Locai

Locai is a private AI infrastructure platform that enables organizations to deploy, manage, and operate modern artificial intelligence models entirely on infrastructure they own and control. Instead of relying on cloud-based AI services for model inference, the platform is designed to execute language, vision, audio, and multimodal models on local hardware, private servers, virtual private clouds, edge devices, or fully air-gapped environments. By keeping computation within an organization's infrastructure, Locai emphasizes data sovereignty, security, regulatory compliance, and reduced operating costs. The platform is built around a unified ecosystem composed of three primary components: Workspace, Control, and Link. Workspace serves as the central user interface where employees interact with AI-powered tools, including document generation, database querying, audio transcription, and information retrieval. Control manages the deployment and orchestration of AI models across an organization's infrastructure, while Link enables applications and existing software systems to access locally hosted models through compatible APIs. Together, these components provide a complete environment for developing and deploying enterprise AI without requiring cloud inference. A major focus of the platform is supporting organizations that operate in environments with strict security or regulatory requirements. Locai is designed to function in completely offline or air-gapped deployments where no internet connectivity is available. This allows sensitive data, confidential documents, meeting recordings, videos, and operational information to remain entirely within the organization's own infrastructure. The platform positions this approach as an alternative to cloud-based AI services, enabling businesses to maintain full ownership and visibility over their data while reducing exposure to external infrastructure providers. Unlike many private AI solutions that concentrate solely on conversational language models, Locai supports multiple categories of AI models within a unified platform. These include large language models for chat, summarization, extraction, and AI agents; speech recognition models for meeting and call transcription; computer vision models for image analysis and object detection; and video processing models for local visual inspection and analytics. Because all of these models operate under the same management layer, outputs from one model can be consumed by another, allowing organizations to build integrated AI workflows without relying on multiple disconnected products. The platform also emphasizes interoperability and ease of deployment. Organizations can install the Control component on their own servers using a streamlined installation process, deploy models from an integrated registry or import their own models, and connect existing applications through an OpenAI-compatible API endpoint. Support is provided for popular model formats such as GGUF, ONNX, and TensorFlow Lite, enabling teams to deploy a broad range of open-source and custom AI models. Hardware flexibility is another key advantage, with compatibility across NVIDIA GPUs, AMD hardware, Apple Silicon systems, and CPU-only environments. One of Locai's distinguishing capabilities is its support for unified data workflows. Rather than treating language, audio, image, and video models as isolated systems, the platform allows information generated by one model to flow naturally into another. For example, an image recognition model can detect a manufacturing defect, pass the result to a document generation model, and automatically produce a quality assurance report without transferring any data outside the local environment. This integrated architecture simplifies enterprise automation while maintaining complete control over sensitive information. The website also highlights a version of the platform intended for software companies building AI-enabled products. Instead of paying for centralized cloud inference, developers can route model execution to their customers' own hardware, significantly reducing infrastructure costs while preserving privacy and scalability. This approach enables SaaS providers to deliver AI-powered features without assuming the full computational expense of serving every inference request from centralized cloud infrastructure.