PN
Prrashanth H. Nagaraj
DIGITAL TRUST ARCHITECT • INDEPENDENT DIRECTOR •
RESPONSIBLE GROWTH STRATEGIST
PAGE 3 • 6 USE CASES • SAME PIPELINE, DIFFERENT HUMAN ACTION • CLICK ANY CARD

Same 5 signals.
Different human action.
Not theory, product.

Patent evidence generated in autonomous recruitment 2019, before generative avatars. Then ecommerce, tutor, meetings, support. Same pipeline — transcript + verbal + visual → scoring → live adaptation — but action changes per context.

✓ 2019 Recruitment • First Deployment✓ Text, Audio, Video • EQ at core
THE PATTERN • ONE PIPELINE, MANY ACTIONS
👁️
Signal
Hand raise / Flat tone / "Not sure"
↓
📊
AI MODEL 116
Conviction 42% LOW • Receptivity 78% HIGH
↓
MEETING
→ Pause, simplify
TUTOR
→ Stop when distracted
ECOM
→ Don't upsell, help
SUPPORT
→ Train, not fix
01
ORIGIN • 2019
551315

Autonomous Recruitment

Candidate says "I'm confident" — old bot passed as confident. Missed flat tone, eye drop, slouch.

42% LOW78% HIGH
View Use Case →
02
ONLINE MEETINGS
MOST REQUESTED

Meeting That Knows Who Is Actually Paying Attention

What we have now: transcript + summary + CTAs. EI gives: attentiveness % per participant.

Transcript+Summary+CTAAttentiveness
View Use Case →
03
ECOMMERCE
Relevance, not predation

Ecommerce That Doesn't Upsell When You Hesitate

Old satisfaction score counted "this is good" but missed 2am comparison for 3 weeks.

F6 IntentSemantic, not sentiment
View Use Case →
04
TUTOR
NOT RECORDED VIDEO

Tutor That Stops When Student Is Distracted

Old plays like recorded video even when student not there — marks 60% complete.

Plays when absentStops when distracted
View Use Case →
05
SUPPORT
TRAIN, NOT FIX

Support That Identifies Issue on Screen Share and Trains User

User stuck on web app — old gave KB article. EI: screen share repeated clicks + visual hand on head.

Repeated clicksTrain, not fix
View Use Case →
06
BONUS • LEADERSHIP
HUMAN-LIKE LEADERSHIP

Onboarding Coach That Suggests Break, Not More Modules

Yawn + slouch + eye drop = fatigue, not laziness. Old pushes next module.

Yawn + slouchBreak, simplify
View Use Case →
TRADITIONAL CONVERSATIONAL AI vs EI LAYER • DEFICIENCIES vs DISRUPTIVE DIFFERENTIATORS
TRADITIONAL • DEFICIENCIES
•Suitable only for text & audio conversations and not video conversations.
•Lacks on-the-go capability to dynamically generate virtual humanoid video responses. Satisfaction score = positive/neutral/negative word count.
•Completely ignores end-user's emotional quotient while responding back to queries.
•Mostly unidirectional AI models adopting sequential processing.
•Lacks multimodal capability in business use-cases restricting either to presales / post-sales.
EI LAYER 2022 • DISRUPTIVE DIFFERENTIATORS
•Capable of interacting in text, audio and video formats with emotional quotient at core. Semantic-based contextual analysis + objectified voice modulation + correlated body language.
•Patent pending video generator can generate human-based video responses in less than 5 seconds. Engine 210 live-updates during streaming.
•Patent 551315 algorithms to consider end-user emotions while responding to queries — empathy-based, not emotion label.
•Adopts bidirectional or non-sequential model that processes information in multiple directions.
•Custom experience promoting emotion bonding and connect between brands and consumers. Train, not just fix.
NEXT: WHY EI IS NOT EMOTION AI — THE DIFFERENCE THAT MATTERS FOR ADOPTION
Emotion AI labelled. EI adapts. One gives dashboard, other gives action.
WHY NOT EMOTION AI →FRAMEWORK