KERNELIUS HEALTH FORGE

Universal Infrastructure for AI Agents in Healthcare and Life Sciences

Investor Pitch Deck

Yam Catzenelson, Founder & CEO January 2026

Kernelius Health Forge

Universal Infrastructure for AI Agents in Healthcare and Life Sciences

Yam Catzenelson, Founder & CEO

January 2026

SLIDE 2: THE PROBLEM - Healthcare Lacks Infrastructure for AI Agents

The 17-Year Evidence-to-Practice Gap

It takes an average of 17 years for medical evidence to reach clinical practice
Healthcare organizations can't systematically improve knowledge
Life sciences research is fragmented and non-reproducible
Result: Patients receive outdated care, research progress is slow

AI Agents Are Being Adopted - But Without Infrastructure

According to Deloitte's 2026 industry outlooks:

Life Sciences:

78% of biopharma and medtech executives expect AI to drive major change in 2026
But only 22% have successfully scaled AI
Only 9% report achieving significant returns on AI efforts
Leading organizations (Mayo Clinic, Dana-Farber, etc.) are deploying AI agents in ad-hoc, uncontrolled ways

Healthcare Delivery:

Over 80% of health system and health plan executives see gen AI delivering moderate-to-significant value
But 49% are still experimenting; 18% haven't adopted at all
Only one-third have scaled AI successfully
43% of leaders feel "uncertain" about the industry outlook, up from 28% last year

The Critical Gap

Organizations lack infrastructure to:

❌ Control and govern AI agent use across workflows
❌ Validate agent outputs systematically
❌ Share and reuse successful configurations
❌ Track changes and maintain compliance
❌ Collaborate on improvements
❌ Learn from outcomes

Every team is reinventing the wheel. Every workflow is ad-hoc. There's no systematic way to make AI agents work reliably at scale.

SLIDE 3: WHY NOW - The Infrastructure Crisis

AI Agents Are Being Deployed Across ALL Workflows

Healthcare workflows using AI today:

Clinical care (diagnosis, treatment decisions, patient monitoring)
Operations (prior authorization, scheduling, revenue cycle)
Quality improvement (pathway optimization, outcomes analysis)
Administrative (documentation, coding, compliance)

Life sciences workflows using AI today:

R&D (literature review, hypothesis generation, experimental design)
Clinical trials (design, site selection, safety monitoring)
Regulatory (submission preparation, compliance, surveillance)
Commercial (market access, medical affairs, patient support)

But Scaling Is the Critical Challenge

From Deloitte research:

78-80% believe AI will deliver value across functions
Only 22-33% have scaled AI successfully
Only 9% report significant ROI from AI efforts
Gap between belief and reality is massive

Organizations Need Infrastructure NOW

New regulatory pressures create forcing functions:

CMS prior authorization: 14 days → 7 days (Jan 2026)
EU AI Act compliance requirements
EHDS (European Health Data Space) regulations
FDA guidance on AI/ML in medical devices

Cost Pressures Demand Efficiency:

Life sciences: Average drug development cost exceeds $2 billion
Healthcare: Only 38% of spending on prevention/wellness (62% wasted on reactive care)
Both sectors need systematic improvement, not incremental pilots

The organizations that solve infrastructure will win. Those that don't will fall behind.

SLIDE 4: THE INSIGHT - Software Solved This with Git/GitHub

What Makes Software Development Productive at Scale?

Git/GitHub methodology provides:

Version Control

Every change tracked with full history
Who changed what, when, and why
Easy rollback if something breaks

Branching & Experimentation

Safe to test new approaches without breaking production
Multiple developers explore solutions in parallel
Best approach wins through validation

Merge Validation (CI/CD)

Automated testing before code reaches production
Quality gates ensure standards are met
Continuous integration prevents bugs

Collaboration Infrastructure

Fork projects and adapt for your needs
Contribute improvements back to community
Shared language and workflows across all developers

Continuous Improvement

Learn from what works across millions of developers
Best practices emerge and propagate
Knowledge compounds exponentially

Result: Software moves fast while staying reliable. Developers collaborate globally. Knowledge evolves at internet speed.

Healthcare and Life Sciences Have NONE of This

Clinical protocols, research workflows, and operational processes:

❌ No version control
❌ No safe experimentation infrastructure
❌ No systematic validation before deployment
❌ No collaboration infrastructure
❌ No continuous improvement mechanisms
❌ Siloed knowledge, slow evolution

But the workflows are remarkably similar to software development:

Complex knowledge work
Multiple stakeholders collaborating
Need for quality assurance
Requirement for audit trails
Continuous need for improvement
High cost of errors

The Opportunity: Apply proven software development methodology to ALL healthcare and life sciences workflows powered by AI agents.

SLIDE 5: THE SOLUTION - Universal Infrastructure for AI Agents

Kernelius Health Forge: The infrastructure layer that makes AI agents work reliably at scale across ALL healthcare and life sciences workflows

Not Just for One Use Case - Infrastructure for ANY Workflow

Healthcare:

Clinical care: Diagnosis, treatment decisions, patient monitoring, care coordination
Operations: Prior authorization, scheduling, billing, resource allocation
Quality improvement: Pathway optimization, outcomes tracking, performance analytics
Administrative: Documentation, coding, compliance, credentialing

Life Sciences:

R&D: Literature review, hypothesis generation, experimental design, data analysis
Clinical trials: Trial design, site selection, patient recruitment, safety monitoring
Regulatory: Submission preparation, compliance checking, post-market surveillance
Commercial: Market access strategy, medical affairs, patient support programs

