Infrastructure for Trustworthy AI
Virelya delivers infrastructure for trusted LLMs—enabling platform integrity, GenAI compliance, and clinical safety.
Built on the LLM Design & Assurance (LLM-DA) stack, it unifies governance, red teaming, and real-time validation into a single trust layer for high-stakes AI.
📄 Read Our Latest Paper:
“Risks & Benefits of LLMs in Platform Integrity & Healthcare” IEEE Invited Talk • SVCC 2025
Backed by peer-reviewed science, real-world deployments, and developer tools.
📚 50+ Publications 🧠 10+ Patents 📱 1M+ App Users
📄 IEEE Invited Paper on LLM Safety ⚡ GPU-Accelerated Platform
What We Do
🛡️ Platform Integrity
Detect and block AI-generated abuse across app stores and marketplaces. Vet apps, plugins, and content at scale to ensure trust and safety.
• 🎤IEEE Invited Speaker: LLMs & GenAI • 🎨10+ Apps, 1M+ Users, iOS/macOS/Android • 📜10+ Patents, 50+ Papers, 1,750+ Citations • 🏆IEEE 1st Place AI Award • 🛡️Platform Integrity • 🏥 GenAI Diagnostics • 📜 Scalable AI Safety Frameworks
🔒 AI Governance & Compliance
Develop end-to-end governance infrastructure for GenAI deployment using formalized compliance-as-code, audit trail generation, explainability layers, and red-teaming simulation. Ensure global regulatory alignment (GDPR, HIPAA, NIST AI RMF, FDA SaMD) through proactive policy verification, bias auditing, and automated documentation modules integrated into the LLM Design & Assurance (LLM-DA) stack.
🧠 LLM Blueprinting & Simulation Infrastructure
Develop end-to-end governance infrastructure for GenAI deployment using formalized compliance-as-code, audit trail generation, explainability layers, and red-teaming simulation. Ensure global regulatory alignment (GDPR, HIPAA, NIST AI RMF, FDA SaMD) through proactive policy verification, bias auditing, and automated documentation modules integrated into the LLM Design & Assurance (LLM-DA) stack.
Research Highlight
OUR CORE CAPABILITIES
🧬 Clinical AI & Diagnostics
Bridge patient-reported symptoms and medical imaging via LLM-powered multimodal mapping. Integrate contrastive learning for biomarker alignment, Retrieval-Augmented Generation (RAG) for medical evidence grounding, and physician-in-the-loop interfaces. Built on edge-deployable, GPU-accelerated architecture, this framework enables real-time diagnostics, digital twin modeling, and explainable AI for safe, high-throughput clinical decision support.
📦 Marketplace Compliance & AI Plugin Vetting
Automatically validate AI-powered apps, agents, and plugins against marketplace-specific policy requirements (e.g., Google Play, App Store, Hugging Face Spaces). Integrate SDK tracing, behavior monitoring, and zero-shot policy alignment to accelerate review cycles while reducing rejection rates and liability.
🧠📊 Federated Learning & Privacy-Preserving AI
Train and evaluate models across distributed clinical or enterprise environments without centralizing sensitive data. Incorporate differential privacy, secure model updates, and collaborative evaluation pipelines to ensure fairness, generalization, and HIPAA/GDPR compliance in real-world LLM systems.
See our invited IEEE paper on LLM safety →
🛡️ Red Teaming & AI Risk Audits
Simulate adversarial abuse—including prompt injection, jailbreaks, misinformation, and synthetic bias—with automated threat modeling and LLM red teaming frameworks. Integrate jailbreak simulation, policy circumvention tracing, forensic hallucination logging, and OWASP/NIST-aligned risk testing. Embedded within Virelya’s runtime stack for continuous abuse detection and proactive mitigation.
🧾 Explainability & Trust UX Layers
Overlay LLM outputs with transparent rationale, source attribution, token tracebacks, and confidence scores for both platform moderators and end-users. Enable feedback loops, PITL validation, and audit-ready rationales that support developer trust and regulatory accountability in AI decisions.
IEEE 2025 Invited Talk
• 🎤IEEE Invited Speaker: LLMs & GenAI • 🎨10+ Apps, 1M+ Users, iOS/macOS/Android • 📜10+ Patents, 50+ Papers, 1,750+ Citations • 🏆IEEE 1st Place AI Award • 🛡️Platform Integrity • 🏥 GenAI Diagnostics • 📜 Scalable AI Safety Frameworks
IEEE 2025 Invited Talk
Our Founder
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K. Ahi, "A review on machine learning for EEG signal processing in bioengineering," IEEE Rev. Biomed. Eng., vol. 14, pp. 204-218, 2020. Cited by: 466.
