Edge AI & Computer Vision Development Services

Real-time, on-device vision AI that runs fast, works offline and keeps your data private.

KriraAI is an AI development company that designs, trains and deploys computer vision models directly on cameras, gateways, mobile phones and on-premise servers. Instead of sending video to the cloud, our edge AI solutions process images where they are captured. Your systems detect, count, inspect and respond in milliseconds.

With 5+ years of AI delivery, 100+ projects shipped and a dedicated in-house AI research team, we handle the full on-device AI lifecycle, from proof of concept to production rollout.

Edge AI and on-device computer vision development services.

Years of AI Delivery

100+

Projects Delivered

In-House

AI Research Team

Global

Clients Across India, USA & Global Markets

Trusted by Enterprises and Institutions

What Is Edge AI and Why Does It Matter?

Edge AI means running artificial intelligence models on local hardware, at the "edge" of the network, instead of in a remote data center. When edge AI works on images and video, it is called on-device vision or edge vision AI.

AI at the edge removes the round trip to the cloud. A camera running an edge AI model can spot a product defect, read a number plate or flag a safety violation in under 50 milliseconds, even without internet access.

Edge computing AI also cuts bandwidth costs, because only results and alerts leave the device, not raw video. And when sensitive footage never leaves your premises, privacy compliance becomes far simpler.

Our Edge AI Solutions

We offer end-to-end edge AI and computer vision development services, from a single smart camera to a fleet of connected devices.

On-Device AI Development

We build on-device machine learning models that run natively on Android, iOS, embedded Linux and microcontrollers. Every model is designed around your hardware's memory, power and latency limits from day one.

Embedded Vision Systems

Our embedded vision engineers combine cameras, sensors and edge processors into complete working systems. We handle camera and lens selection, image pipelines, firmware integration and model deployment.

AI Video Analytics

Turn your existing CCTV and IP camera feeds into real-time insights. Our AI video analytics detect people, vehicles, objects and events, then push alerts and dashboards to your team.

Real-Time Object Detection on Edge Devices

We deploy object detection, segmentation, tracking and pose estimation models, including YOLO, RT-DETR and custom architectures. Each one is tuned to run at 30+ FPS on your target edge hardware.

AI Model Optimization for Edge Devices

Already have a model that is too slow or too large? We apply quantization, pruning, knowledge distillation and compiler optimization with TensorRT, OpenVINO and TensorFlow Lite. Models typically shrink up to 4x and run several times faster, with minimal accuracy loss.

TinyML Development

For battery-powered and low-cost products, we build TinyML models that run on microcontrollers such as ARM Cortex-M and ESP32. This brings vision and sensor intelligence to devices that cost only a few dollars.

Edge AI Camera Solutions

We develop smart edge AI camera solutions where the camera itself runs the model. They suit retail stores, traffic junctions, factories and remote sites with weak connectivity.

On-Device Computer Vision App Development

We build mobile apps with on-device computer vision using Core ML, TensorFlow Lite and ML Kit. Typical features include document scanning, product recognition, AR measurement and visual inspection.

Edge AI vs Cloud AI: Which One Do You Need?

Edge AI makes decisions on the device; cloud AI sends data to remote servers for processing. The right choice depends on speed, privacy and connectivity needs.

Factor

Edge AI

Cloud AI

Latency

Milliseconds, on the device

Higher, depends on network

Internet needed

No, works offline

Yes, always

Data privacy

Data stays on-device

Data leaves your premises

Bandwidth cost

Low, only results are sent

High for continuous video

Compute power

Limited by device hardware

Practically unlimited

Best for

Real-time detection, remote sites

Model training, heavy analytics

Most production systems use a hybrid setup: edge AI for real-time decisions and the cloud for training, retraining and fleet-wide reporting. We design both sides.

Benefits of On-Device Vision AI

Platforms and Technologies We Work With

NVIDIA Jetson development services: we build multi-camera vision pipelines on NVIDIA Jetson Orin Nano, Orin NX and AGX Orin using JetPack, DeepStream and TensorRT. That gives you GPU-accelerated inference in a compact, industrial-ready module.

DevelopmentNVIDIA Jetson

DevelopmentNVIDIA GPUs

DevelopmentRaspberry Pi

DevelopmentGoogle Coral Edge TPU

DevelopmentQualcomm Snapdragon

DevelopmentHailo

DevelopmentARM Cortex-M

DevelopmentESP32

Edge AI Use Cases Across Industries

Edge AI for Manufacturing

Automated visual quality inspection, surface defect detection, assembly verification and worker safety (PPE) monitoring on the production line.

Edge AI for Retail

Footfall counting, queue monitoring, shelf and planogram checks, and loss prevention, all without streaming store video to the cloud.

Smart Cities and Traffic

Automatic number plate recognition (ANPR), vehicle counting and classification, and traffic violation detection at the roadside.

Healthcare

Patient fall detection, room occupancy monitoring and medical device vision, with sensitive data kept on-premises.