Every Workflow Gets the Same Powerful Infrastructure

1. Agent Orchestration

User-facing agents (Cowork interface for any role)
Background agents (long-running tasks for any domain)
Multi-agent collaboration across functions

2. Git Methodology

Version control for any knowledge artifact
Branching for safe exploration of alternatives
Merge validation before production deployment
Full audit trail for any decision or change

3. CI/CD Infrastructure

Automated validation agents check quality
Domain-specific checks (safety, evidence, compliance, feasibility)
Quality gates prevent errors
Continuous monitoring of outcomes

4. Skills + MCP Ecosystem

Pre-built capabilities for common workflows
Integrations with domain-specific tools
Environment templates for rapid deployment
Extensible and composable architecture

5. Collaborative Layer

Share configurations within organization (Layer 2)
Share successful patterns across organizations (Layer 3)
Learn from community best practices
Network effects make platform stronger over time

6. Outcomes Learning

Track results of every agent-assisted workflow
Feed learnings back into validation
Protocols and workflows improve continuously
Data flywheel creates sustainable competitive advantage

Three-Layer Architecture Supports ALL Workflows

Layer 1: Healthcare Cowork (Universal Agent Interface)

Works for clinicians, researchers, quality analysts, operations staff, etc.
Natural language interaction with agents
File/folder access for any work product
Task assignment and orchestration
Adapts to any workflow's unique needs

Layer 2: Organizational Infrastructure

Git-based version control for ANY knowledge artifact
Background agent orchestration for ANY long-running task
CI/CD validation pipelines for ANY decision type
Governance and compliance frameworks
Works seamlessly across ALL departments and workflows

Layer 3: Community Network

Share ANY workflow configuration across organizations
Fork and adapt best practices from community
Contribute improvements back
Network effects compound value exponentially
Accelerates evidence-to-practice from 17 years to months

We're Not Just Providing Agents - We're Providing the Complete Ecosystem That Makes Agents Work at Scale

SLIDE 6: HOW IT WORKS - Complete Platform Architecture

Universal Tooling Ecosystem Addressing Real Workflow Needs

Skills Library - Organized by Domain

Clinical Care Skills (addressing 41% who cite care delivery transformation):

clinical_decision_validator - validates treatment decisions against protocols
patient_data_summarizer - synthesizes EHR data for clinical review
treatment_option_explorer - creates branches for alternative treatments
care_pathway_optimizer - analyzes and improves clinical pathways
patient_monitoring_agent - continuous surveillance for deterioration
discharge_planner - coordinates post-acute care

Operations Skills (addressing 26% who cite affordability challenges):

prior_auth_processor - automates authorization workflows (CMS 7-day compliance)
revenue_cycle_optimizer - improves billing and collections
scheduling_coordinator - optimizes resource allocation
compliance_auditor - ensures regulatory adherence
supply_chain_manager - predicts and prevents shortages

Quality & Safety Skills:

safety_checker - validates clinical safety across decisions
guideline_compliance_verifier - ensures evidence-based practice
outcomes_analyzer - tracks and predicts patient outcomes
adverse_event_detector - identifies safety signals early

Research Skills (addressing $2B+ drug development costs):

literature_systematic_reviewer - comprehensive evidence synthesis
hypothesis_generator - identifies research opportunities
experimental_designer - optimizes study protocols
data_pipeline_builder - creates reproducible analysis workflows
statistical_validator - ensures methodological rigor

Clinical Trials Skills:

trial_designer - optimizes Phase I/II/III study design
site_selector - identifies optimal trial sites
enrollment_predictor - forecasts recruitment timelines
safety_monitor - real-time adverse event analysis
data_quality_checker - validates trial data integrity

Regulatory Skills:

submission_preparer - automates FDA/EMA submissions
compliance_checker - validates regulatory requirements
label_updater - manages product labeling changes
surveillance_monitor - post-market safety tracking

Background Agent Infrastructure - Meeting the Scaling Challenge

Addressing the gap where only 22-33% have scaled AI:

Agent Types:

Scheduled agents: Run daily/weekly (literature monitoring, compliance checks)
Event-triggered agents: Respond to specific triggers (new guidelines, safety alerts)
Long-running agents: Hours-to-days tasks (comprehensive reviews, analyses)
Continuous agents: Always-on monitoring (patient surveillance, trial safety)

Orchestration Features:

Parallel execution across multiple branches
Priority queuing for urgent tasks
Resource management and optimization
Progress tracking and notifications
Automatic retry and error handling

MCP Servers (Integrations) - Connecting Essential Tools

Healthcare MCP Servers:

EHR integration (Epic, Cerner) - patient data, orders, results
- Addresses prior authorization burden (CMS 7-day requirement Jan 2026)
Clinical guidelines (UpToDate, AHA, ADA, NCCN) - evidence-based recommendations
Drug databases (Lexicomp, Micromedex) - formularies, interactions, dosing
Quality metrics (CMS measures, HEDIS, Joint Commission) - performance tracking
Lab systems (Cerner PathNet, Epic Beaker) - test results and ordering

Life Sciences MCP Servers:

PubMed/MEDLINE - literature search and retrieval
- Supporting 43% expanding into new therapeutic areas
ClinicalTrials.gov - trial registry and results database
Regulatory databases (FDA, EMA, PMDA) - guidance and submission requirements
Lab information systems (LIMS) - experimental data management
Genomics databases (dbSNP, ClinVar, TCGA) - genetic variation data
Protein databases (PDB, UniProt) - structural biology data
Drug databases (DrugBank, ChEMBL) - compound information

