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Presentation Overview

Opening

Market opportunity and conversational data potential

Framework Foundation

The 5-step NeoSapien framework explained

NeoSapien Implementation

How we use our own framework internally

Healthcare Deep Dive

Clinical documentation and patient care applications

Manufacturing & Supply Chain

Operational efficiency and communication optimization

Financial Services

Customer experience and compliance applications

Implementation & Future

Roadmap and responsible AI considerations

Unlocking Business Value with GenAI-Powered Conversational Intelligence

Aryan Yadav, Co-founder & CTO

NeoSapien

The Conversational Data Opportunity

80% of business-critical information flows through conversations

Yet 95% of this data remains unstructured and untapped

Market Opportunity 2025

$66.89B
GenAI Market Size
36.99%
CAGR 2025-2031
72%
Enterprise Adoption

The Intelligence Gap

Traditional systems capture transactions

But miss the why behind decisions

Conversations hold the context that transforms data into wisdom

Why Traditional BI Fails

Traditional BI

  • Backward-looking reports
  • Structured data only
  • Manual analysis required
  • Delayed insights

Conversational Intelligence

  • Real-time insights
  • Unstructured data capture
  • AI-powered analysis
  • Predictive capabilities

The GenAI Advantage

GenAI doesn't just analyze conversations

It understands context, emotion, and intent

Transforming every interaction into actionable intelligence

Introducing the NeoSapien Framework

A universal 5-step approach to conversational intelligence

1. Capture
2. Extract
3. Integrate
4. Analyze
5. Act

Framework Step 1: Capture

Always-on conversational data collection

Neo 1 wearable devices
API integrations
Mobile applications
Existing communication platforms

Seamlessly capture every business conversation across all channels

Framework Step 2: Extract

Multi-modal signal processing

Advanced speech-to-text
Natural language processing
Emotion detection
Acoustic analysis

Transform raw audio into structured, analyzable data

Framework Step 3: Integrate

Business context fusion

CRM integration
ERP connection
EHR synchronization
Knowledge graph construction

Enrich conversations with business context and historical data

Framework Step 4: Analyze

GenAI-powered insights

Large Language Models
Multi-agent systems
Predictive analytics
Pattern recognition

Generate actionable insights from integrated conversational data

Framework Step 5: Act

Automated workflows and alerts

API-driven actions
Intelligent dashboards
Smart routing
Real-time notifications

Trigger immediate actions based on conversational insights

NeoSapien Internal Implementation

How we eat our own dog food

Customer calls → Neo 1 captures
Extract pain points & requirements
Integrate with CRM & product roadmap
AI analyzes feature requests & sentiment
Auto-update tickets & notify product team

Our Results: Real-World Metrics

65%
Faster Issue Resolution
40%
Reduction in Documentation Time
90%
Accuracy in Feature Prioritization

Lessons from Our Journey

The framework is technology-agnostic

Success depends on integration depth

User adoption requires demonstrable value

Start small, scale systematically

Healthcare: The Documentation Crisis

15.5
Hours/week on paperwork
16.6%
Of working hours on admin
26.7
Hours/day needed for guideline care

Healthcare Use Case 1: Clinical Documentation

Automated clinical note generation

Doctor-patient conversation captured
AI extracts symptoms, diagnosis, treatment plan
Integrates with EHR and medical protocols
Generates structured clinical notes
Auto-submits to medical records

Result: 80% reduction in documentation time

Healthcare Use Case 2: Discharge Summaries

Comprehensive discharge documentation

Capture discharge planning conversations
Extract medications, follow-up instructions
Integrate with pharmacy and scheduling systems
Generate patient-friendly summaries
Auto-send to patient and care team

Result: 95% reduction in readmission communication errors

Healthcare Use Case 3: ICU Counseling

Critical care family communication

Family counseling sessions recorded
Extract emotional states and concerns
Integrate with patient care plans
Identify support needs and resources
Alert social services and chaplain

Result: 60% improvement in family satisfaction scores

Healthcare Use Case 4: Medical Education

Enhanced learning through conversation analysis

Student-patient interactions captured
Extract diagnostic reasoning patterns
Integrate with medical knowledge base
Generate personalized feedback
Recommend targeted learning resources

Result: 45% faster clinical skill development

Healthcare ROI Potential

Documentation Time
12.4 hours/week saved
$65,000 annually per physician
Accuracy Improvement
90% fewer errors
$45,000 risk reduction per physician
Patient Throughput
2.5 more patients/day
$180,000 additional revenue
Total ROI: $290,000 per physician annually

Framework Applied: Healthcare

1
Capture
Neo 1 during patient consultations
2
Extract
Medical terms, symptoms, treatment plans
3
Integrate
EHR, medical protocols, drug databases
4
Analyze
Clinical decision support, risk assessment
5
Act
Auto-generate notes, trigger alerts

Manufacturing: Communication Breakdown Costs

$260,000
Cost per hour of downtime
800
Annual downtime hours
$50B
Annual unplanned downtime cost

Manufacturing Use Case 1: Maintenance Documentation

Automated maintenance records

Technician maintenance conversations captured
Extract equipment issues and solutions
Integrate with CMMS and parts inventory
Generate maintenance reports and schedules
Update preventive maintenance plans

