Edge and Cloud AI Deployment
for Smart Cameras
We design and optimize AI systems that balance real-time performance on the edge with scalable cloud intelligence,
ensuring reliability, speed, and adaptability across millions of connected devices.
Our Expertise
in Edge and Cloud AI Deployment
We design and deploy AI that performs reliably in real conditions. Our Edge AI and Cloud AI teams ensure that models operate efficiently
on limited hardware while scaling in the cloud for retraining, monitoring, and long-term stability.
We build architectures that connect on-device inference with cloud-based analytics, creating systems that learn, adapt,
and maintain accuracy throughout their lifecycle.
Edge AI
Our engineers optimize AI models for real-time performance on embedded and on-device systems with limited compute, memory, bandwidth, and power. Hardware-aware optimization and efficient runtimes keep detection and tracking stable in production.
Our work includes:
Model optimization and compression for ARM CPUs and accelerators (GPU, NPU, DSP)
Integration of inference runtimes and pipelines for video analytics, event and motion understanding, and other vision or sensor workloads
Tuning latency, throughput, and power to meet product requirements
Cloud AI
We design scalable cloud infrastructures that support model retraining, validation, and orchestration across large device fleets.
Our expertise includes:
Model management, versioning, and deployment at scale
Automated validation and continuous performance monitoring
Distributed inference and retraining loops for adaptive AI systems
AI Model Optimization
We apply model pruning, quantization-aware training, and knowledge distillation to achieve high accuracy with reduced computational demand. Each deployment is tailored to the target hardware and architecture to maintain efficiency and reliability.
Challenges We Solve for
AI-Powered Smart Cameras
We make sure that smart camera systems remain efficient and reliable across both edge and cloud environments.
Our teams address the technical challenges that affect long-term accuracy and performance.
Balancing speed and accuracy
Achieving real time performance on limited hardware without losing precision.
Maintaining consistency across environments
Aligning AI behavior between local inference and cloud analytics for uniform results.
Preventing model drift
Establishing automated validation and retraining to preserve accuracy over time.
Scaling reliably across large deployments
Managing rollout, monitoring, and rollback processes across device fleets.
Minimizing latency in real time use
Optimizing data flow and inference pipelines to meet operational timing targets.
Our Process
We follow a refined four-phase process shaped by years of experience in developing complex embedded and AI-powered systems.
It ensures clarity, technical alignment, and long-term scalability across every stage of product development.
Analyze & Scope
Stakeholder alignment sessions to define requirements, constraints, and business priorities. We evaluate technical feasibility, assess hardware and software dependencies, and outline a clear execution roadmap.
R&D (Research & Delivery)
Delivery-focused research to validate system concepts through rapid prototyping, performance testing, and proof-of-concept builds. We combine experimentation with practical implementation to ensure readiness for production.
Launch & Validate
Pilot programs, controlled rollouts, and early production trials. We monitor reliability, collect feedback, and validate performance across firmware, AI, and cloud integrations before full-scale release.
Scale & Sustain
Continuous optimization through feature updates, AI model tuning, and hardware revisions. We ensure long-term reliability, maintainability, and scalability across devices and software systems in production.
Why Choose SQUAD
We are a single engineering partner that takes your product from concept to manufacturing readiness. We combine product design, hardware, firmware, cloud, machine learning, and mobile into an integrated end-to-end process, so you get a complete, production-grade solution with predictable execution.
Our R&D is built to deliver, not just explore. We use a proven, codified delivery process and modern AI-based tools to move quickly, validate ideas, and deliver meaningful improvements with clear business value.
Our 6,500 m² (70,000 sq ft) labs help teams accelerate validation and product maturity. With specialized equipment and test benches, we detect issues early, reduce technical risk, and solve deep engineering challenges faster.
We have 700+ tech and product development experts across embedded systems, cloud, mobile, and AI. Our cross-functional teams build seamless user experiences and complex products, backed by strong engineering discipline and recognized certifications.
We engineer for measurable impact. Our delivery model is focused on on-time, on-budget execution, high customer satisfaction, and confident product launches, helping teams move from idea to market. Our track record includes 500+ projects, 50+ devices, 100+ app releases, and 20+ AI features.
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Other Related Services
EMBEDDED ENGINEERING
From silicon to system-level firmware, we deliver fully integrated, end-to-end solutions that combine expert hardware design with embedded software, helping our clients accelerate time-to-market and ensure long-term reliability.
MOBILE ENGINEERING
We develop native iOS and Android apps that interface directly with smart camera hardware—handling real-time video streaming, BLE/Wi-Fi communication, on-device event processing, and live view optimization.
HARDWARE ENGINEERING
We help companies design, build, and scale hardware for smart cameras, sensors, security systems and connected products. Our team handles every step—planning, design, testing, certification and production support—to deliver reliable, efficient solutions ready for real-world use and market growth.