Iron Horse Acquisitions Ii Corp. · NASDAQ:IRHO
Iron Horse Acquisitions Ii — Iron Horse Acquisition II — SPAC Merger (June 2026)
40 slides from Iron Horse Acquisitions Ii's SPAC merger presentation, Jun 2, 2026
Slide 1: ELECTRA: AI Brain for Batteries
cLECTRA Al Brain for Batteries” electrabrain.ai &)
Slide 2: Disclaimer
Disclaimer 2 About this Presentation This investor presentation (this "Presentation") is provided for informational purposes only and has been prepared to assist interested parties in making their own evaluation with respect to a potential business combination (the "Business Combination") between El
Slide 3: Disclaimer (cont'd)
Disclaimer (cont'd) 3 Financial Information The financial information and data contained in this Presentation is unaudited and does not conform to Regulation S-X. Such information and data may not be included in, may be adjusted in or may be presented differently in the registration statement to be
Slide 4: ELECTRA AI's Mission
ELECTRA AI's Mission To make every battery safer, last longer, and deliver more usable energy at scale. ELECTRA AI applies AI-driven battery intelligence to predict faults, extend lifespan, and optimize performance across all battery-powered systems. AI-Intelligence for Every Battery System 4 Electr
Slide 5: Today's Speakers
Today's Speakers Former principal investigator DOE, DOD, NASA; Former Director of R&D for Novel Energy Storage Systems. Former SPAC CEO; Founder of Bengochea Capital; background in Sony Global Business Development and corporate law. Extensive experience in C-Suite, general management, sales/BD, prod
Slide 6: ELECTRA AI at a Glance
ELECTRA AI at a Glance Tech Transfer Startup NVIDIA Inception Program Startup 70%+ Expected Contribution Margin 5.3 TWh Pipeline Issued and Pending 1 GWh Batteries Controlled 20 Patents Technical Partners Additional Key Investors Large and Strategic Investors Large Customers 6
Slide 7: Investment Highlights
Investment Highlights 7 Origin Story with Substantial IP Moats Proprietary battery intelligence platform rooted in mission-critical, space-derived research, supported by 20 issued & pending patents covering core algorithms and creating durable competitive barriers Scalable, Asset-Light Platform Acro
Slide 8: TAM/SAM
_ TAM/SAM
Slide 9: Expanding Global Battery End Markets (TAM)
Expanding Global Battery End Markets (TAM) Electrification Across Mobility, Energy, AI Infrastructure, & Autonomous Systems is Accelerating Battery Demand 9 $128 Bn 2029E | CAGR 16% Robotics & Autonomous Systems $33 Bn 2029E | CAGR 11% Drones & UAVs $1,368 Bn 2029E | CAGR 5% Mobility $914 Bn 2029E |
Slide 10: Intelligent Battery Management is Becoming Mission-Critical (SAM)
Intelligent Battery Management is Becoming Mission-Critical (SAM) Battery Growth Across Electrification Markets is Expanding Demand for Intelligent BMS 10 $12.5B $27B+ 2023 2029 $0 $10 $20 $30 Global Intelligent Battery Management Market Target Market Mission-Critical $19.1B • 71% Non-Target Market
Slide 11: AI is Transforming Battery Management from Monitoring to Optimization
AI is Transforming Battery Management from Monitoring to Optimization AI-native Battery Intelligence is Becoming a Core Software Layer Across Electrification Markets AI-native battery intelligence is becoming a core infrastructure layer across electrification markets 11 Economic Impact Legacy BMS AI
