Delivering AI on Edge
Production-grade Edge AI solutions for enterprises that need real-time decisions, lower cloud costs, and secure on-premise processing.
* Vision analytics for manufacturing quality and safety on edge cameras.
* Smart retail shelves and in-store analytics without streaming video to the cloud.
* Edge LLM assistants embedded in vehicles and industrial equipment for hands-free operations
From model idea to fleets of edge devices, Mobodexter manages the full lifecycle so your team can focus on the application
Train & Optimize
We help you choose or build the right model, then optimize with techniques such as pruning, quantization, and hardware-aware tuning to meet your device constraints.
Result: Higher accuracy at lower latency and power on real edge hardware.
Mobodexter designs and validates edge AI stacks on NVIDIA, Intel, and AMD so you get the best performance for your workload, not just a generic reference design
Choose Compute Architecture
- NVIDIA for vision-heavy AI
Ideal for GPU-accelerated computer vision and deep learning on devices like Jetson and data center GPUs.
We provide tuned containers, TensorRT-optimized models, and deployment playbooks for NVIDIA-based edge fleets. - AMD for flexible GPU compute
Suitable for organizations standardizing on AMD GPUs and CPUs for cost-effective edge compute.
We help you validate performance and reliability on AMD-based edge servers for your specific AI stack - Intel for CPU-centric analytics
Best for mixed workloads that combine traditional analytics with AI using CPUs, iGPUs, and accelerators with OpenVINO.
We optimize inference pipelines to fully exploit Intel architecture while keeping power and cost in check.
Not sure which way to go? Share your workload and we’ll recommend the optimal hardware + software stack
Choose Computing
Deploy EdgeSage on proven edge server platforms from Dell, HPE, and Lenovo for reliable, secure, and manageable edge AI at scale.
What’s included:
- Pre-integrated hardware + EdgeSage software stack for AI workloads.
- Secure connectivity and remote management for multi-site edge fleets.
- Implementation, performance tuning, and ongoing support services.
Deployment patterns:
- Single-site edge server for factory or warehouse analytics.
- Multi-site fleets across retail stores or branches, centrally managed.
- Hybrid setups where models are trained in the cloud and deployed at the edge.
Cut bandwidth and cloud costs while meeting strict latency and data residency requirements with on-premise edge AI
Run Edge AI Models
Mobodexter delivers ready-to-run edge AI solutions built on CNNs, RNNs, lightweight architectures, anomaly detectors, and custom models
- Visual inspection & safety (CNNs, lightweight models)
Defect detection, safety compliance, and occupancy monitoring with camera-based analytics on edge devices. - Predictive maintenance (Anomaly detection models)
Detect unusual patterns in sensor data from machines and vehicles to prevent downtime and costly failures. - On-device assistants (RNNs, LLMs)
Voice and text assistants embedded in vehicles, kiosks, and industrial consoles for hands-free interaction. - Custom vertical solutions
We design custom edge AI models for your specific environment in manufacturing, retail, smart city, and more, then deploy them via EdgeSage.
Edge deployments have helped customers reduce data transfer to the cloud by up to 80% while improving responsiveness for critical decisions
Start Your Edge AI Project
Whether you are modernizing an existing product or launching a new edge-native solution, Mobodexter can join at any stage of your journey
SOLUTION DESIGN
Edge AI solution design and architecture for your devices and sites.
MODEL SELECTION
Model selection, optimization, and integration with your existing systems.
MODEL DEPLOYMENT
EdgeSage setup for deployment, orchestration, and monitoring across fleets.