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Impact Statement: As a highly cited foundational review, this publication defines the landscape of machine learning for EEG signal processing, providing a cornerstone resource that has significantly influenced research and development in biomedical AI and neurodiagnostics.
IEEE Invited Tech Talk, 6/24/2025: K. Ahi, "Risks & Benefits of LLMs & GenAI for Platform Integrity, Healthcare Diagnostics, Cybersecurity, Privacy & AI Safety: A Comprehensive Survey, Roadmap & Implementation Blueprint," arXiv preprint arXiv:2506.12088, 2025. [Link to full paper]
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K. Ahi, S. Valizadeh, "Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies," presented at the 6th IEEE Silicon Valley Cybersecurity Conf. (SVCC), 2025.
Impact Statement: This pivotal research delivers a comprehensive survey of the critical cybersecurity and privacy risks posed by Large Language Models and Generative AI. It rigorously analyzes emerging threats like AI-generated malware, emphasizes the importance of explainability, and proposes essential defensive strategies, providing crucial insights for safeguarding digital ecosystems in the age of advanced AI.
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K. Ahi, GPU-Accelerated Feature Extraction for Vision AI & LLM Systems Efficiency: Autonomous Image Segmentation & Smart Pattern Recognition for Scalable Real-Time AI Processing with 6.6× Faster Performance…," IEEE ASMC, 2025.
Impact Statement: This research delivers a groundbreaking 6.6x performance acceleration in real-time Vision AI, demonstrating transformative potential for highly scalable and efficient visual processing systems and LLM systems in high-performance computing environments.
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K. Ahi, "Mathematical modeling of THz point spread function and simulation of THz imaging systems," IEEE Trans. Terahertz Sci. Technol., vol. 7, no. 6, pp. 747-754, 2017. Cited by: 161.
Impact Statement: Published in the premier journal for the field, this seminal work provides the fundamental mathematical framework for modeling and simulating Terahertz (THz) imaging systems. This foundational research is critical for optimizing THz system design, enhancing image fidelity, and pushing the boundaries of non-destructive evaluation and security applications.
Dr. Kiarash Ahi holds M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Leibniz University Hannover, Germany, and the University of Connecticut, USA, respectively. He is a 0→1 product leader, pioneering scientist, distinguished researcher, serial founder, and technology innovator with deep expertise in AI, cybersecurity, Large Language Models (LLMs), GPU computing, high-performance computing (HPC) architectures, edge computing, big data analytics, biomedical engineering, digital signal and image processing, natural computation, compressive sensing, optics, and system-level architecture.
Dr. Ahi’s work emphasizes parallel processing, scalable AI models, and intelligent automation, with a strong focus on system design and user experience. His research and industry applications extend across real-time data processing, computer vision, and high-throughput AI platforms.
Since 2019, Dr. Ahi has led the concept-to-market strategy for SEMSuiteTM, Siemens' AI-powered analytics platform, spearheading multinational teams across the globe in orchestrating the full-spectrum scaling of AI-optimized and UX-aware tools including RDF, CDF, CPG, and CMi into scalable, cross-industry solutions optimized for both AI performance and user experience, driving multi-million dollar revenue and receiving multiple performance awards.
He holds more than 10 patents, has published over 50 peer-reviewed papers, and his work has garnered more than 2,500 citations.
Additionally, Dr. Ahi has developed more than 10 creative AI-powered applications for iOS, Android, and macOS platforms, achieving more than one million global users.
He is the recipient of the IEEE AI 1st Place Award and is recognized as a Top Peer-Reviewer by Publons, with over 200 reviews for leading publishers like Nature, IEEE, Springer, and Elsevier.
As a thought leader in AI ethics and governance, he advocates for responsible AI deployment, data privacy, and regulatory compliance, shaping the future of digital trust and platform security.
Dr. Ahi has served as a co-advisor to several PhD students and has been an invited IEEE tech speaker on LLMs, app safety, platform integrity, automated review systems, advanced imaging systems, and cybersecurity.
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K. Ahi, "Risks & Benefits of LLMs & GenAI for Platform Integrity, Healthcare Diagnostics, Cybersecurity, Privacy & AI Safety: A Comprehensive Survey, Roadmap & Implementation Blueprint," arXiv preprint arXiv:2506.12088, 2025.