Slide 12: Business Overview
| -. ° . —_. ‘ e “ - BUSINESS). a ~ OVERVIEW
Slide 13: The Problem: Systemic Challenges Across Battery Infrastructure
Catastrophic Failures Reduced Asset Lifespan Asset Value Erosion 30% Cost Increase from Failures From unplanned failures & reactive maintenance across battery systems 3-5 Years of Lost Asset Life Due to inefficient usage, unmanaged degradation & lack of adaptive control 15% ROI Lost Due to limited v
Slide 14: Our Solution: AI-Driven Battery Intelligence Platform
Our Solution: AI-Driven Battery Intelligence Platform Improves Safety, Extends Lifespan, and Maximizes Battery Value Across All Deployments Impact Reduce Downtime and Predict Failures Extend Battery Life Enhance Safety and Reliability Simplify Compliance (Battery Passport) Maximize Revenue & Battery
Slide 15: ELECTRA AI's Products: EVE-Ai Software & Embedded Solutions
ELECTRA AI's Products: EVE-Ai Software & Embedded Solutions AI-driven Battery Intelligence Across Scalable and Mission-critical Systems 1.5 Billion+ Battery Data Points Processed 4 Million+ Lines of Code 10,000+ Model Parameters Tracked 20 Patents Issued & Pending Software and Cloud Solution Embedde
Slide 16: How ELECTRA AI Unleashes Battery Performance & Economics
How ELECTRA AI Unleashes Battery Performance & Economics AI-driven Intelligence Powering Smarter, Safer, and More Efficient Battery Systems at Scale Advanced State of Health (SoH), State of Charge (SoC) & Remaining Useful Life (RUL) modeling to predict degradation and extend battery lifespan Real-ti
Slide 17: ELECTRA AI in Action: Battery Intelligence Across High-Value Applications
17 ELECTRA AI in Action: Battery Intelligence Across High-Value Applications One Platform Powering Performance, Safety, and ROI Across High-value Battery Systems Data Centers (AI Infrastructure) BESS (Grid-Scale Energy Storage) High-density GPU racks create volatile power profiles Detects early stre
Slide 18: Case Studies
Case Studies Real-World Results Across Mobility, Energy, and Infrastructure EV-Fleet Managers Optimization & Cost Reduction BESS Operators Optimization & Revenue Uplift Automotive OEMs Precision & Performance Validation Embedded EVE-Ai into Tesla Cybertruck to validate real-world performance, effici
Slide 19: Selected Customer Engagements
Selected Customer Engagements Engaged with Leading OEMs, Energy Operators, and Fleet Providers Across Pilot and Commercial Programs Strategic Customer Engagements Underway Mobility • Energy • Infrastructure • Autonomous & Mission-Critical Systems 19
Slide 20: Features and IP Moat
Features and IP Moat Patented EVE-Ai Platform Spanning Cloud Analytics and Embedded Controls 20 20 Patents Issued & Pending | Global Filings Full-stack IP across cloud and embedded layers - driving data scale, deep integration, and long-term defensibility EVE-Ai Cloud - Fleet Analytics EVE-Ai Embedd
Slide 21: Owning the Battery Intelligence Stack
Owning the Battery Intelligence Stack One of the Few Platforms Delivering Full-stack Battery Intelligence Battery Analytics Landscape n ≈ 17-20 System Visibility & Monitoring n ≈ 16 Optimization n ≈ 10 Unified AI + Control Platform n = 2 Data visibility + Dashboards Optimization & Controls Hybrid AI
Slide 22: Why ELECTRA AI?
