Cognitive Intelligence Platform

NOUS: Neural Oscillation Unified Signature System

From a narrative or voice sample, NOUS extracts cognitive signatures, maps them across 12 neural sectors, and produces clinically actionable intelligence.

NOUS is not an AI product that happens to do clinical assessment. It is a clinical instrument built on a novel neurocognitive theory that happens to use AI in specific, bounded, replaceable roles.
Architecture

The NOUS Pipeline

Seven layers of processing transform raw linguistic input into scored, clinically interpretable cognitive intelligence.

L1AAE
L2LOGOS
L3REMIEL
L4R2X
L5NOSS
L6Beverly Index
L7Empyrean
Assessment Modes

Four Assessment Pathways

Purpose-built pipelines for cognitive assessment, forensic authentication, longitudinal monitoring, and real-time analysis.

CI

Cognitive Intelligence
Operational

Full-spectrum cognitive assessment with 12-sector NOSS classification, seven-component Beverly Index scoring, agent-guided interpretation, and evidence-based treatment intelligence.

CSAA

Cognitive Signature Authenticity Assessment
Development

Forensic narrative authenticity analysis for legal proceedings, insurance verification, and security screening. Authenticity Index scoring with fabrication pattern detection.

VOCA

Vocal Oscillation Cognitive Archive
Operational

Continuous cognitive monitoring through periodic vocal analysis. Dual-rate baseline tracking with automated alerts for significant trajectory shifts.

RTCA

Real-Time Call Analysis
Development

Live analysis of telehealth sessions, crisis calls, and ambient speech. 30-second sliding window analysis with per-participant audio isolation.

Quantification

Seven-Component Beverly Index

The Beverly Index is the composite scoring framework that translates raw NOSS sector activations into clinically interpretable metrics. Each assessment produces seven interconnected scores that together characterize the cognitive signature's clinical significance, internal consistency, and recommended action priority.

CAP
Clinical Action Priority
SM
Signature Match
CoI
Coherence Index
ID
Integration Dynamics
IP
Integration Pattern
CC
Clinical Confidence
AI
Authenticity Index (CSAA only)
Specialized Capabilities

Additional NOUS Modules

THANATOS

Mortality Forecasting

Predictive Life-Course Monitoring. THANATOS analyzes multi-domain NOSS decay trajectories to forecast mortality risk at 6, 12, and 36-month horizons. By tracking the rate and pattern of cognitive-linguistic deterioration across all twelve NOSS sectors simultaneously, it identifies convergent decline signatures that precede end-of-life transitions, often months before clinical indicators.

ORACLE

Pharmacological Response Prediction

Precision Medicine Through Language. ORACLE predicts individual drug efficacy, adverse reactions, and optimal dosing by analyzing pre-treatment cognitive-linguistic biomarker profiles. Captures the brain's functional state, how it is actually processing, to predict how it will respond to pharmacological intervention.

AEGIS

Cognitive Fitness Certification

On-Demand Cognitive Performance Verification. AEGIS provides non-invasive, real-time cognitive fitness certification for safety-critical roles. A 3 to 5 minute assessment produces a definitive fitness-for-duty determination, detecting fatigue, impairment, medication effects, and subclinical deterioration that traditional screening misses.

All module names are subject to change pending partnership agreements.

Research

Publications

Beverly Jr, Randolph R, Neural Oscillation Signature Theory: Theoretical Foundations for Inferring Cognitive States from Linguistic Output (March 02, 2026). Available at SSRN: https://ssrn.com/abstract=6447802 or http://dx.doi.org/10.2139/ssrn.6447802

Beverly Jr, Randolph R, Mathematical Foundations and Predictive Dynamics of Neural Oscillation Signature Theory (NOST): A Unified Signal-Processing Framework for Cognitive Signature Extraction and Integration Trajectory Prediction (March 03, 2026). Available at SSRN: https://ssrn.com/abstract=6484919 or http://dx.doi.org/10.2139/ssrn.6484919