Impact Statement: This pioneering work establishes a critical roadmap for navigating the complex landscape of Large Language Models and Generative AI, providing essential guidance for robust platform integrity, advanced cybersecurity, and ethical AI development across vital sectors like healthcare.
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K. Ahi, et al. "Quality control and authentication of packaged integrated circuits using enhanced-spatial-resolution terahertz time-domain spectroscopy and imaging," Opt. Lasers Eng., vol. 104, pp. 274-284, 2018. Cited by: 228.
Impact Statement: This highly cited paper provides a breakthrough in non-destructive testing, demonstrating advanced terahertz imaging and spectroscopy techniques crucial for ensuring the quality, authenticity, and security of integrated circuits in a global supply chain.
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K. Ahi, et al. , "Advancing AI-driven computer vision and image segmentation via pattern recognition, GPU-accelerated unsupervised clustering, and edge AI for HPC-scalable big data processing: 85% efficiency gains," in Proc. SPIE Metrology, Inspection, and Process Control XXXIX, vol. 13426, no. 1342649, 2025. DOI: https://doi.org/10.1117/12.3059959.
Impact Statement: This breakthrough research showcases an AI-driven computer vision system that achieves remarkable 85% efficiency gains for HPC-scalable big data processing. By leveraging GPU-accelerated unsupervised clustering, advanced pattern recognition, and edge AI, this work sets a new standard for high-performance image segmentation and real-time visual data analysis.
AI Powered User Friendly Creative Applications & Entrepreneurship
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Dr. Ahi’s entrepreneurial ventures demonstrate the rare fusion of deep scientific knowledge with product-market execution. Rooted in advanced research, including his PhD thesis in Terahertz (THz) image processing, Dr. Ahi has launched 10+ creative AI-powered apps on iOS, Android, and macOS.
These tools—used by 1M+ global users—combine technical sophistication with user-centered design. Monetization includes in-app purchases, direct sales, and ads, ensuring real-world sustainability.
Impact Statement: This extensive portfolio of AI-powered mobile and desktop applications powerfully demonstrates the ability to translate advanced research into highly successful consumer products, reaching over a million global users and generating significant revenue through a diversified monetization strategy.
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K. Ahi, et al. (20+ engineers Dr. Ahi led) , "AI-powered end-to-end product lifecycle: UX-centric human-in-the-loop system boosting reviewer productivity by 82% and accelerating decision-making via real-time anomaly detection and data refinement with GPU-accelerated computer vision, edge computing, and scalable cloud," in Proc. SPIE Metrology, Inspection, and Process Control XXXIX, vol. 13426, no. 1342632, 2025. DOI: https://doi.org/10.1117/12.3052252.
Impact Statement: This landmark publication introduces a pioneering AI-powered end-to-end system that dramatically transforms the product lifecycle, achieving an 82% boost in reviewer productivity and accelerating decision-making. Leveraging GPU-accelerated computer vision, real-time anomaly detection, edge computing, and scalable cloud infrastructure, this work exemplifies high-impact innovation in industrial automation and human-in-the-loop AI systems.
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K. Ahi, "Unsupervised, Scalable Clustering, Pattern Recognition, and Graphics Processing Unit (GPU)-Accelerated Contour Extraction from Challenging High-Variability Images Using Edge…," U.S. Patent App. PCT/US25/027,065, 2025. Cited by: 4.
Impact Statement: This patent pending work describes a system for high-performance, GPU-accelerated image processing, enabling robust contour extraction and pattern recognition in complex and high-variability visual data.
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K. Ahi, "Artificial intelligence based IoT system for managing and optimizing shopping lists, purchased items, shopping visits, delivery schedules and reducing food waste," U.S. Patent 11,461,825, Oct. 4, 2022.
Impact Statement: This patent (U.S. Patent 11,461,825) introduces an AI-powered IoT system that significantly advances smart home and retail. It offers a comprehensive solution for managing and optimizing shopping, from lists and tracking to deliveries, which streamlines daily routines. By focusing on reducing food waste, this innovation promotes sustainability and has the potential to enhance consumer convenience, improve resource utilization, and foster sustainable consumption patterns.
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Our Vision & Mission
Virelya is dedicated to pioneering AI solutions while ensuring compliance and trust.
Join us on our journey to redefine the future of AI governance.