Why ELECTRA AI? The Intelligent Layer for Batteries - Driving Performance, Lifespan, and Value Across Industries Business Impact Technology Advantage What we deliver How we win 22 Unlock peak battery performance in real-world conditions Hardware-agnostic across battery platforms Extend battery life
Slide 23: Financial Overview
FINANCIAL OVERVIEW
Slide 24: Use of Proceeds
Use of Proceeds Core Platform Built — Capital Accelerates Expansion Across Large, High-growth Verticals Private Capital SPAC Proceeds 24 Expand Within Existing Verticals • BESS (grid-scale energy storage) • Data centers (AI infrastructure) • EV fleets & mobility Penetrate New Verticals • Robotics &
Slide 25: Illustrative Customer Unit Economics
Illustrative Customer Unit Economics Blended Software (Cloud + Embedded) Model Drives Revenue Expansion Per Customer Current Contract (Baseline) per average customer deployment Contract Structure (Cloud vs Embedded) per average customer deployment Scaled Contract (Target State) per average customer
Slide 26: Pipeline Overview
Pipeline Overview 26 Applying Blended Software (Cloud + Embedded) Economics Across a 5.3 TWh Pipeline Stage 3: Contracted & Deploying Stage 1: Early Pipeline (Discovery / Evaluation) 5 TWh High-volume opportunity pipeline Stage 2: Active Engagement (Commercial Structuring) 270 GWh In active conversi
Slide 27: ELECTRA AI's Financial Strengths
27 ELECTRA AI's Financial Strengths Cloud + Embedded contracts drive predictable, multi- year recurring software revenue Blended deployments increase contract value ~11.6x vs cloud-only Expected ~70– 75%+ contribution margins driven by software (cloud + embedded) mix No manufacturing capex – asset l
Slide 28: M&A Strategy
M&A STRATEGY
Slide 29: Accelerating Platform Leadership Through Selective M&A
Accelerating Platform Leadership Through Selective M&A 29 Core Platform Deployed Today – Selective M&A Accelerates Scale, Integration, and Defensibility Expand Control Across the Stack (Where Strategic) Enhance integration across testing, intelligence, and embedded control layers Strengthen Long-Ter
Slide 30: Expanding Full-Stack Control: Capability Expansion Framework
Expanding Full-Stack Control: Capability Expansion Framework 30 Targeted M&A Enhances an Already Integrated Battery Intelligence Platform to Accelerate Scale Deepens platform integration, strengthens long-term defensibility, expands high-margin software & embedded revenue streams Data Creation Testi
Slide 31: Disciplined Capital Deployment Roadmap (5-Year Horizon)
Disciplined Capital Deployment Roadmap (5-Year Horizon) 31 Phased M&A Aligned with Platform Scaling, Data Expansion, and Monetization M&A Pipeline Built in Advance to Enable Rapid, Disciplined Execution Phase 1 Phase 2 Scale Data, Distribution Expand Monetization Platform Adoption Revenue Intelligen
Slide 32: ELECTRA: AI Brain for Batteries
cLECTRA Al Brain for Batteries” electrabrain.ai &)
Slide 33: Appendix
e __ APPENDIX
Slide 34: Our Investors
Our Investors LV united ventures "2 BlackBerry prithvi Ventures LAUNCHPAD VENTURE GROUP TA @X@ cus degli ev@ !NVESTITORI MASS CLEAN ENERGY CENTER Sa Tz BOSTON ‘e) PLANETARY VC 34
Slide 35: EVE-Ai: Predicting Thermal Runaway Before It Happens
EVE-Ai: Predicting Thermal Runaway Before It Happens Understanding and Predicting Thermal Runaway Mechanical Abuse • Cause: Physical damage (compression & penetration) • What Happens: Internal deformation • Detection: Pressure monitoring Electrical Abuse • Cause: Overcharge or internal short circuit
Slide 36: Types of Battery Faults Detected
Types of Battery Faults Detected Identify and Predict Faults Before they Become Safety-critical Events Fault Type Root Cause Detection Electrical Faults Overcurrent, short circuits, resistance changes Voltage drops and abnormal current behavior Thermal Faults Overheating from rapid cycling or poor c
Slide 37: Go-To-Market Strategy
Go-To-Market Strategy Two Complementary Growth Engines Battery Fleet Analytics (BFA) Scalable & Repeatable Growth Engine Scale, Speed, Adoption Fast Market Entry Driven by Data & Usage Expansion Adaptive Controls Embedded, High Margin Growth Engine Depth, Lock-in, Defensibility Deep Integration Driv
Slide 38: Current Board of Directors
Current Board of Directors Fabrizio Martini CEO & Co-Founder 14 patents, 4 World Records for energy storage system performance, Principal investigator DOE, DOD, NASA; Former Director of R&D for Novel Energy Storage System Marco Morchio Board Member Former Accenture Partner with 30 years in Professio
Slide 39: Current Board of Technical Advisors
Current Board of Technical Advisors David Mebane Assoc. Professor of Mechanical & Aerospace Engineering West Virginia University Matthias Preindl Assoc. Professor of Electrical Engineering Columbia University Simona Onori Assoc. Professor of Energy Science Engineering Stanford University David Burg
Slide 40: ELECTRA: AI Brain for Batteries
cLECTRA Al Brain for Batteries” electrabrain.ai &)