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Run AI where it matters most — at the edge and in real time. Low-latency inference, on-device and edge-deployed models, and streaming pipelines for mission-critical applications. Reduce round-trip latency, cut cloud costs, and meet strict SLAs for voice, video, and high-frequency decision systems.
Capabilities
Built for production teams that need reliability, security, and measurable outcomes.
Deploy compact, optimized models to edge devices, gateways, and regional nodes. Run inference locally for sub-50ms response times and offline-capable workflows.
Stream audio, video, and text through AI pipelines with minimal latency. Support live transcription, real-time translation, and continuous analysis.
Route requests by latency, cost, and capability. Fall back to cloud for complex tasks while keeping hot paths on the edge.
Compress and quantize models for edge deployment without sacrificing accuracy. Support ONNX, TensorFlow Lite, and custom runtimes.
Design APIs and SDKs for real-time use cases: voice assistants, live moderation, fraud detection, and interactive copilots.
Monitor latency, throughput, and errors across edge nodes. Centralized dashboards and alerts for distributed inference.
Applications
How teams are using AI Edge & Real-Time Inference to drive business outcomes.
Real-time speech-to-text, intent detection, and response generation for contact centers and voice assistants.
Frame-by-frame or stream-based analysis for moderation, object detection, and compliance in live video.
Sub-millisecond inference for trading signals, risk checks, and compliance in financial systems.
Why AI Edge & Real-Time Inference
Measurable improvements that compound over time.
Talk to our team about how AI Edge & Real-Time Inference fits into your delivery roadmap. We will help you scope priorities and plan a practical rollout.