Private AI Deployment Engine

Private AI Deployment Engine is a software system for deploying, managing and operating private AI models within customer-controlled infrastructure. It enables organisations to register AI servers, deploy Large Language Models and expose dedicated inference services while keeping sensitive data inside their own infrastructure. The engine centralises the management of AI infrastructure and model deployments, providing organisations with greater control over where models run, how computational resources are used and how AI services are exposed to enterprise applications. This approach brings AI models closer to enterprise data instead of sending sensitive information to external AI providers, improving data sovereignty, privacy, governance and operational control. Further details: https://www.luca-bds.com/how-to-solve-access-to-sensitive-data-with-local-ai/

The Private AI Deployment Engine provides centralized management of infrastructure dedicated to AI workloads. AI servers can be registered and subsequently used as deployment targets for private models, providing a unified way to manage the infrastructure where AI services are executed. The engine supports the registration and management of remote AI servers, deployment and lifecycle management of private Large Language Models, and selection of model versions and deployment infrastructure. Models can be executed using CPU or GPU resources depending on their requirements and the available hardware. Deployed models are exposed through dedicated inference services accessible through APIs, enabling integration with enterprise applications. The engine also provides monitoring capabilities for model availability, inference performance, errors and computational resource usage. The platform supports container-based deployments in environments such as Docker and Kubernetes. Its modular architecture separates AI orchestration, model deployment and inference services, allowing each layer to evolve and scale independently from the enterprise applications consuming the models.

Requires Linux-based infrastructure capable of running containerized workloads. Depending on model size, quantization and expected workload, dedicated GPU resources may be required. Network connectivity is required between the management platform and registered AI servers. Applications consuming deployed models integrate with the inference services through APIs.

Telecommunications operators, industrial companies, utilities and other organisations managing sensitive or regulated data that require private AI processing.

Proprietary software developed by CIC Consulting Informático. Reuse and integration are subject to commercial agreement and licensing conditions defined by CIC.