KOGO AI and Qualcomm Technologies Plan Collaboration on Private AI Stack

KOGO AI and Qualcomm Technologies have announced their intention to collaborate on the development of an end-to-end private AI stack. The initiative is aimed at delivering secure, compliant, and high-performance AI infrastructure for enterprise use.

Integration of Agentic AI and Edge Processing Capabilities

The proposed solution will combine KOGO AI’s modular Agentic Platform with Qualcomm® Cloud AI 100 Ultra accelerators and the Qualcomm AI Inference Suite. The Agentic Platform enables the deployment of intelligent agents, fine-tuned models, and workflow automation in private or hybrid environments.

This planned collaboration is designed to meet enterprise requirements for data privacy, compliance, and operational control. The integration will focus on creating AI deployments that align with industry regulations and support high-throughput inference performance across enterprise applications.

“Private AI is more than a trend—it is a fundamental shift in how enterprises think about deploying and governing intelligence,” said Raj K Gopalakrishnan, Co-Founder & CEO at KOGO AI. “Our platform running on Qualcomm AI inference accelerators  gives organisations complete control over how AI agents and models are created, deployed, and monitored—without sacrificing innovation or speed.”

The Qualcomm Cloud AI 100 Ultra and Qualcomm AI Inference Suite provide a scalable platform designed to support high-performance large language models (LLMs) and generative AI deployments.

“This collaboration addresses the growing demand for AI solutions that combine intelligence with autonomy,” said SVP and President of Qualcomm India. "Together, we are empowering enterprises to create robust AI systems that respect data privacy while delivering exceptional performance at scale, whether on the cloud or on device. With Qualcomm’s extensive product range spanning from edge AI to the cloud, we believe the future lies in private AI”, said Savi Soin, SVP and President, Qualcomm India.

Private AI Stack for Enterprise and Edge Deployment

KOGO AI and Qualcomm Technologies have outlined the capabilities of their enterprise-grade private AI stack designed for edge and hybrid environments. The unified platform integrates AI agents, model management, hardware-accelerated inference, and compliance tools to meet enterprise requirements for data governance and operational efficiency.

Key Components of the AI Stack

• AI Agents as a Service: Includes over 60 pre-configured, no-code agents for tasks such as customer service, research, and claims processing. These agents can be deployed through APIs, enterprise applications, and chat platforms.

• Models as a Service (MaaS): Enables no-code fine-tuning and deployment of foundational and domain-specific models, including Llama, Claude, and Gemini. Features include observability tools and version control.

• Hardware-Accelerated Inference: Powered by Qualcomm Cloud AI 100 Ultra, supporting large language models (LLMs) with up to 70 billion parameters using 128 GB memory and a 150W power profile.

• Red Teaming and Compliance: Incorporates adversarial testing, vulnerability scanning, and regulatory evaluation aligned with standards such as HIPAA, PCI-DSS, and GDPR.
  
  

Industry Use Cases and Deployment Models

The AI stack is optimised for regulated sectors where public cloud infrastructure may pose latency or compliance risks. Early implementations are targeting use cases such as:

• Fraud detection and risk analytics in banking

• Claims processing automation in insurance

• Clinical decision support and patient interaction in healthcare

• Digital service delivery and policy insights in government

Deployment options include fully on-premises, hybrid cloud, and edge-based configurations, enabling secure AI integration across embedded systems, field units, and private data centres.

The platform is currently available to selected enterprise and public sector partners through an early access program. General availability is scheduled for Q3 2025, with phased releases and quarterly updates planned for expanded functionality.

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