Environment Templates - Pre-configured for Common Scenarios

Healthcare Environments:

hospital_clinical_care_env - Full suite of clinical decision support
payer_operations_env - Prior auth, utilization management, quality
quality_improvement_env - Analytics, pathway optimization, outcomes
ambulatory_care_env - Outpatient workflows, chronic disease management

Life Sciences Environments:

pharma_rd_env - Discovery, preclinical, translational research
clinical_trials_env - Trial design, monitoring, analysis
regulatory_affairs_env - Submissions, compliance, surveillance
market_access_env - Pricing, reimbursement, medical affairs

CI/CD Validation Pipelines - Ensuring Quality

Validation Agent Types:

Safety agents: Check clinical safety, drug interactions, contraindications
Evidence agents: Grade evidence quality, assess support for decisions
Protocol agents: Verify compliance with organizational protocols
Regulatory agents: Ensure regulatory requirements are met
Outcomes agents: Predict impact on patient/organizational outcomes
Cost agents: Assess financial implications
Feasibility agents: Check resource availability and practical constraints

Pipeline Stages:

Syntax check: Validate data structure and format
Domain validation: Check domain-specific requirements
Safety validation: Run safety checks
Evidence validation: Assess supporting evidence
Outcome simulation: Predict results
Human review: Flag for approval when needed
Deployment: Merge to production with audit trail

The Pattern Is Universal - Same Infrastructure, Different Domains

SLIDE 7: USE CASE - Clinical Decision Support

Scenario: Dr. Jones Treating Patient with Complex Chronic Conditions

Patient Context:

65-year-old female
Type 2 diabetes, hypertension, stage 3 chronic kidney disease
Current medications not achieving blood pressure targets
Complex medication regimen (8 medications)

Today (Without Kernelius)

Doctor reviews chart manually (15-20 minutes)
Googles "hypertension diabetes kidney disease treatment"
Checks UpToDate (may be outdated, generic guidance)
Considers options based on memory and experience
Uncertain about interactions with existing medications
No systematic validation of decision
No comparison of alternatives
Limited evidence synthesis
No audit trail of decision process
No learning from outcomes
Timeline: 30-45 minutes, moderate-to-high uncertainty

With Kernelius Health Forge

Step 1: Agent Summarizes Patient (Layer 1 - Cowork)

Doctor opens Kernelius Cowork
Agent (via EHR MCP) pulls and synthesizes:
  • Key conditions: Diabetes + hypertension + CKD stage 3
  • Current meds: Metformin, lisinopril, atorvastatin, etc.
  • Recent labs: HbA1c 8.5%, BP 152/94, eGFR 55, K+ 4.2
  • Flags: Uncontrolled BP, kidney function declining
  • Risk factors: Cardiovascular disease, progression to ESRD
Display: Clear 2-minute summary with key decision points

Step 2: Doctor Explores Options (Git Methodology)

Doctor asks: "What are my treatment options for better BP control?"

Agent creates 4 exploration branches:
  Branch 1: "Increase lisinopril dose"
  Branch 2: "Add calcium channel blocker (amlodipine)"
  Branch 3: "Switch to ARB (losartan) - kidney protective"
  Branch 4: "Add thiazide-like diuretic (chlorthalidone)"

Each branch represents a parallel exploration of treatment space

Step 3: Infrastructure Validates Each Branch (Layer 2 - CI/CD)

Background validation agents run in parallel on all 4 branches:

Branch 1 (Increase lisinopril):
  protocol_agent: ✓ Within hypertension protocol
  safety_agent: ⚠️  Already at max recommended dose (40mg)
  evidence_agent: Level C (weak evidence for further increase)
  outcomes_agent: Predicts modest BP reduction (5 mmHg)
  
Branch 2 (Add amlodipine):
  protocol_agent: ✓ Standard second-line per protocol
  safety_agent: ✓ No contraindications
  evidence_agent: Level A (strong evidence)
  outcomes_agent: Predicts 12 mmHg BP reduction
  cost_agent: $35/month (generic, insurance covers)
  
Branch 3 (Switch to ARB):
  protocol_agent: ✓ Preferred for diabetic nephropathy
  safety_agent: ✓ Excellent safety profile
  evidence_agent: Level A (ALTITUDE, RENAAL, IDNT trials)
  outcomes_agent: Predicts 15 mmHg BP reduction
  kidney_protection_agent: ✓✓ Strong kidney protection data
  cost_agent: $48/month (generic, insurance covers)
  clinical_trials_agent: 3 major trials show kidney benefit
  
Branch 4 (Add diuretic):
  protocol_agent: ✓ Appropriate combination therapy
  safety_agent: ⚠️  Monitor electrolytes closely with CKD
  evidence_agent: Level B (moderate evidence)
  outcomes_agent: Predicts 10 mmHg BP reduction
  monitoring_agent: Requires lab follow-up in 2 weeks

Step 4: Final Validation & Decision

Doctor decides: "Branch 3 - Switch to losartan 50mg daily"

Agent generates recommendation with rationale:
  Drug: Losartan 50mg daily (replace lisinopril 40mg)
  Evidence: Level A (RENAAL, IDNT, ALTITUDE trials)
  Expected Benefits:
    • Blood pressure reduction: ~15 mmHg systolic
    • Kidney protection: Slows CKD progression
    • Cardiovascular risk reduction
  Monitoring: Recheck BP, kidney function, K+ in 4 weeks
  
Decision logged with full audit trail
Prescription sent to EHR/pharmacy

Step 5: Outcomes Learning (Continuous Improvement)

