Edge Computing Services: Process Data Where It's Created
Stop sending everything to the cloud. Our AI-enabled edge solutions build secure, low-latency applications that deliver real-time insights and superior user experiences for Industrial IoT, smart retail, and beyond.
Unlock Real-Time ProcessingTrusted by Global Leaders and Industry Innovators
Is Your Cloud-First Strategy Hitting a Wall?
For applications that demand instant response, the round-trip to a distant data center is a bottleneck. High latency, bandwidth costs, and data sovereignty concerns are limiting innovation. Edge computing moves computation and data storage closer to the sources of data, enabling a new class of intelligent, responsive applications.
The Latency Problem
In applications like autonomous vehicles, factory automation, or AR/VR, millisecond delays matter. Centralized clouds can't deliver the sub-10ms response times required for safe and effective real-time control.
The Bandwidth Bottleneck
Industrial IoT sensors, video surveillance, and smart city devices can generate terabytes of data daily. Transmitting all this raw data to the cloud is expensive, inefficient, and often unnecessary.
The Security & Privacy Risk
Processing sensitive data locally on the edge reduces the attack surface and helps comply with data residency regulations like GDPR. It minimizes the risk of data interception during transit to the cloud.
Our AI-Enabled Edge Computing Services
We provide end-to-end services to design, build, deploy, and manage powerful edge computing solutions. Our AI-enabled teams ensure your edge infrastructure is not just fast, but intelligent.
Edge Strategy & Roadmap
We help you identify the most impactful use cases for edge computing in your organization and create a phased implementation roadmap aligned with your business goals.
- Feasibility analysis and ROI modeling.
- Use case prioritization based on business value.
- Technology stack and vendor selection.
Edge Architecture Design
Our experts design resilient, scalable, and secure edge architectures that integrate seamlessly with your existing cloud and on-premise infrastructure.
- Hybrid cloud-edge and multi-cloud integration.
- Hardware selection for edge nodes and gateways.
- Network topology and connectivity planning.
Hardware & Device Consulting
Choosing the right hardware is critical. We guide you through the complex landscape of edge devices, from powerful edge servers to low-power IoT sensors.
- Evaluation of CPUs, GPUs, and specialized AI accelerators.
- IoT sensor and gateway selection.
- Device lifecycle management planning.
Edge Application Development
We build lightweight, containerized applications optimized to run efficiently on resource-constrained edge devices, enabling real-time data processing and analytics.
- Development of low-latency microservices.
- AI/ML model optimization for edge inference (Edge AI).
- Real-time data stream processing applications.
IoT & IIoT Solutions
We connect your physical assets to the digital world, enabling predictive maintenance, operational efficiency, and new revenue streams through Industrial IoT.
- Device provisioning and management at scale.
- Integration with SCADA, PLC, and MES systems.
- Development of digital twin solutions.
Edge Platform & Orchestration
We deploy and configure platforms like Kubernetes (K3s, KubeEdge) to manage and orchestrate your containerized edge applications across a distributed network of devices.
- Automated application deployment and updates.
- Centralized monitoring and management of edge nodes.
- Implementation of CI/CD pipelines for the edge.
Edge Security Services
Securing a distributed edge environment is complex. We implement a multi-layered security strategy to protect your devices, data, and applications from threats.
- Device identity and access management.
- Secure boot and hardware root of trust.
- Data encryption at rest and in transit.
Edge Monitoring & Observability
Gain complete visibility into the health and performance of your distributed edge infrastructure with our comprehensive monitoring and observability solutions.
- Centralized logging, metrics, and tracing.
- AI-powered anomaly detection and alerting.
- Performance optimization and troubleshooting.
AI Model Management at the Edge
We manage the complete lifecycle of your AI models at the edge, from deployment and monitoring to continuous retraining and updating, ensuring peak performance.
- Over-the-air (OTA) model updates.
- Performance monitoring and drift detection.
- Federated learning for privacy-preserving model training.
Why Developers.dev for Your Edge Transformation?
We're more than just developers; we are architects of intelligent, distributed systems. We combine deep expertise in cloud-native technologies with a pragmatic, business-focused approach to deliver edge solutions that create real value.
AI-Enabled at the Core
Our teams leverage AI tools for faster development, smarter testing, and proactive monitoring, ensuring your edge solution is not just built, but built to excel.
Full-Stack Edge Expertise
From embedded systems and IoT protocols to cloud-native orchestration and AI/ML, we have the end-to-end expertise to manage the entire edge stack.
Deep Industry Knowledge
We understand the unique challenges of manufacturing, logistics, retail, and healthcare, tailoring edge solutions that solve real-world operational problems.
Security-First Mindset
With certifications like ISO 27001 and SOC 2, we build security into your edge architecture from day one, not as an afterthought.
Global Delivery, Local Context
Our global delivery model provides cost-effective, 24/7 development, while our strategists ensure solutions meet the specific needs of your target markets in the US, EMEA, and Australia.
Transparent Partnership
We offer flexible engagement models, a 2-week paid trial, and full IP transfer. We succeed when you succeed, and our 95% client retention rate proves it.
Edge Computing in Action: Success Stories
Predictive Maintenance for a Global Manufacturer
Industry: Manufacturing
Client Overview: A leading automotive parts manufacturer with facilities across North America and Europe was struggling with unplanned downtime on their assembly lines. Their existing cloud-based monitoring system had significant latency, meaning critical alerts often arrived too late to prevent equipment failure.
Problem: The inability to predict equipment failure in real-time was costing the company millions annually in lost production and emergency repair costs. They needed a solution to analyze sensor data from their CNC machines and robotic arms instantly, right on the factory floor.
Key Challenges:
- High-frequency vibration and temperature data overwhelming their network.
