AI Integration Platform for Innovation Centers

Build Your AI Research, Training, and Deployment Ecosystem

Core Mobile’s AI Systems Integration Platform (AI-SIP) is purpose-built for academic and public-sector AI Centers of Excellence looking to create a scalable, interdisciplinary, and secure testbed for AI research and real-world application. AI-SIP integrates data across silos, enables modular use of AI/ML/LLM engines, and supports continuous innovation—from exploratory research to operational deployment.

Whether your institution is launching a new AI innovation hub or expanding existing capabilities, AI-SIP delivers everything you need: data connectivity, flexible AI model orchestration, hybrid cloud/on-premise support, and full compliance with HIPAA, FISMA, and FedRAMP standards.

Key Benefits

  • Multi-Engine AI/ML/LLM Support: Plug and play with OpenAI, AWS SageMaker, Google Vertex, NVIDIA, Mistral, and more.

  • Turnkey Data Integration: Ingest structured, unstructured, IoT, audio, and video sources through real-time pipelines.

  • On-Premise + Cloud Flexibility: Operate securely in your data center, on the cloud, or in a hybrid environment.

  • Comprehensive AI Lifecycle Tools: Manage training, validation, inference, and observability—all from one platform.

  • Built-in Research + Startup Enablement: Incubate innovation with prebuilt interfaces, training tools, and commercialization support.

  • Fully Compliant + Federally Proven: National Authority to Operate (ATO) from the US Government; meets HIPAA, HECVAT, and NIST RMF (800-53) standards.

Transform Into an

AI Innovation Hub

Whether you’re building a regional testbed, a national research collaboration, or a translational AI commercialization pipeline, Core Mobile’s AI-SIP delivers the speed, security, and scalability you need.

Features:

  • Unified AI/ML/LLM Pipeline

    Train, deploy, and manage models across multiple engines and data types using a secure and extensible architecture. Supports batch and real-time inference, containerized workflows, and A/B testing.

  • Context-Based Data Integration

    Map institutional systems, including EHRs, PACS, HR, LMS, and IoT, to AI-ready datasets using drag-and-drop configuration tools and BPMN-defined workflows.

  • Ambient Data Collection & Annotation

    Automatically extract data from real-world interactions (in-person or virtual) for use in model training and real-time analytics. Integrated tools allow expert annotation with feedback loops.

  • Flexible Deployment Models

    Deploy on university-owned servers, cloud providers (Azure, AWS), or both. Support for GPU-enabled infrastructure (e.g., NVIDIA H100/H200), container orchestration, and CI/CD pipelines.

  • Secure Research Sandbox

    Enable safe experimentation with pseudonymized datasets, isolated environments, and full traceability for compliance and reproducibility. Logs are retained in both research and clinical data marts.

  • Built-in Video and Voice AI Tools

    Use out-of-the-box video monitoring, voice transcription, and behavioral analysis features for rapid prototyping and development.

  • Prebuilt Interfaces for Web, Mobile & IoT

    Accelerate time-to-value with white-labeled apps and portals for researchers, clinicians, students, or external collaborators.

  • Governance, Privacy, and Compliance

    Designed for secure, ethical AI. Includes end-to-end encryption, MFA, RBAC, de-identification modules, audit logs, and integration with FedRAMP-authorized environments.

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    Kathryn Camille Holeyfield, MD, Anesthesiologist at Stanford