Stop Managing Probability.
Start Managing Certainty.

The World's First Sensor-Agnostic Adherence Engine.

We synthesize Passive Telemetry (Wearables/IIoT) with Static Risk Data to eliminate "Alert Fatigue" and automate risk prevention.

See The Hybrid Model View COFA Data

*Patent Pending | Automated Operational Intervention Ready

Agnostic Integration Layer: We Connect With Your Existing Stack

SAP®
SIEMENS
Honeywell
ORACLE
MQTT/API

The "Data Gap" Costs Billions

Legacy systems rely on Manual Inputs (Checklists) and Static History. They cannot see the human factor in real-time.

Friction = Falsification

When you force workers to use apps and checklists, they "pencil-whip" the data. Manual input is inherently unreliable.

Alert Fatigue

Legacy systems generate 98% false positives. Supervisors ignore them. Critical risks hide in the noise.

No "Circuit Breaker"

Current software can only "notify." It cannot physically stop a machine or block a transaction when risk is critical.

Patent Pending: The Sensor-Agnostic Engine

We don't ask for data. We capture it.

ZBT™

Passive Telemetry

The Input. We replace manual checklists with Networked Sensors (Wearables, IIoT, Biometrics). We measure Kinematic Fidelity and Spatio-Temporal Dwell to verify adherence without friction.

CARI™

Certainty Index

The Calculation. The core algorithm generating a real-time 'confidence score' (0-100%). It synthesizes Static Risk with Passive Telemetry to filter out the noise.

PANT™

Automated Intervention

The Circuit Breaker. If Risk is High and Certainty is Low, PANT™ triggers an Automated Operational Intervention (e.g., Machine Lockout or Transaction Block).

Deep Agent Architecture

Not Generative. Zenprexi uses Deep Reinforcement Learning agents that act autonomously based on physics and biometrics, not language probability.

Zero Hallucinations

Deterministic Logic. Our engine relies on verifiable sensor data. It does not "guess" safety outcomes; it calculates them with mathematical certainty.

Future-Proof Stability

Adaptive Learning. The engine ingests new sensor types (Lidar, Thermal, EEG) without code rewrites, keeping the core stable as hardware evolves.

The Deterministic Loop

How Zenprexi converts chaos into certainty in < 20ms.

1. Ingest

Passive Capture: The engine listens to PLC vibration, CCTV telemetry, and worker biometrics simultaneously.

2. Compute

Deep Agent Analysis: The AI cross-references real-time stress levels against static safety protocols.

3. Intervene

PANT™ Execution: If the Certainty Index drops below threshold, the system physically locks the hazard and/or signals and intervention.

GTM: The "Incentive-to-Mandate" Hybrid

We avoid the "Death Valley" of enterprise sales by partnering with Liability Managers (Insurers & Banks) to create value first.

PHASE 1
1

INCENTIVIZE

The Wedge. We partner with Carriers to authorize a Risk Engineering Credit (5-10% discount). Clients adopt Zenprexi voluntarily to save money. Zero friction.

PHASE 2
2

VALIDATE

We collect Passive Data to generate the Cost of Failure Averted (COFA) report. We prove to the Carrier that Zenprexi users have 40% fewer claims.

PHASE 3
3

STANDARDIZE

The Mandate. Armed with actuarial data, the Carrier converts the credit into a Required Standard for high-risk policy renewals.

The COFA Metric (Patent Claim M)

We translate "Safety" into "Dollars Saved."

Cost of Failure Averted (COFA) Waterfall

Sources: [1] Siemens (2024) & Gartner (2024) report avg. industrial downtime costs from $500k to $2.3M per hour. [2] NAEM & OSHA report EHS software can reduce incident rates (TRIR) by 20-40%. [3] Sabentis (2025) report 80-90% of accidents are human error.

Vertical Expansion

Deploying context-aware safety across the highest-risk sectors.

Manufacturing High Risk
Phase 1: Available Now

Manufacturing

Monitoring fixed assets (CNC, Assembly) via PLC integration and Smart PPE. Preventing operator error in high-stress environments.

  • Fatigue Detection
  • Gaze Fixation Tracking
Energy Control Room
Phase 1b: 2026 (Late)

Energy & Utilities

Critical infrastructure monitoring (Nuclear, Oil & Gas). Preventing cognitive overload in control rooms during alarm floods.

  • Alarm Suppression Logic
  • 24/7 Shift Work Biometrics
Logistics High Risk
Phase 2: 2027

Logistics

Driver Monitoring Systems (DMS) for long-haul trucking and rail. Correlating vehicle drift with PERCLOS (eye closure) metrics.

  • Microsleep Prevention
  • Insurance Premium Reduction
Construction High Risk
Phase 2b: 2027 (Late)

Construction

Heavy equipment monitoring (Cranes, Excavators) in chaotic environments. Correlating operator distraction with load stability.

  • Rollover Prevention
  • Site Geofencing
Healthcare Critical
Phase 3: 2028

Healthcare

Provider safety suite for surgeons and ER staff. Monitoring biometric stress levels to prevent medical errors during critical procedures.

  • Burnout Prevention
  • Clinical Error Reduction

Leadership & Advisory

Michael Peck

Michael Peck

Founder & CEO

The visionary behind the Behavioral Utility model has an extensive background executing with precision and efficiency in leading highly skilled teams who understand complex enterprise risk and compliance is leading capital allocation, enterprise partnerships, and the global mission to cure alert fatigue.

Contribution: Mitigates Enterprise Sales Risk via complex contract fluency, accelerating contract negotiations to implementation.
Dr. Mark Dean

Dr. Mark Dean

Sr. Strategic Advisor

Legendary computer scientist, an IBM Fellow, and a co-inventor of the original IBM PC. He provides Zenprexi™ with invaluable technical, architectural, and IP-defense validation, having led the team that created the industry's first gigahertz processing chip.

Contribution: Provides the technical guidance required to defend the comprehensive utility patent claims and future-proof the Zenprexi engine architecture.

Enterprise Assurance

Addressing critical questions for Risk Officers and CISOs.

Does this replace human oversight?

No. Zenprexi acts as a cognitive filter. By suppressing 98% of false positives (noise), we allow human supervisors to focus purely on the 2% of verified threats that require judgment.

How is biometric data handled?

Privacy-First Architecture. Biometric data is processed at the edge and instantly tokenized. We transmit "fatigue scores" (metadata), not personal health information or identity.

Do we need new hardware?

Rarely. Zenprexi is sensor-agnostic. We ingest data from your existing PLCs, CCTV, and standard industrial wearables via standard protocols (MQTT, REST API).

How is liability determined?

Immutable Logs. The engine creates a forensic audit trail of every intervention. This provides legal proof of adherence, reducing negligence claims and insurance premiums.