In 4 weeks, patient returns:
  • BP: 138/82 (improved from 152/94)
  • eGFR: 54 (stable)
  • K+: 4.5 (safe)
  
Outcomes feed back into Kernelius:
  • Decision pattern validated
  • Similar patients benefit from this learning
  • Protocol may be refined based on outcomes

Impact

Time Savings:

30-45 minutes → 5-10 minutes (70-80% reduction)

Quality Improvements:

Evidence-based decision with Level A support
Systematic evaluation of alternatives
Patient-specific optimization (kidney protection prioritized)

Safety:

All drug interactions checked automatically
Contraindications flagged
Monitoring plan built in

Compliance:

Full audit trail for malpractice protection
Protocol adherence documented
Quality metrics automatically generated

SLIDE 8: USE CASE - Prior Authorization Automation

Scenario: Health Plan Processes Prior Authorization for Expensive Biologic

Context:

Health plan processes 10,000+ prior authorizations per day
New CMS requirement: 7-day decision turnaround (down from 14 days, effective Jan 1, 2026)
Must publicly report metrics by March 31, 2026
Manual process can't scale to meet new requirements

Today (Without Kernelius)

Prior auth request arrives via fax/portal
Staff manually enters data (10-15 minutes)
Clinical reviewer pulls member chart
Reviewer searches policy documents
Checks formulary manually
Reviews clinical notes
Checks step therapy requirements
May call physician office (1-3 days wait)
Timeline: 5-14 days, high variability, resource-intensive

With Kernelius Health Forge

Step 1: Automated Processing

Prior auth request arrives
Agent automatically:
  • Extracts structured data
  • Pulls member information via MCP
  • Retrieves clinical documentation from EHR
  • Creates case file

Step 2: Validation (Layer 2 - CI/CD)

Validation agents run in parallel:

policy_compliance_agent:
  ✓ All medical necessity criteria met
  ✓ Failed ≥2 conventional DMARDs
  ✓ No contraindications
  ✓ Prescriber is rheumatologist
  
safety_agent:
  ✓ No active infection, TB negative
  ✓ No contraindications
  
cost_agent:
  • Originator: $7,000/month
  • Biosimilar: $4,500/month
  • Recommend biosimilar (save $30K annually)
  
regulatory_compliance_agent:
  ✓ CMS Part D compliant
  ✓ 7-day timeline met (currently Day 1)

Step 3: Decision & Logging

Recommendation: Approve adalimumab-atto (biosimilar)

Rationale:
  ✓ All medical necessity criteria met
  ✓ No contraindications
  ✓ ACR guidelines support use
  ✓ Cost-effective option
  
Decision logged:
  • Complete validation trail
  • Policy version used
  • Timestamp (4 hours from receipt)
  • Auto-approved per protocol
  
CMS reporting metrics auto-generated

Impact

Speed:

5-14 days → 4-8 hours (>90% reduction)
Meets CMS 7-day requirement easily

Consistency:

100% policy compliance
Reduced appeals/overturns

Cost:

80% auto-approval rate
Biosimilar utilization: $2-3M monthly savings
Reduced staffing needs

Member Experience:

Faster access to therapy
Transparent decisions

SLIDE 9: USE CASE - Clinical Trial Design

Scenario: Biotech Designing Phase 2 Oncology Trial

Context:

Novel immunotherapy for non-small cell lung cancer
Phase 1 showed promising signals
Need optimal Phase 2 design
Goal: Submit IND amendment in 60 days

Today (Without Kernelius)

Manual literature review (weeks)
Consults with advisors
Drafts protocol based on experience
Internal reviews and iterations
Uncertainty about optimal design
Risk of amendments later
Timeline: 12-16 weeks, high design risk

With Kernelius Health Forge

Step 1: Design Space Exploration (Git Methodology)

Agent creates multiple design branches:

Dose Selection:
  Branch A1: "3mg/kg Q3W"
  Branch A2: "10mg/kg Q3W"
  Branch A3: "Dose escalation"

Primary Endpoint:
  Branch C1: "ORR" (standard Phase 2)
  Branch C2: "PFS" (more rigorous)
  Branch C3: "DOR" (durability focus)

Patient Selection:
  Branch D1: "PD-L1 positive only"
  Branch D2: "All PD-L1 levels"
  Branch D3: "Biomarker adaptive"

Agent evaluates combinations: 3×3×3 = 27 potential designs

Step 2: Evidence Synthesis (Skills + MCP)

literature_systematic_reviewer via PubMed MCP:
  • 47 relevant publications on PD-L2 inhibitors
  • Key finding: PD-L2 expression predicts response
  
clinical_trials_mcp via ClinicalTrials.gov:
  • 8 active competitor trials
  • Most using ORR as primary endpoint
  • Enriching for PD-L1+ patients

Step 3: Design Validation (Layer 2 - CI/CD)

For top design candidates:

feasibility_agent:
  ✓ Enrollment: 13.2 months predicted
  ✓ Budget: $4.2M (within range)
  ✓ Sites: 15 US sites capable
  
statistical_power_agent:
  ✓ N=60 patients (Simon 2-stage)
  ✓ Power: 90% to detect ORR difference
  
regulatory_compliance_agent:
  ✓ FDA Phase 2 guidance compliant
  ✓ IND amendment straightforward
  
competitive_analysis_agent:
  ✓ Differentiation: PD-L1 enrichment strategy
  ✓ Timeline advantage possible