Agriculture, Security and Surveillance

Crop and livestock monitoring, perimeter intrusion detection and smart surveillance cameras for remote sites.

Logistics and Supply Chain

Package counting, damage detection at loading docks, and vehicle and container tracking in yards and warehouses.

Education

Handwritten answer-script evaluation and document vision, as in our AI exam grading system that scores thousands of scripts in minutes per paper.

Our Edge AI Development

Process

Discovery

We define the use case, success metrics and target hardware.

Data Collection and Labeling

We gather and annotate images that match real field conditions.

Model Training

We train and validate computer vision models for accuracy and robustness.

Edge Optimization

We quantize, prune and compile the model for your device.

Deployment

We integrate the model with cameras, apps and your existing systems.

Monitoring and Support

We track performance, retrain on new data and ship OTA updates.

Computer Vision Projects We've Delivered

View All

Textile Printing · Visual QC · Fabric Waste Reduction

AI-Powered Positional Printing QC Automation for Textile Manufacturing

How screen-level visual monitoring replaced 18 QC operators per day watching for misalignment across jacquard, saree border, and lace printing lines.

Read full case study

PM Kusum Yojana · Solar Infrastructure Verification

AI-Powered Solar Installation Verification and Fraud Detection for PM Kusum Yojana

Replacing manual field photo audits with AI that detects pixel-level fraud no human reviewer can see.

Read full case study

Government / Defense · On-Premise AI · Secure Document Retrieval

How the Indian Navy Replaced Manual Document Search With a Secure, On-Premise AI Assistant

Built entirely inside the client's own infrastructure, the system grounds every answer in verified internal documents instead of open internet knowledge.

Read full case study

Why Choose KriraAI as Your Computer Vision Company

5+ Years of AI Delivery

100+ enterprise and startup projects shipped to production, not just demos.

Ready to Build Your Edge AI Solution?

Tell us about your use case. KriraAI's research-backed engineering team will suggest the right hardware, model and rollout plan, free of charge.

Frequently Asked

Questions

Edge AI is artificial intelligence that runs directly on local devices such as cameras, phones or embedded boards instead of in the cloud. It processes data where it is created, which gives faster responses, offline operation and better data privacy.

On-device computer vision is when a device analyzes images or video itself, using a built-in AI model, without sending footage to a server. Examples include a smart camera detecting intruders or a phone app recognizing products in real time.

Edge AI processes data on the device, while cloud AI sends data to remote servers for processing. Edge AI is faster, works offline and keeps data private; cloud AI offers more computing power for training and large-scale analytics. Many businesses combine both.

The main benefits are real-time decisions, lower bandwidth and cloud costs, stronger data privacy and reliable offline operation. These make edge AI ideal for manufacturing, retail, traffic, healthcare and security use cases.

The best hardware depends on your workload, budget and power limits. NVIDIA Jetson suits multi-camera, high-accuracy vision; Google Coral and Hailo suit low-power acceleration; Raspberry Pi suits prototypes; and microcontrollers like ESP32 suit TinyML products.

Yes, optimized AI models can run fully offline on small devices. Techniques like quantization, pruning and TinyML let models fit on phones, single-board computers and even microcontrollers with a few hundred kilobytes of memory.

We optimize models using quantization, pruning, knowledge distillation and hardware-specific compilers such as TensorRT, OpenVINO and TensorFlow Lite. This usually makes models up to 4x smaller and several times faster with minimal accuracy loss.

Yes, most IP and CCTV cameras can be connected to an edge AI device that runs video analytics on their feeds. This adds object detection, people counting or ANPR without replacing your cameras.

Most KriraAI projects take 4 to 16 weeks from discovery to deployment. A focused proof of concept sits at the shorter end, while multi-camera or multi-site rollouts take longer, depending on data, model complexity and hardware.

Edge AI cost depends on the use case, data, hardware and number of devices. Projects range from a few thousand dollars for simple builds to six figures for enterprise-scale systems, and KriraAI shares a transparent, scope-based quote after a free discovery call.

Edge AI & Computer Vision Development Services

Trusted by Enterprises and Institutions

What Is Edge AI and Why Does It Matter?

Our Edge AI Solutions

On-Device AI Development

Embedded Vision Systems

AI Video Analytics

Real-Time Object Detection on Edge Devices

AI Model Optimization for Edge Devices

TinyML Development

Edge AI Camera Solutions

On-Device Computer Vision App Development

Edge AI vs Cloud AI: Which One Do You Need?

Benefits of On-Device Vision AI

Real-Time Response

Works Offline

Stronger Privacy

Lower Operating Cost

Scales Easily

Energy Efficient

Platforms and Technologies We Work With

Edge AI Use Cases Across Industries

Edge AI for Manufacturing

Edge AI for Retail

Smart Cities and Traffic

Healthcare

Agriculture, Security and Surveillance

Logistics and Supply Chain

Education