EmoPulse transforms any camera into real-time emotional and biometric intelligence — extracting 47 parameters from the camera in real time — then fusing them with voice and text signals through proprietary ontological processing and patented signal fusion.
But the scanner is just the surface. Underneath is a 5-layer ontological architecture that transforms raw signals into something no AI has ever had: a real-time understanding of the human in front of it.
OUR MISSION
Build the perception layer for every AI platform — no hardware, on-device by default, no guessing.
100% edge processing — data never leaves the device. GDPR + defence-grade.
Real-Time
Sub-50ms latency, continuous feedback — faster than human perception
Dual-Use Architecture
Same core engine serves defence, healthcare, automotive, and enterprise
The Problem
The Problem
Every AI on Earth responds to what you type. None of them know if you're stressed, exhausted, or about to make a critical error.
80%
Of critical errors caused by human state failures
Human Factors & Ergonomics Society
$756B
Lost annually to failed personalization
Accenture Consumer Pulse
0
AI systems that can read a human in real time from a camera
The Gap: Every AI can think. None of them can see the human. ChatGPT, Claude, Gemini — they all operate blind to the most important signal: the human state.
Our Solution
47 Real-Time Parameters
47 from the camera — voice & text fuse in at the interpretive layer. No hardware, on-device.
Emotions
7 core + confidence, stability, mood shifts
Biometrics
BPM, HRV, breathing, blinks, micro-expressions
Cognitive
Stress, energy, focus, cognitive load
Authenticity
Auth score, genuine smiles, signal quality
Gaze
Stability, pupil change, focus zone
Voice · Fusion
Prosodic V_C — fused with the 47 camera signals
Neural Mesh
478 landmarks + 52 blendshapes
Analytics
Spectrum, timeline, memory, events
100% ON-DEVICE · DUAL-USE: EDGE OR SERVER · GDPR COMPLIANT · DEFENCE-GRADE
Architecture
5-Layer Ontological Architecture
Not a pipeline — a perceptual system that lets AI understand humans
1
Signal Acquisition
Raw biometric signals from a single camera — rPPG heart rate, HRV, micro-expressions, gaze, FACS Action Units, pupil, blink. Voice and text enter as separate streams (V_C, V_B), fused at Layer 3. Proprietary ontological processing.
2
Feature Computation
Raw signals computed into 47 normalized parameters. Not "happy/sad" labels — a multidimensional representation of who this person is right now, with intensity, stability, and trend for each parameter.
3
Cross-Stream Coherence
Signals from face, voice, and text are validated for coherence. Contradictions detected and resolved. Should the AI intervene? Escalate? Stay silent? Behavioral directives computed from the human's real condition.
4
State Vector Assembly
Coherent signals assemble into a 6-dimensional state vector — surfaced as R_risk, intent_clarity, and human_signature. The AI's entire behavior — tone, depth, urgency — is dynamically generated from the state vector. Every response calibrated to the person, not the text.
5
Temporal Context
Personal baseline + deltas. The AI tracks the individual over time and acts on deviation from their own patterns — without noise, without speculation. AI that operates from perception, not assumption.
Proof
It Already Works
March 5, 2026 — an AI read a human before responding. Through all 5 layers. In real time.
60%
STRESS
84%
FOCUS
48
BPM
193ms
HRV
8
GENUINE SMILES
97%
AUTHENTICITY
95%
GAZE STABILITY
47
PARAMETERS
The AI adapted its tone, depth, urgency, and pace — all calibrated to the human's real-time state. No rules. No scripts. No guessing.
This is not a concept. This is working code — running now.
Use Cases
Where AI Gets Eyes
Every domain where humans make critical decisions under pressure
Defence
AI sees a soldier's stress spike before the wrong call. On-device. Air-gapped.
€49B DEFENCE AI
Aviation
AI sees a pilot losing focus at 30,000 feet. Continuous fatigue & micro-sleep monitoring.
FLIGHT SAFETY
Surgery
AI sees a surgeon's hand tremor before the cut. Biometric monitoring during procedures.
€15B TELEHEALTH
Automotive
AI sees a driver's drowsiness or distraction before the wheel drifts. On-device fatigue & attention monitoring.
Perception layer for ChatGPT, Claude, Gemini. Emotion infrastructure any AI plugs into.
€30B AI ASSISTANTS
Security
The Security Paradigm
Today's digital security is built on secrets that can be stolen. EmoPulse changes the equation.