Step 4: Protocol Auto-Generation

protocol_generator_agent creates:
  • Study schema and schedule
  • Inclusion/exclusion criteria
  • Statistical analysis plan
  • Safety monitoring plan
  • Biomarker strategy
  
All documents version-controlled
Ready for refinement and submission

Impact

Speed:

12-16 weeks → 7-10 days (>85% reduction)
Earlier trial start = earlier results

Quality:

Evidence-based design decisions
Comprehensive risk assessment
Optimized for success

Risk Reduction:

Statistical power ensured
Feasibility validated
Protocol amendments prevented

Cost:

$4.2M vs typical$ 6-8M
Reduced external consultants

SLIDE 10: THE CROSS-POLLINATION OPPORTUNITY

Bridging Healthcare and Life Sciences

The Current State: One-Way, 17-Year Lag

Research → Publication → Guidelines → Practice
  (Lab)      (2-3 yrs)     (5-7 yrs)   (7-10 yrs)
  
= 17 YEAR EVIDENCE-TO-PRACTICE GAP

With Kernelius: Bi-Directional, Real-Time Learning

Research ↔ Practice
(Life Sciences ↔ Healthcare)

Continuous, collaborative improvement

Example: Cancer Immunotherapy Protocol Evolution

Phase 1: Research Creates Protocol

Pharma completes Phase 3 trial
↓
Creates treatment protocol in Kernelius
↓
Shares with community (Layer 3)

Phase 2: Healthcare Adapts

Academic medical center forks protocol
↓
Adapts for their population
↓
Background agents validate adaptations
↓
Protocol deployed in clinical practice

Phase 3: Real-World Data Flows Back

6 months later: 42 patients treated
↓
Outcomes captured: ORR 44% (vs 38% in trial)
↓
Discovery: Never-smoker subgroup shows 62% ORR!
↓
Real-world data shared back to research

Phase 4: Research Incorporates Learning

Pharma team sees signal
↓
Designs Phase 3b trial for never-smokers
↓
New trial launches in 6 months (not 3-5 years)

Phase 5: Continuous Improvement

As more centers share outcomes:
  • Best practices emerge
  • Patient selection refined
  • Resistance mechanisms identified
  
Evidence-to-practice gap: 17 years → 6-12 months

Cross-Pollination Benefits

For Healthcare:

✅ Immediate access to cutting-edge protocols
✅ Adapt for local populations
✅ Contribute to medical knowledge
✅ Faster innovations for patients

For Life Sciences:

✅ Real-world validation
✅ Hypothesis generation from practice
✅ Subgroup signals for label expansion
✅ Better trial designs

Both Benefit:

✅ Network effects compound value
✅ Innovation spreads at internet speed
✅ Best practices emerge organically

Supporting Data

From Deloitte 2026 Outlooks:

Only 38% of US healthcare spending on prevention/wellness
80% of healthcare executives prioritize cross-industry collaboration
63% of life sciences executives expect partnerships to become higher priority

This Is What "Learning Health Systems" Was Always Meant to Be

SLIDE 11: MARKET OPPORTUNITY

Healthcare Market

Organizations:

6,000+ hospitals in US
10,000+ specialty clinics
900+ health plans
Focus: Academic medical centers, large systems, national payers

Market Dynamics:

67% of leaders expect to outperform competitors in 2026
63% expect partnerships to become higher priority
80% prioritize cross-industry collaboration
70% plan technology alliances in 2026

Life Sciences Market

Organizations:

Top 20 pharma: $1T+ combined R&D spend annually
5,000+ biotech companies
1,000+ academic research institutions

Market Dynamics:

45% biopharma + 51% medtech see M&A as top priority
2025 deal value ($91.9B) exceeds 2024 total
44% cite pricing/access as strategic concern
37% face competitive pressure from generics/biosimilars

Key Market Drivers

Regulatory Forcing Functions:

CMS Prior Authorization: 14 days → 7 days (Jan 2026)
EU AI Act compliance requirements
EHDS regulations
FDA AI/ML guidance

Cost Pressures:

Life Sciences: $2B+ average drug development cost
- 41% prioritize improving R&D productivity
Healthcare: 62% of spending wasted on reactive care
- Systematic efficiency gains needed

AI Scaling Challenge (The Core Gap):

Belief in AI value: 78-80% of executives
Actually scaled AI: Only 22-33%
Achieving significant ROI: Only 9%
Gap = Infrastructure opportunity

Initial Beachhead Market

Organizations Already Using AI Agents (100-500 globally):

Healthcare:

Mayo Clinic, Johns Hopkins, UCSF, Cleveland Clinic
Providence, Intermountain, Kaiser Permanente
UnitedHealthcare, Anthem, Humana

Life Sciences:

Takeda, Novartis, Roche, AstraZeneca, Lilly
Recursion, Insitro, BenevolentAI
Intuitive Surgical, Medtronic, Abbott

These organizations:

Already deploying AI agents
Frustrated by ad-hoc implementations
Have budget allocated ($500K-2M)
Need governance and collaboration
Express greater optimism when they have better infrastructure

Market Sizing

Bottom-Up (Conservative):

Early Adopters (Years 1-3):
  200 orgs × $300K avg = $60M

Growth Segment (Years 3-5):
  1,000 orgs × $500K avg = $500M

Mature Market (Year 5+):
  5,000+ orgs × $400K avg = $2B+

Top-Down (Market Share):

Healthcare IT: $200B annually
  Addressable: $20B (clinical decision support, quality, analytics)
  Kernelius TAM: $200M-400M (1-2% capture)

Pharma R&D IT: $50B annually
  Addressable: $10B (informatics, clinical ops, regulatory)
  Kernelius TAM: $100M-200M (1-2% capture)

