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Welcome to Loc.ai

Welcome to the official Loc.ai documentation your starting point for building intelligent, location-aware applications. From real-time geofencing and proximity search to spatial analytics, Loc.ai helps turn location data into smarter user experiences.

We address the critical bottlenecks of cloud cost and compute availability by enabling software vendors to execute AI models directly on end-user hardware at the edge, on-premises, or across a managed device fleet.

Let's get started 🚀

What is Loc.ai?​

Traditional AI deployments rely on centralised cloud compute: every inference request travels to a remote server, incurring latency, egress costs, and capacity constraints. As applications scale, so does the bill and the risk of hitting availability ceilings.

Loc.ai inverts this model. Rather than routing every request to the cloud, Loc.ai distributes model execution to the devices already in your users' hands. The Loc.ai:Link agent runs on each device, receives model deployments from the Loc.ai:Control platform, and handles inference entirely on local hardware. Raw data never leaves the device during inference.

The result is an inference network that grows with your user base, not against your budget.

Why Loc.ai?​

Cut cloud inference costs: Every inference that runs on a user's device is one you don't pay a cloud provider for. At scale, this compounds significantly particularly for high-frequency inference tasks like image classification or real-time language model queries.

  • Eliminate availability ceilings: Cloud GPU capacity is finite and contested. Loc.ai's device network scales horizontally as your fleet grows. No quota requests, no waitlists, no cold-start delays.

  • Keep data on-device: Prompts, inputs, and model outputs are processed locally. Sensitive data never transits your infrastructure or Loc.ai's. This simplifies compliance for privacy-sensitive verticals healthcare, legal, finance, and industrial applications.

  • Deploy without infrastructure overhead: Loc.ai handles device registration, model delivery, versioning, command orchestration, and telemetry. You define the deployment; Loc.ai manages the distribution.

  • Support offline and low-bandwidth environments: Loc.ai:Link is designed for reliability in constrained environments. Devices poll for commands and sync models asynchronously they don't require a persistent connection to operate.

Core Concepts​

Understanding these core concepts will help you navigate Loc.ai with confidence. Each component works together to create a secure, scalable, and intelligent edge AI ecosystem.

From connected devices to model deployments, these terms define how the platform operates in real-world environments.

  • Device: Any machine registered with the Loc.ai platform. Devices run the Loc.ai:Link agent and participate in the inference network. Supported hardware includes Windows, macOS, and Linux PCs, local servers, and edge hardware such as NVIDIA Jetson and Intel NUC.

  • Loc.ai:Link: The lightweight Python runtime installed on each device. It handles model execution, hardware sensor interfacing, command polling, and telemetry reporting.

  • Loc.ai:Control: The central management platform, composed of a FastAPI backend and a React dashboard. It manages device lifecycles, orchestrates deployments, and aggregates inference results and telemetry.

  • Model: A trained ML artifact stored in the Model Library. Supported formats are TFLite (image and audio classification) and GGUF (language models via llama-cpp). Models are versioned and deployed to devices through the Control platform.

  • Deployment: A targeting configuration that assigns a model to a set of registered devices. Deployments are pushed from the Control platform and picked up by devices on their next command poll.

  • Inference: A single model execution on a device. Inference runs locally; results are returned to your application and reported asynchronously to the Control platform.

  • Telemetry: Health and performance data reported by each device every 30 seconds: CPU, RAM, temperature, and inference statistics.

  • Fleet: The collection of devices registered under your account, typically organised with tags for targeting and management.