Integrated Quantum Technologies Inc. · CSE:VEIL
Integrated Quantum Technologies — Investor Presentation (June 2026)
20 slides from Integrated Quantum Technologies's investor presentation, June 2026
Slide 1: Quantum-Resilient Infrastructure for AI & Machine Learning
Quantum-Resilient Infrastructure for AI & Machine Learning Investor Presentation (Q2-2026) Private & Confidential | All Rights Reserved 1
Slide 2: Disclaimer
DISCLAIMER General The information provided in this presentation pertaining to Integrate Cyber, Inc ("Integrated Cyber" from sources (other than the Company) not bound by any obligation to keep such information industry; competition; technology failures; failure of counterparties to perform their co
Slide 3: A Research and Innovation Company for Next-Generation AI Infrastructure
Our story A Research and Innovation Company for Next-Generation AI Infrastructure Grounded in research and innovation, Integrated Quantum Technologies is shaping the next frontier of intelligent AI infrastructure. We develop quantum resilient technologies that allow AI to operate securely, adapt dyn
Slide 4: AIQu VEIL: The Quantum-Resilient Engine for High-Performance AI
INTRODUCING AIQU VEIL AIQu VEIL : The Quantum-Resilient Engine for High-Performance AI Emerging from Integrated Quantum Technologies’ Innovation Center, AIQu VEIL enables organizations to harness AI while keeping sensitive data fully protected. By ensuring data is never exposed in raw form, AIQu VEI
Slide 5: 9 out of 10 executives see AI as the future of revenue growth
9 out of 10 executives see AI as the future of revenue growth, yet most are held back by concerns over data security and compliance risks. Source: Gartner: Gartner. (2024). Gartner 2024 CEO and Senior Business Executive Survey. Author. IBM: IBM. (2024). IBM Global AI Adoption Index 2024. Author. 5
Slide 6: Why Today's AI Systems Fall Short on Security, Compliance, and Scalability?
The Problem Why Today’s AI Systems Fall Short on Security, Compliance, and Scalability? Regulatory hurdles: rivacy constraints: P Security vulnerabilities: Local deployment rules prevent economies GDPR, HIPAA, APRA, and other laws require data to Centralized, monolithic data storage creates of scale
Slide 7: Why Enterprises Need AIQu VEIL Now?
Our SOLUTION Why Enterprises Need AIQu VEIL Now? Our solution closes a critical market gap that currently limits AI adoption while equipping companies for a quantum-ready future. Enterprises can’t afford to wait. AIQu VEIL eliminates these barriers, enabling secure, compliant, and scalable AI deploy
Slide 8: How AIQu VEIL Secures AI Ecosystems?
PRIVACY Protection How AIQu VEIL Secures AI Ecosystems? If attacked or intercepted, ML data remains impossible to reconstruct. Attackers gain nothing but unusable fragments with zero context or value. Even a successful breach becomes a dead end by design, turning data security from risk mitigation i
Slide 9: Quantum Resilient AI Security, Optimized for Performance
UNMATCHED PERFORMANCE Quantum Resilient AI Security, Optimized for Performance AIQu VEIL sets a new standard for secure ML, combining unmatched protection, speed, and accuracy so enterprises can deploy AI with confidence at scale. 92.45% 99.25% +6.80% Raw Data Test Accuracy Protected Data Test Accur
Slide 10: AIQu VEIL: Secure, Efficient, and Quantum-Ready
WHY AIQU VEIL MATTERS? AIQu VEIL: Secure, Efficient, and Quantum-Ready Faster & Smarter ML Global Deployment: End-to-End Security: Enhances model speed and Centralized ML deployment across global Protects sensitive data in regulated accuracy while compressing data to operations reduces duplication,
Slide 11: Organizations That Need Quantum-Ready AI Protection
Our Target Market Organizations That Need Quantum-Ready AIProtection Organizations across industries face regulatory, security, and skills challenges that hinder the safe and scalable adoption of AI. Technical assessments and performance expectations reflect management’s current beliefs based on int
Slide 12: No Comparable Solution Exists
No Comparable Solution Exists Technical assessments and performance expectations reflect management’s current beliefs based on internal research, validation and testing conducted by the Company. Results may vary across customer environments, datasets, configurations and deployment conditions. 12
Slide 13: We Don't Just Protect AI, We Enhance It.
Intellectual Property & Innovation We Don’t Just Protect AI, We Enhance It. Our patent pending AIQu VEIL infrastructure redefines how intelligence is protected, setting new standards for performance, trust, and scalability. Each breakthrough reinforces our position at the intersection of AI, privacy
Slide 14: The Results (AIQu VEIL): Secure, Fast, Accurate
PERFORMANCE The Results (AIQu VEIL): Secure, Fast, Accurate Supporting technical references and source materials are in the appendix. Technical assessments and performance expectations reflect management’s current beliefs based on internal research, validation and testing conducted by the Company. R
Slide 15: Managed Cybersecurity & SecureGuard360
Unified cybersecurity Managed Cybersecurity & SecureGuard360™ Integrated Quantum Technologies provides managed cybersecurity services to SMB and mid-market organizations across North America while developing proprietary cybersecurity software solutions. Managed Security Services (MSSP) Recurring cyb
Slide 16: Why Invest in ICS: Research Driven AI Security
Why Invest in ICS: Research Driven AI Security ICS is focused on research and innovation, developing quantum-resilient AI security, architecture, and infrastructure solutions 16
Slide 17: Our Team
OUR tEAm Behind Integrated Quantum Technologies is a Multidisciplinary leadership team We bring together leading researchers, engineers, and strategists united by a single goal: to secure the world’s most powerful technologies. 17
Slide 18: Board and Advisors
OUR tEAm Board and Advisors We bring together leading researchers, engineers, and strategists united by a single goal: to secure the world’s most powerful technologies. 18
Slide 19: Thank You
Thank You Email: [email protected] Private & Confidential | All Rights Reserved
Slide 20: References & Supporting Research
Appendix References & Supporting Research [1] Mohammadi, M., Vejdanihemmat, M., Lotfinia, M., Rusu, M., Truhn, D., Maier, A., & Tayebi Arasteh, S. (2026). Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications. npj Digital Medicine, Nature Portfolio. DOI: 10.
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