Combined 5-Year TAM: $300M-600M

5-Year Revenue Projection

Year	Customers	Avg Deal	ARR	Growth
2026	5-10	$200K	$1.5M	-
2027	25-50	$300K	$11M	633%
2028	75-100	$400K	$35M	218%
2029	150-200	$450K	$79M	126%
2030	250-350	$500K	$150M	90%

Comparable Platform Benchmarks:

Snowflake: $592M →$ 2.1B (3.5x in 3 years)
Databricks: $1B →$ 2.4B (2.4x in 3 years)
Kernelius: Similar trajectory potential with vertical focus

Metric	2026	2027	2028	2029	2030
ARR	$1.5M	$11M	$35M	$79M	$150M
Gross Margin	70%	75%	78%	80%	80%
Operating Margin	-30%	-15%	0%	+13%	+25%

SLIDE 13: GO-TO-MARKET STRATEGY

Phase 1: Land with Early Adopters (Months 1-12)

Target:

Organizations already using AI agents
Anthropic's healthcare/life sciences partners
Academic medical centers with AI programs
Top 20 pharma with AI initiatives

Tactics:

Direct sales: 2 AEs + 1 SE
Design partner program (3-5 orgs)
Leverage Anthropic partnership
Thought leadership (conferences, publishing)

Metrics:

5-10 paying customers
$1M-3M ARR
Avg deal: $150K-200K (Team tier)

Phase 2: Expand Within Customers (Months 12-24)

Strategy:

Land-and-expand: Department → Org-wide
Prove ROI (70% time savings, 30% quality improvement)
Customer success-driven growth
Case study development

Channels:

2 CSMs for high-value accounts
Add 2 more AEs (total 4)
Industry conferences (HIMSS, BIO, ASCO, CHI)
Content marketing and webinars

Metrics:

25-50 customers
$10M-15M ARR
Avg deal: $300K-400K (Organization tier)
NRR: 130-150%

Phase 3: Network Effects (Months 24+)

Strategy:

Enable Layer 3 (community sharing)
Launch skills marketplace
Build partner ecosystem
Geographic expansion (EU, Asia)

Community:

Annual KernelCon conference
Protocol sharing network
Skills marketplace revenue share
Open source contributions

Metrics:

100+ customers by Year 3
$50M+ ARR by Year 5
Marketplace GMV: $10M
Community protocols: 5,000+

Sales Team Scaling

Year 1: 2 AEs, 1 SE, founder selling
Year 2: 4 AEs, 2 SEs, Head of Sales
Year 3: 8 AEs, 4 SEs, 2 SDRs
Year 4+: 15-20 AEs, regional structure

SLIDE 14: COMPETITION & DIFFERENTIATION

Current Landscape

No Direct Competitors

No one is building universal infrastructure for AI agents in healthcare/life sciences

Adjacent Players

1. Clinical Decision Support (UpToDate, Epic)

Static content, no AI orchestration
Single-org, no collaboration
No version control

2. Research Tools (Benchling, LIMS)

Lab-focused only
Single-org, no sharing
No AI agent infrastructure

3. General AI (ChatGPT, Gemini)

Not healthcare-specific
No domain infrastructure
No compliance/governance

4. Point Solutions (Nuance, PathAI)

Single workflow only
Can't extend
No cross-org benefits

5. Enterprise Platforms (Salesforce, ServiceNow)

Not AI-native
Generic, not purpose-built
No Git methodology

✅ Only Platform Combining All Five

1. AI Agent Orchestration

User-facing + background agents
Multi-agent collaboration

2. Git Methodology

Version control for protocols
Branching for experimentation
Merge validation (CI/CD)

3. Domain Expertise

Healthcare + life sciences skills
Specialized MCP integrations
Environment templates

4. Cross-Org Collaboration

Protocol sharing (Layer 3)
Skills marketplace
Community-driven improvement

5. Complete Ecosystem

Not just agents, full infrastructure
Extensible and composable

Competitive Moats

1. Network Effects

Each customer increases value for all
Protocol library compounds
First-mover advantage

2. Data Flywheel

More runs → Better validation → More trust
Proprietary data asset

3. Ecosystem Lock-In

Skills marketplace
MCP integrations become standard
High switching costs

4. Domain Expertise

Healthcare + Life Sciences + AI is rare
Regulatory compliance built-in

5. Category Creation

Defining Git for healthcare
Establishing standards

SLIDE 15: TEAM

Yam Catzenelson - Founder & CEO

Background:

Healthcare tech founder and CEO with track record
Deep clinical workflow understanding
Technical sophistication: AI/ML expertise
Systematic thinker: Research problem selection framework
Mission-driven: Closing evidence-to-practice gap

Why Yam:

Domain expertise (healthcare + life sciences)
Product vision (conceived Health Forge architecture)
Technical credibility
Founder-market fit

Key Hires Needed (18 Months)

CTO - AI/ML Infrastructure Expert

10+ years distributed systems
Agent orchestration at scale
Healthcare compliance (HIPAA, 21 CFR Part 11)
Target: Anthropic, OpenAI, Scale AI background
Comp: $250K-350K + 2-4% equity

VP Engineering - Healthcare Systems Integration

8+ years healthcare IT
Deep EHR knowledge (Epic, Cerner)
FHIR, HL7, clinical data standards
Target: Epic, Cerner, Redox background
Comp: $200K-275K + 1-2% equity

