Edge AI & On-Device Intelligence

AI That Runs Where
Decisions Happen

We deploy intelligence directly on devices and machines for low-latency decision-making, real-time perception, privacy, and reliable operation without constant dependence on cloud infrastructure.

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Edge Models Deployed
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Avg Inference Latency
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Less Cloud Bandwidth
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On-Device Uptime
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EDGE AI
Capabilities

Edge AI &
On-Device Capabilities

Deploy models on devices and machines so intelligence runs at the source of data - with low latency, offline resilience, and strong privacy by design.

On-device AI

On-Device AI

Run neural networks fully on phones, gateways, cameras, and industrial controllers so decisions happen locally without round-trips to the cloud.

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TinyML

TinyML

Compress and optimize models for microcontrollers and ultra-low-power chips - keyword spotting, anomaly detection, and sensing at milliwatt scale.

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Edge vision

Edge Vision

Deploy detection, classification, and tracking models on smart cameras and vision SoCs for real-time inspection, safety, and monitoring at the edge.

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Embedded AI

Embedded AI

Integrate AI into firmware and RTOS-based systems with constrained memory, deterministic timing, and long product lifecycles.

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AI accelerators

AI Accelerators

Target NPUs, TPUs, DSPs, and GPUs on edge silicon - with optimized kernels, batching strategies, and hardware-aware model design.

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Real-time inference

Real-Time Inference

Meet strict latency budgets for control loops, alerts, and interactive experiences - with profiling, scheduling, and continuous monitoring on device.

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Development process

Our Edge AI Deployment Process

01

Workload & Constraints

Define latency, power, memory, and connectivity limits for the target device and use case.

02

Model Selection

Choose or design architectures that fit edge budgets while meeting accuracy requirements.

03

Compression & Quantization

Apply pruning, distillation, and INT8/INT4 quantization to shrink models for device memory.

04

Hardware Targeting

Compile and optimize for NPUs, GPUs, DSPs, or CPUs using vendor toolchains and kernels.

05

Runtime Integration

Embed inference into firmware, RTOS, or edge OS with sensors, buffers, and I/O pipelines.

06

Profiling & Tuning

Measure latency, throughput, thermal, and power; tune batch size, threads, and clocks.

07

Validation

Test accuracy on device data, offline scenarios, and edge cases under real conditions.

08

Deployment

Ship signed packages with secure boot, OTA paths, and fallback modes for field devices.

09

Fleet Optimization

Monitor devices, retrain on edge data, and push incremental model updates over time.

Business Impact

Outcomes that compound

Measurable results from edge and on-device AI systems we design, optimize, and deploy in the field.

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Avg Inference Latency
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Less Cloud Bandwidth
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Faster Local Decisions
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Lower Inference Cost
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On-Device Uptime
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Edge Models Deployed
Tech stack

Technologies We Use

We select runtimes, toolchains, and silicon based on latency, power, memory, and long-term support for edge products.

Ready to Run AI on the Edge?

Deploy on-device models, TinyML, edge vision, and real-time inference that work offline, stay private, and meet strict latency budgets.

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FAQ

Answers for technical & business stakeholders

Put Intelligence on the Device

Partner with our engineers to build on-device AI, TinyML, edge vision, and accelerator-optimized inference that runs where your data and decisions live.

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