Result: 70% reduction in maintenance documentation time

Manufacturing Use Case 2: Quality Control Communications

Real-time quality issue tracking

QC inspector conversations recorded
Extract defect patterns and root causes
Integrate with quality management systems
Trigger corrective actions and alerts
Update quality control procedures

Result: 85% faster defect resolution

Supply Chain Use Case 1: Supplier Communication Analysis

Supplier relationship optimization

Supplier calls and meetings captured
Extract delivery commitments and risks
Integrate with procurement and inventory
Predict supply chain disruptions
Auto-adjust procurement schedules

Result: 60% improvement in on-time delivery

Supply Chain Use Case 2: Logistics Optimization

Intelligent route and resource planning

Driver and dispatcher communications logged
Extract route issues and delays
Integrate with GPS and traffic systems
Optimize routing algorithms
Auto-reroute and notify customers

Result: 25% reduction in delivery times

Manufacturing ROI Potential

Downtime Reduction
200 hours/year saved
$52M annually per facility
Quality Improvements
90% fewer defects
$8M cost avoidance
Documentation Efficiency
15 hours/week per technician
$3.2M labor savings
Total ROI: $63.2M per facility annually

Framework Applied: Manufacturing

1
Capture
Shop floor communications, maintenance calls
2
Extract
Equipment status, quality issues, safety concerns
3
Integrate
CMMS, ERP, quality systems, inventory
4
Analyze
Predictive maintenance, quality patterns
5
Act
Auto-schedule maintenance, alert quality team

Financial Services: Compliance & Customer Experience

$4.66B
AI voice market in 2025
50%
Switch after one bad experience
30%
Cost reduction with AI

Banking Use Case 1: Customer Service Enhancement

Intelligent customer interaction analysis

Customer service calls captured
Extract sentiment, intent, and satisfaction
Integrate with CRM and account data
Real-time coaching for agents
Auto-escalate critical issues

Result: 40% improvement in first-call resolution

Banking Use Case 2: Regulatory Compliance

Automated compliance monitoring

All customer interactions monitored
Extract compliance-relevant content
Integrate with regulatory frameworks
Identify potential violations
Generate compliance reports

Result: 95% reduction in compliance review time

Insurance Use Case 1: Claims Processing

Automated claims assessment

Claims calls and interviews captured
Extract incident details and damages
Integrate with policy and claims systems
Auto-assess claim validity and amount
Expedite payment or flag for review

Result: 75% faster claims processing

Insurance Use Case 2: Fraud Detection

Advanced fraud pattern recognition

Claims conversations analyzed
Extract behavioral and linguistic patterns
Integrate with historical fraud data
AI identifies suspicious patterns
Alert fraud investigation team

Result: 85% improvement in fraud detection accuracy

Financial Services ROI Potential

Customer Service
30% cost reduction
$15M annually per 1000 agents
Compliance Efficiency
80% time savings
$8M in operational costs
Fraud Prevention
85% detection improvement
$25M in prevented losses
Total ROI: $48M per 1000 agents annually

Framework Applied: Financial Services

1
Capture
Customer calls, video conferences, meetings
2
Extract
Intent, sentiment, compliance indicators
3
Integrate
CRM, core banking, regulatory databases
4
Analyze
Risk assessment, fraud detection, satisfaction
5
Act
Auto-route, flag risks, update records

Implementation Roadmap

Phase 1: Pilot (Months 1-3)

  • Single use case implementation
  • Core team training
  • Basic integration setup

Phase 2: Scale (Months 4-8)

  • Expand to multiple departments
  • Advanced integrations
  • Workflow automation

Phase 3: Transform (Months 9-12)

  • Enterprise-wide deployment
  • AI-driven decision making
  • Continuous optimization

Responsible AI Considerations

Building AI systems that are ethical, transparent, and trustworthy

🔒

Privacy & Security

End-to-end encryption, GDPR compliance, data residency controls

• Zero-trust architecture • Data minimization • Regular security audits
👁️

Transparency

Explainable AI decisions, audit trails, human oversight

• Decision reasoning logs • Model interpretability • Clear AI boundaries
⚖️

Bias Mitigation

Diverse training data, regular bias testing, fairness metrics

• Inclusive datasets • Algorithmic fairness • Continuous monitoring
🎛️

Human Control

Human-in-the-loop design, override capabilities, ethical guidelines

• Override mechanisms • Ethical review boards • User consent controls

Trustworthy AI

All principles work together to create AI systems that users can trust and rely on

The Future of Conversational Intelligence

Emerging capabilities that will transform human-AI interaction

Evolution Timeline

2025 2027 2030+
🎭

Multimodal Intelligence

Video, body language, environmental context

2025-2027 • Emotion recognition • Gesture understanding • Context awareness
🔮

Predictive Conversations

AI that anticipates needs before they're spoken

2027-2029 • Intent prediction • Proactive assistance • Behavioral modeling
🤖

Autonomous Actions

AI agents that act independently on insights

2029-2032 • Decision automation • Cross-system integration • Ethical reasoning
🌍

Universal Translation

Breaking down language barriers in real-time

2025-2030 • Real-time translation • Cultural adaptation • Voice preservation

🚀 Transformative Impact

These capabilities will create seamless, intuitive AI interactions that feel natural and human-like

Discussion & Q&A

How can conversational intelligence transform your industry?

Aryan Yadav

Co-founder & CTO, NeoSapien

aryan@neosapien.xyz