Head of Product - Clinical Informatics

5+ years healthcare/life sciences product
Clinical background preferred
User research expertise
Target: Clinical informaticist or healthcare PM
Comp: $180K-250K + 1-2% equity

VP Sales - Enterprise Healthcare/Life Sciences

10+ years enterprise SaaS sales
Healthcare/life sciences relationships
Infrastructure platform experience
Target: Healthcare SaaS or enterprise AI
Comp: $175K-225K base + variable (OTE$ 350K-500K) + 1-2% equity

Advisory Board (In Formation)

Clinical Thought Leaders:

CMIOs from academic medical centers
VP Research from top pharma
Value: Go-to-market, validation, introductions

AI/ML Experts:

Research scientists from Anthropic/leading labs
Academic AI professors
Value: Technical guidance, thought leadership

Healthcare IT Executives:

Former CIOs from large health systems
Heads of Data Science from pharma
Value: Buyer insights, customer development

Compensation: 0.1-0.25% advisor shares

1. Founder-Market Fit: Healthcare tech + systematic thinking
2. Balanced Expertise: Clinical + Technical + Commercial
3. Recruiting Advantage: Mission attracts top talent
4. Advisory Credibility: Industry leaders validate product
5. Retention: Equity upside + category creation + impact

SLIDE 16: WHY WE'LL WIN

1. Market Timing Is Perfect

AI Adoption Accelerating:

78-80% believe AI will deliver value
Only 22-33% have scaled successfully
Only 9% achieve significant ROI
70+ point gap = our opportunity

Industry Quote:

"We're entering a period of purposeful transformation, where discipline and innovation must coexist as the industry matures beyond hype toward measurable productivity." — Gabriele Ricci, CTO, Takeda

We're solving the #1 barrier: Infrastructure

2. Validated by Industry Leaders

Deloitte confirms our thesis:

"Real transformation will involve innovative thinking, agile operating models, and robust external partnerships."

"Successful AI deployment is as much about people and processes as technology... systematically remapping workflows."

3. Addressing Top Strategic Priorities

Our platform directly addresses:

Regulatory/compliance (80% cite as priority) → Built-in
Digital transformation (48% identify) → Enables systematic AI
Collaboration (63-80% prioritize) → Layer 3 facilitates
Cost management ($2B drugs, 62% waste) → Improves efficiency

4. Unique Position

Healthcare + Life Sciences + AI Infrastructure

No one else at this intersection
Bridges research and practice
Solves 17-year evidence gap

Quote:

"Our purpose lies in uniting discovery with patient care." — Simone Thomsen, President, Eli Lilly Japan

This is exactly what we do

5. Network Effects = Unassailable Moat

Every customer increases value:

More protocols shared
Skills marketplace grows
Outcomes data improves predictions
High switching costs

Data:

Organizations further along AI maturity express greater optimism and performance.

Having infrastructure = competitive advantage

6. Platform Strategy

Infrastructure - everyone builds on us:

Horizontal across workflows
Ecosystem of developers
Winner-take-most dynamics

Comparables:

Snowflake: 3.5x revenue in 3 years
Databricks: 2.4x revenue in 3 years

7. Regulatory Forcing Functions

Organizations must move NOW:

CMS: 7-day prior auth (Jan 2026)
EU AI Act compliance
EHDS regulations
Can't build fast enough themselves

We're the fast path to compliance

8. Mission-Driven Impact

We're transforming healthcare:

17 years → 6-12 months (evidence-to-practice)
$2B drug costs → systematic efficiency
62% waste → preventive care
Better patient outcomes

Attracts best talent

9. Category Creation

We're defining the category:

"Universal Infrastructure for AI Agents in Healthcare/Life Sciences"
Set standards before competitors realize
Network effects before competition

This is GitHub's moment for healthcare

10. Proven Pattern

Not inventing new concepts:

Git/GitHub: Proven 15+ years, 100M+ developers
Background agents: Validated by Ramp
Platform economics: Snowflake, Databricks

Lower execution risk - proven patterns, new domain

Why We'll Win - Summary

✅ Market timing (AI gap massive)
✅ Validated thesis (industry confirms)
✅ Strategic alignment (top priorities)
✅ Unique position (bridging domains)
✅ Network effects (unassailable moat)
✅ Platform strategy (winner-take-most)
✅ Regulatory urgency (must move now)
✅ Mission-driven (best talent)
✅ Category creation (define standards)
✅ Proven patterns (lower risk)

The organizations with the right infrastructure will win.

We're building that infrastructure. Now.

Layer 1: Healthcare Cowork MVP
Layer 2: Organizational infrastructure (Git, CI/CD)
Skills library (25 core skills)
Key MCP integrations (EHR, PubMed, trials, regulatory)
Infrastructure: HIPAA, HITRUST, SOC 2, scalability

Go-to-Market (30% - $2.4M)

Design partner program (3-5 orgs)
Sales: 2 AEs + 1 SE
Customer success: 2 CSMs
Marketing: content, conferences, thought leadership
Partnerships: Anthropic ecosystem, EHR vendors

Team (15% - $1.2M)

CTO, VP Engineering, Head of Product
3-4 Senior Engineers
Product Designer

Operations (5% - $400K)

Legal (compliance, IP)
Finance & accounting
Cloud infrastructure
Tools & systems

18-Month Milestones

Product:

✅ Layer 1 + Layer 2 operational
✅ 25 skills across healthcare and life sciences
✅ 10+ MCP integrations
✅ HIPAA, HITRUST, SOC 2 compliance

Commercial:

✅ 5-10 paying customers
✅ $1M-3M ARR
✅ 3-5 case studies with quantified ROI
✅ Design partnerships established
✅ >120% NRR

