Industries

Run your AI locally, securely, and sustainably.

Defense & The Warfighter

triangle

Deploying AI at the edge for the defense and intelligence community can significantly enhance real-time analysis, decision-making, operational efficiency, and security, empowering defense personnel to operate more effectively in both combat and intelligence-gathering environments.

AI at the edge can process data from sensors, cameras, and other intelligence-gathering tools in real time, providing up-to-the-minute situational awareness to field operators and command centers. During military operations, an edge-based SLM could process satellite images or drone footage on-site, detecting troop movements, vehicles, or other threats in real time, helping commanders make rapid decisions.

image_0
image_1

More Industries

Manufacturing

triangle

Deploying AI at the edge for manufacturing can enhance real-time decision-making, reduce operational costs, and improve overall productivity within a manufacturing environment.

AI can process sensor data locally to identify abnormalities or deviations in production. This allows for real-time intervention without sending data to a cloud server, reducing latency and preventing potential failures. Edge devices running SLMs can flag irregular vibrations in machinery or detect anomalies in product dimensions or quality, alerting operators to take immediate action.

image_0
image_1

Transportation

triangle

Deploying AI at the edge in transportation offers the potential to increase safety, improve operational efficiency, optimize routes, and enhance the overall experience for both passengers and drivers. By processing data locally, transportation systems can make real-time, context-aware decisions while minimizing network dependencies.

SLMs can process traffic data locally on vehicles or infrastructure (e.g., traffic lights, road sensors) to provide real-time route optimization based on current conditions.

Edge devices running SLMs in delivery trucks or taxis could reroute vehicles instantly when detecting congestion, accidents, or road closures, leading to more efficient routing and fuel savings.

image_0
image_1

Oil & Gas

triangle

Deploying AI at the edge in the oil and gas industry can improve safety, optimize production, enhance environmental protection, and reduce operational costs, all while improving real-time decision-making capabilities in remote and critical environments.

AI can analyze sensor data from drilling equipment, pipelines, and other infrastructure to monitor the health of machinery in real time and predict when maintenance is needed, preventing costly downtime. On an oil rig, SLMs could process vibration, pressure, and temperature data locally to detect early signs of equipment wear or failure, reducing the risk of unexpected breakdowns and optimizing maintenance schedules.

image_0
image_1

Health Care

triangle

Deploying AI at the edge in health care can enhance patient outcomes, streamline operations, and improve the overall quality of care by enabling real-time, privacy-preserving, and context-aware decision-making in a wide range of medical settings.

SLMs at the edge can analyze real-time patient data from wearable devices, sensors, or hospital equipment to detect early signs of health deterioration and trigger alerts for timely intervention.

An SLM in a hospital setting could process data from a patient’s vital signs, like heart rate or oxygen levels, to detect early signs of sepsis or cardiac arrest, alerting healthcare providers instantly for a rapid response.

image_0
image_1

Financial Services

triangle

Deploying at AI the edge in financial services can significantly enhance security, privacy, and operational efficiency while improving customer experiences and real-time decision-making, making the sector more responsive and agile.

AI can analyze transaction data in real-time at the edge, detecting anomalies and potential fraud instantly, without the latency of sending data to the cloud. A mobile banking app could deploy an edge-based SLM to analyze customer transactions locally, flagging suspicious behavior such as unusual withdrawal patterns or logins from unexpected locations, and blocking potentially fraudulent transactions in real-time.

image_0
image_1