- Latency in cloud analytics making real-time intervention impossible.
- Concerns about sending sensitive operational data off-site.
- Integrating with a diverse range of legacy factory equipment.
Our Solution:
We designed and deployed an Industrial IoT edge solution that processed sensor data locally. AI models for anomaly detection were deployed on ruggedized edge gateways installed directly on the factory floor.
- Deployed lightweight AI models using TensorFlow Lite on edge devices.
- Established a local data processing pipeline to analyze vibration data in under 5ms.
- Only anomaly alerts and summary data were sent to the central cloud for trend analysis.
- Utilized MQTT and OPC-UA protocols for seamless integration with existing machinery.
"The real-time insights are a game-changer. We're now fixing problems before they happen. Developers.dev delivered a robust edge solution that integrated perfectly with our existing operations."
Real-Time Customer Analytics for a Retail Chain
Industry: Retail
Client Overview: A large fashion retailer wanted to personalize the in-store experience and optimize store layouts. Their goal was to understand customer behavior—foot traffic patterns, dwell times in different sections, and queue lengths—without infringing on privacy.
Problem: Sending high-resolution video streams from hundreds of stores to the cloud for analysis was prohibitively expensive and raised significant privacy concerns. They needed a way to extract anonymous analytics data directly within each store.
Key Challenges:
- Massive bandwidth requirements for multi-camera video streams.
- Compliance with GDPR and CCPA for customer privacy.
- Need for immediate insights to allow staff to react to long queues.
- Deploying and managing a consistent software stack across 500+ locations.
Our Solution:
We developed a computer vision application deployed on in-store edge servers. The application processed video feeds locally, using AI to generate anonymous metadata like customer counts and heatmaps. No personally identifiable information (PII) ever left the store.
- Used NVIDIA Jetson devices for efficient, low-power AI video processing.
- Developed a system that converted video to anonymous data points in real-time.
- Built a dashboard that alerted store managers to long checkout queues instantly.
- Leveraged Kubernetes (K3s) for remote orchestration and application updates.
"Developers.dev gave us the in-store intelligence we needed without the privacy headache. The ability to optimize layouts based on real-time data has directly impacted our sales."
Optimizing Fleet Management for a Logistics Leader
Industry: Transportation & Logistics
Client Overview: A national logistics company with a fleet of over 5,000 trucks needed to improve fuel efficiency, enhance driver safety, and provide real-time tracking to its customers. Their existing GPS-based system only provided periodic location updates.
Problem: Lack of real-time data from vehicles meant they couldn't proactively reroute trucks around traffic, monitor driver behavior for safety coaching, or provide accurate ETAs. Constant connectivity in remote areas was also a major challenge.
Key Challenges:
- Intermittent network connectivity on long-haul routes.
- Processing data from multiple in-vehicle sensors (GPS, accelerometer, CAN bus).
- Need for immediate alerts for unsafe driving events (e.g., hard braking).
- Minimizing cellular data costs across a massive fleet.
Our Solution:
We engineered a comprehensive edge solution for their fleet. Each truck was equipped with an edge computing gateway that collected and processed sensor data locally. The system could buffer data during connectivity loss and only transmitted critical events and summary reports over the cellular network.
- Developed an application to run on in-vehicle gateways for data aggregation.
- Implemented on-device logic to detect events like harsh acceleration or idling.
- Used a store-and-forward mechanism to ensure no data was lost during outages.
- Created a centralized dashboard for fleet managers with real-time alerts and analytics.
"This solution transformed our fleet operations. The on-board processing gives us instant alerts and has saved us a fortune in data costs. The reliability is outstanding, even in areas with poor signal."
Our Edge Technology Stack & Tools
We are experts in the modern, cloud-native toolchain required to build and manage sophisticated edge solutions at scale. We select the right tools for the job to ensure performance, reliability, and security.
Frequently Asked Questions
Edge computing is a decentralized computing model where data processing and storage happen closer to where the data is generated, rather than sending it to a centralized cloud. Think of it as having mini-data centers (edge nodes) on-site, like in a factory, a retail store, or on a vehicle. This drastically reduces latency (delay) and saves on bandwidth costs, making it ideal for applications that need to react instantly.
They are complementary, not competitors. Cloud computing offers massive, centralized processing power and storage. Edge computing offers distributed, low-latency processing. A common architecture (hybrid model) involves the edge handling immediate, real-time tasks and sending only summarized or important data to the cloud for long-term storage, big data analytics, and model training.
Key industries include:
- Manufacturing (IIoT): For predictive maintenance, quality control, and robotics.
- Retail: For real-time inventory management, smart checkout, and in-store analytics.
- Telecommunications: For 5G network optimization and delivering low-latency services.
- Healthcare: For real-time patient monitoring and processing medical imaging data securely.
- Transportation: For autonomous vehicles, fleet management, and smart traffic systems.
Edge security is a multi-layered process. It starts with a hardware root of trust and secure boot on the device itself. We then implement robust device identity and access management, encrypt all data both at rest and in transit, and use containerization to isolate applications. Continuous monitoring and a zero-trust network architecture are crucial components of our security strategy.
This is where edge orchestration platforms are essential. We use tools like Kubernetes (specifically lightweight versions like K3s or KubeEdge) to manage the entire application lifecycle remotely. This allows us to automate the deployment, updating, and scaling of containerized applications across the entire fleet of devices from a central control plane, ensuring consistency and reliability.
Ready to Move Intelligence to the Edge?
Let's discuss how our AI-enabled edge computing services can reduce your latency, cut your cloud costs, and unlock new possibilities for your business. Schedule a free, no-obligation consultation with our edge solution architects today.
Request a Free Consultation