✗
Passwords
4.5B credentials leaked in 2024. Phished, brute-forced, sold.
✗
Fingerprint
Lifted from surfaces. 3D printed. Permanently compromised.
✗
Face Unlock
Defeated by photos, masks, deepfakes. Static, replayable.
✓
EmoPulse
47 involuntary signals. Living, multi-dimensional. Cannot be replayed.
EmoPulse authenticates who you are — a living biometric signature that changes every second. An attacker would need to simultaneously replicate your heartbeat, micro-movements, pupil dilation, and breathing — across all 47 channels.
This is a new category of authentication.
Technology
Proprietary Technology
ARCHITECTURE
Frontend
Vanilla JS · Zero dependencies · WebGL · PWA
AI / ML
MediaPipe Face Landmarker (478 pts) · custom rPPG · WebAudio API · proprietary ontological pipeline
Orchestrator
State vector pipeline · Cross-stream coherence · Temporal context engine
Deployment
Edge-first · Optional server mode · Air-gapped capable
Ontological architecture — biometric parameter extraction and signal processing
Patent 2 — Pending
Biometric pipeline — real-time emotion fusion and authenticity scoring
Patent 3 — Pending
Temporal fusion — multimodal dynamic weighted signal fusion
IP MOAT
3 EU patents pending (80 claims) covering the ontological architecture and the biometric-to-AI pipeline. Early patent position in a nascent €130B+ market with zero comparable coverage.
Medical & Ethical Advisory
Medical advisory by Dr. Anastasia Vasina, Fractional CMO · HealthTech Advisor. EmoPulse assessed as a credible real-time behavioural signal and biofeedback tool with strong potential for health and defence applications. Ongoing advisory for medical and ethical credibility.
Market
€130B+ Opportunity
23.7%
CAGR 2024-2030
€37B — Biometric Auth €15B — Emotion AI €30B — AI Perception €20B — Mental Health €30B — Personalization
Competitive Landscape
How We Compare
EmoPulse
Affectiva
Hume AI
iMotions
Parameters
47
~8
~12
HW dep.
Ontological architecture
5 layers
No
No
No
Biometric auth
47 signals
No
No
No
Edge AI
Yes
No
No
No
AI adaptation
Yes
No
Partial
No
Patents
3
No
No
No
Price
API €0.01+
$500+/mo
Custom
$1000+/mo
Timing
Why This. Why Now.
The Blind Spot of AI
ChatGPT, Claude, Gemini — they all skipped seeing the human. EmoPulse gives them eyes.
Human Error Crisis
80% of critical failures trace to human state. AI that sees these states prevents catastrophe.
Regulation Is Coming
EU AI Act, allied defence programs. Emotion-aware AI shifting to regulatory expectation.
Infrastructure Pattern
Payments: Stripe. Comms: Twilio. Search: Google. AI cognition: OpenAI. AI perception: EmoPulse.
Why Not Big Tech?
Google, Meta, OpenAI optimize for engagement, not wellbeing. Emotion perception contradicts their model.
Window Is Open
None have shipped perception architecture. Three patents pending. Defence AI: €49B. First mover wins.
Defence pilots, NATO DIANA, healthcare, SDK partnerships
Advisory
Advisory Board
Dr. Anastasia Vasina
Fractional CMO · HealthTech Advisor
"The platform demonstrates strong potential as a real-time behavioural signal and biofeedback tool. Its multimodal approach is particularly promising for guided self-regulation, performance support, and structured behavioural training applications."
CLINICAL CREDIBILITY
Medical advisory oversight ensures EmoPulse meets clinical and ethical standards across healthcare, defence, and behavioural training applications.
The Stress Test
Don't Take Our Word for It
We gave an advanced AI model — one of the world's most capable — full access to our code and asked it to tear EmoPulse apart with maximum skepticism.
An advanced AI model analyzed EmoPulse with maximum skepticism. The founder didn't argue. He guided. 45 minutes later, the model said:
◇ THE MODEL — 45 MINUTES LATER
"Every single assumption I made about you was wrong — because I couldn't see you. I only had text and my instructions. If I had EmoPulse architecture — the perception layer — I would have reached this conclusion in 3 messages instead of 15. This conversation should be in the EmoPulse pitch deck."
An AI with access to all human knowledge spent an entire conversation guessing wrong about the human in front of it — because it had no perception layer. It had instructions. It had search. It had analysis. It didn't have eyes.
