.

The Large Behavioural Model (LBM) represents a paradigm shift: a foundation model built from the ground up to understand, predict, and adapt to individual human behaviour through causal reasoning and continuous learning.

Unlike LLMs that model language distributions, LBM models behavioural state trajectories — creating a living digital twin that evolves with every interaction.

Architecture Overview
Privacy ShellDomain BridgeAdaptation LayerCausal EngineBehavioural StateIdentity Core
Benchmarking

Intelligence Landscape — Where AI Models Fall

Plotting all major AI paradigms on two axes — language/pattern intelligence vs. behavioural intelligence. LBM occupies an entirely new quadrant.

AI Intelligence Map — Behavioural vs Language/Pattern Axis
00252550507575100100Language / Pattern Intelligence →Behavioural Intelligence →GPT-4GeminiDeepSeekRecSysPsychometricRule-BasedMeta Behav.LBMFrontier
LLMs: High language, low behaviour
RecSys: Mid pattern, mid behaviour
LBM: High on both axes
BENCHMARKS

Quantified impact vs traditional AI

PERSONALISATION ACCURACY
LBM
99.6%
Rest
57%
REAL-TIME ADAPTATION
LBM
ms-level
Rest
min-hours
BIAS REDUCTION
LBM
90%
Rest
~10%
DOMAIN FLEXIBILITY
LBM
∞ domains
Rest
1 vertical
RESPONSE RELEVANCE
LBM
Rest
DATA LABELLING COST
LBM
−75%
Rest
0%
LONG-TERM CONTINUITY
LBM
Rest
COMMERCE CONVERSION
LBM
9-12%
Rest
2-3%
+42%
Personalisation
>100×
Adaptation
75%
Cost reduction
+700%
Empathy
4× ROI
Commerce
50%
Wellbeing
HUMANS ARE NON-STATIONARY

The logic of Behavioural Regimes.

You are not the same person at 3 PM on a deadline Tuesday as you are on a Sunday morning. Life is an alternating sequence of latent regimes. LBM detects transitions and optimises per regime.

CAUSAL REWIRING

Stable State Dynamics

SleepFocusStressMetabolism

In a stable state, all variables have balanced influence. Sleep is a peripheral variable with moderate connections to focus and stress.

Insight: An intervention valid in Regime A may be detrimental in Regime B.
PRIVACY-PRESERVING ML

Your data.
Never leaves your device.

Four-layer PPML stack. No PII ever leaves the device. The model goes to the data — never the reverse. Complete sovereignty.

YOUR
DATA
Homomorphic Encryption
Secure aggregation — compute on encrypted data
Federated Learning
Model goes to data. Never the reverse.
Differential Privacy
Noise injection N(0, σ²I) on every gradient
Your Data (Raw)
Never leaves your device. Complete sovereignty.
CASE STUDIES

Before vs. After LBM

STANDARD AI
"You seem tired. Try sleeping more."
LBM CAUSAL REASONING
"CBG shows Sleep→Focus edge spiked to 0.9 in Regime B. Cortisol lag from Thursday's 11pm screen session is the root cause. Shifting screens off by 10pm predicts +23% focus by Tuesday."
STANDARD AI
"Based on users like you, try this course."
LBM CAUSAL REASONING
"BSV shows cognitive capacity at 0.85 but motivational drive dipping. LoRA detects dopamine-reward misalignment. Micro-lesson at 11:10am (your peak) — 90 seconds, matched to learning rate."
STANDARD AI
"Your heart rate is elevated."
LBM CAUSAL REASONING
"Regime switch: Baseline→High-Demand. Backtracing: 3 meetings over average + 1.5h sleep debt. 15-min break now → Energy +1.4, crash delayed 2h."
Methodology

How LBM thinks

A proprietary six-stage continuous intelligence loop. Protected by patent.

Sense
Interpret
Learn
Adapt
Act
Reflect
01SENSE

Multi-modal signal ingestion from behavioural touchpoints

02INTERPRET

Causal reasoning engine extracts meaning beyond patterns

03LEARN

Foundation model updates behavioural state continuously

04ADAPT

Real-time personalisation of predictions and actions

05ACT

Context-aware interventions across any domain

06REFLECT

Self-improving feedback loop that strengthens over time

Deep Comparison

Performance Heatmap

Head-to-head across 8 critical dimensions. Benchmarked against every major AI paradigm — not just LLMs.

DIMENSIONLBMGPT-4GeminiDeepSeekRecSysPsychRuleMeta
Personalisation99.642403857524548
Causal Depth94151820835512
Real-Time Adapt.97101282231525
Cross-Domain9670656015101220
Paradigm Shift

LBM vs every AI paradigm

Large Language Models

GPT-4, Gemini, LLaMA
Model language, not behaviour
No individual state tracking
Prompt-dependent, no memory
No causal reasoning about people

Recommendation Engines

Netflix, Spotify, Amazon
Collaborative filtering only
Correlation-based predictions
Cold start problem persists
Domain-locked silos

LBM

Dots-In · Patent Protected
Causal behavioural modelling
Living individual digital twin
Real-time adaptive evolution
Universal cross-domain transfer
DOMAIN AGNOSTIC

One infrastructure. Every domain.

🧠
Identity Layer for LLMs
AI INFRASTRUCTURE
💊
Personalised Medicine
HEALTHCARE
👤
Digital Twin OS
CORE INFRASTRUCTURE
🛒
Subconscious Commerce
COMMERCE
Cognitive Work Agents
PRODUCTIVITY
🏠
Neurofeedback Environments
IoT & AMBIENT
🤖
Chips for Embodied AI
ROBOTICS
🎵
Content & Media Intelligence
MEDIA
🧠
AI INFRASTRUCTURE
Identity Layer for LLMs
Standard LLMs hallucinate because they lack a user model. LBM injects real-time behavioural state into every prompt, generating responses that feel like they come from a close friend—not a generic assistant.
• IMPACT VECTOR
"Zero-shot alignment with user values."
EARLY ACCESS

The infrastructure is ready.
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