Market:

✅ Thought leadership (published, speaking)
✅ Product-market fit demonstrated
✅ Strong retention (>90%) and satisfaction

Team:

✅ Core executives in place
✅ 12-15 person team
✅ Advisory board established

Position for Series A ($20M+)

Metrics:

$10M ARR run-rate
25-50 customers
130-150% NRR
Path to $50M ARR by Year 3

Proof Points:

Layer 1 → Layer 2 progression validated
Network effects beginning
Skills ecosystem developing
International expansion ready

Investment Highlights

1. Massive Market

$300M-600M TAM (5-year)
Healthcare IT + Pharma R&D

2. Perfect Timing

AI infrastructure gap urgent
Regulatory forcing functions

3. Proven Team

Founder-market fit
Hiring top talent

4. Capital Efficient

Land-and-expand motion
120% NRR
80%+ gross margins
Path to profitability Month 36-42

5. Defensible Position

Network effects
Category creation
Platform lock-in

6. Measurable Impact

17-year gap → months
Transform $2T+ industry
Better patient outcomes

✅ Make knowledge collaborative

✅ Give organizations control over AI agents

✅ Create transformative network effects

The Data Is Clear

Belief in AI Value: 78-80% of executives

Reality of AI Scaling: Only 9-22% achieve ROI

The Gap = Infrastructure

This Is a $300M-600M Opportunity

The Organizations That Will Win Are Already Using AI Agents

They Just Don't Have the Infrastructure to Do It Right.

That's What We're Building.

The Time Is NOW

Regulatory Deadlines:

CMS: 7 days (Jan 2026)
EU AI Act: 2026
FDA guidance: evolving

Competitive Pressure:

Infrastructure = advantage
First-movers establish network effects

Market Is Ready:

AI agents deployed now
Budget allocated
Infrastructure gap painful

Let's transform healthcare and life sciences.

Let's build the infrastructure that makes AI agents work at scale.

Thank you.

Questions?

APPENDIX

A1: Detailed Use Case - Life Sciences

Adverse Event Analysis Across Clinical Trials

Scenario: Pharma analyzing safety signals across 3 Phase 3 trials (1,500 patients)

Traditional Timeline: 8-12 weeks
With Kernelius: 24-48 hours

Key Benefits:

Data integration from 3 CROs automated
MedDRA coding standardized automatically
Statistical analysis run in parallel branches
FDA-ready report auto-generated
Full audit trail for regulatory inspection
95% time savings

Impact:

Faster regulatory submissions
Better safety signal detection
Higher quality dossiers
Reduced external vendor costs
$2-5M savings per submission

A2: Market Validation Quotes

From Deloitte 2026 Outlooks

"Volatility fuels innovation. We're entering a period where discipline and innovation must coexist as the industry matures beyond hype toward measurable productivity from AI." — Gabriele Ricci, Chief Data and Technology Officer, Takeda

"AI is already out of the box, and the speed at which innovation is moving will only accelerate." — William Phillips, Chief Commercial Officer, Terumo Neuro

"Companies should focus on what they do best: discovering novel biology while remaining flexible in all other areas. As an industry, we need to deliver innovation." — Karl Gubitz, CFO, argenx

"Agility and resilience matter, but our true purpose lies in uniting discovery with patient care." — Simone Thomsen, President & GM, Eli Lilly Japan

A3: Competitive Matrix

Capability	Kernelius	CDSS	Research Tools	General AI	Point Solutions	Enterprise Platforms
AI Agent Orchestration	✅ Full	❌ None	❌ None	🟡 Basic	🟡 Single use	❌ None
Version Control (Git)	✅ Native	❌ None	🟡 Limited	❌ None	❌ None	❌ None
Background Agents	✅ Yes	❌ No	❌ No	❌ No	❌ No	❌ No
Healthcare Skills	✅ Comprehensive	🟡 Basic	❌ None	❌ Generic	✅ Deep/narrow	❌ Generic
Life Sciences Skills	✅ Comprehensive	❌ None	✅ Lab-only	❌ Generic	✅ Deep/narrow	❌ Generic
Cross-Org Collaboration	✅ Built-in	❌ None	❌ None	❌ None	❌ None	❌ None
CI/CD Validation	✅ Native	❌ None	❌ None	❌ None	🟡 Internal	🟡 Process
Network Effects	✅ Strong	❌ None	❌ None	❌ None	❌ None	❌ None

A4: Financial Model Summary

5-Year Projection

Year	Customers	ARR	Gross Margin	Op Margin	Rule of 40
2026	8	$1.5M	70%	-30%	N/A
2027	38	$11M	75%	-15%	618%
2028	88	$35M	78%	0%	218%
2029	175	$79M	80%	+13%	139%
2030	300	$150M	80%	+25%	115%

Key Assumptions:

NRR: 120-150% (land-and-expand)
LTV/CAC: 15-20x
Payback: 12-18 months
Gross margin: 75-80%
Path to profitability: Month 36-42

A5: References

Primary Sources:

Deloitte 2026 Life Sciences Outlook
- 280 C-suite executives surveyed
- August-September 2025
Deloitte 2026 US Healthcare Outlook
- 120 C-suite executives surveyed
- August-September 2025

Key Data Points:

78% life sciences executives expect AI to drive change
80%+ healthcare executives see AI delivering value
Only 22-33% have scaled AI successfully
Only 9% report significant ROI
17-year evidence-to-practice gap
$2B+ average drug development cost
38% healthcare spending on prevention

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