EDGE AI HARDWARE & SOFTWARE INNOVATION
Build and deploy intelligent systems at any scale
Works with:
From concept to device deployment end to end
1.Assess & architect solutions
We design and build end-to-end on-device AI systems, covering everything from chip selection and hardware optimization to operating system integration. Our solutions are then distributed directly through major marketplaces like Walmart and Amazon, enabling broad accessibility.
2.Build & integrate systems
Each system is engineered for speed, privacy, and reliable real-world performance, allowing AI to run entirely on-device without relying on the cloud. Through our partnership with Google, we design the software that fuses Moxo AI's operating system directly with hardware into one unified stack.
3.Deploy & scale outcomes
Models run locally, data stays on-device, and your system scales without cloud bottlenecks. In partnership with Anthropic, we expand what people can do with AI. Systems continuously adapt and improve locally, getting smarter as new data comes in, without sending anything to a server.
Put real intelligence inside the device not in a distant server
One architecture from chip to application
Combine hardware selection, firmware, model training, and edge deployment into one seamless AI delivery workflow. Drive mission outcomes. Eliminate the gap between software and silicon.
Faster inference, lower power, zero cloud dependency
On-device AI means sub-millisecond response times, no bandwidth costs, and AI that keeps working even when connectivity drops. Built for laptops, phones, and electronics that can't afford cloud latency.
Every edge device generates structured intelligence
Capture inference outputs, sensor signals, confidence scores, and performance telemetry as structured data. Enable better lifecycle visibility, device health monitoring, and smarter decision-making across your hardware fleet.
Scale edge deployments without the complexity
Manage model updates, hardware expansions, and new device integrations with structured workflows. Deploy new capabilities across your entire edge fleet with less rework and one unified system of record.
Purpose-built AI capabilities for the intelligent edge
Move faster from prototype to device deployment. Moxo AI replaces fragmented development with end-to-end edge pipelines built for complex hardware configurations, consumer electronics, and cross-team delivery.
Accurate model selection for constrained edge hardware
Pre-built edge pipeline templates let teams configure models using predefined hardware constraints, power budgets, and inference benchmarks. Ensure accurate on-device performance on every deployment, even for complex multi-sensor use cases.
Built-in governance processes auto-route models for hardware validation, so your AI team can move fast without skipping critical performance checks.
Automated edge deployment and hardware validation workflows
With flexible, rule-based deployment workflows, teams can set hardware validation criteria once and let standard edge models auto-deploy. Non-standard configurations novel architectures, sensitive environments, or custom silicon are routed to Engineering or Compliance.
Streamline device delivery. Minimize delays without sacrificing hardware governance.
System accuracy down to the on-device inference layer
Model versions, on-device inference outputs, and hardware performance benchmarks from each deployment sync directly into your monitoring stack. This enables better visibility, cleaner telemetry, and easier identification of retraining and hardware upgrade opportunities post-launch.
Simplify edge AI delivery and cross-team hardware workflows
Eliminate manual steps with native edge deployment pipelines, real-time telemetry sync, and unified workspaces that keep hardware and software teams aligned.
Pre-validated hardware configs and AI compliance rules
With built-in policy libraries and rule-based customization, teams maintain compliance and ensure accurate edge configurations without slowing device delivery.
Collaborate and review hardware + software in one workspace
Remove the friction of disconnected tools. Empower better teamwork on every edge deployment.
Edge AI infrastructure that drives measurable real-world ROI
Drive faster device deployments, cleaner telemetry, and tighter operational alignment at scale.
Faster time to device deployment
Hardware and software teams spend less time on manual integration and sign-offs, accelerating delivery cycles and increasing success rates as systems move from prototype to live edge deployment in a fraction of the time.
On-device inference accuracy from day one
With hardware configurations, model specs, and inference parameters validated before device deployment, there's no risk of mismatched outputs, degraded on-device performance, or silent system failures.
Fewer revision cycles, more hardware control
Compliance and program leads don't need to inspect every inference run. With logic-based validation and standard hardware policy injection, they focus only on edge cases and exceptions.
Clean telemetry for confident edge AI planning
Real-time device telemetry ensures your system status reflects actual hardware running in the wild not estimates so engineering and leadership can trust the data and plan for scale.
Stronger retraining and hardware expansion strategy
Because deployment data is structured and searchable, teams can analyze on-device model performance across clients and environments, surfacing retraining, hardware upgrade, and fleet expansion opportunities with less effort.
Alignment across hardware and software teams
Hardware Engineering, AI Science, and Program Management all work from the same system, same data, and same version of the truth. No handoff gaps, no data silos just streamlined collaboration that supports device-scale growth.
Enterprise-grade security for mission-critical edge systems
Keep AI operations, device data, and on-device systems safe with enterprise-grade security. Data processed on laptops, phones, and electronics never leaves the device unless explicitly authorized reducing attack surface and ensuring compliance with the most stringent government and commercial standards.
- SOC 1 & 2
- ISO 27001
- FedRAMP Ready
- NIST AI RMF
What our clients say
-
3+ HOURS SAVED
PER DEPLOYMENT”Instantly powerful. Incredible ease of integration. Everything's there and you're confident in the outputs. Turns complex AI workflows into one coherent platform.”
Taylor McPherson
Former Business Ops Specialist Adeptia
-
5 MINUTES TO
CONFIGURE PIPELINES”I can literally log in, configure a new model pipeline in minutes, and get the analytics I need. It's a great tool to surface the insights you need. Recommend!”
Ron Jacobson
CEO Ronbox
Recognized for edge AI deployments and hardware-software innovation
Moxo AI FAQs
Moxo AI replaces fragmented edge AI development siloed hardware specs, manual model-to-device handoffs, and post-deployment data entry into monitoring systems. It acts as a unified platform connecting hardware architecture, model training, edge validation, deployment, and telemetry sync.
Moxo AI is built for the kind of complex edge AI deployments common in government and commercial programs including multi-sensor pipelines, fine-tuned on-device models, and custom silicon inference. Logic-driven templates make it easy to configure accurate edge deployments even for demanding or multi-device programs.
Very customizable. You can define hardware constraints, model parameters, power budgets, and validation logic to match your edge delivery workflow. Standard edge pipelines can auto-deploy, while non-standard ones follow a flexible routing process for Hardware Engineering, Compliance, or other stakeholders.
Once deployed, the full system configuration including model version, hardware specs, and performance baselines syncs automatically to your monitoring platform and operational dashboards. No manual re-entry, which reduces errors and ensures field behavior aligns with what was validated.
As engineers configure edge pipelines, Moxo AI syncs system data in real time to your program dashboards. That means your roadmaps are based on real, in-progress deployments not estimates leading to greater confidence in delivery health, hardware capacity planning, and product readiness.
Moxo AI is a powerful complement that improves everything before and after deployment: hardware system design, model training for edge constraints, validation, and operational integration. It eliminates engineering bottlenecks, speeds up device delivery, and ensures clean hardware-software handoffs so intelligent edge systems perform faster and more accurately.
Yes. Moxo AI shortens the concept-to-device process by automating edge pipeline stages, syncing hardware specs with your infrastructure and monitoring tools, and reducing the need for manual handoffs between hardware and software teams.
The result: faster device delivery, fewer hardware errors, and a better AI experience for your users and stakeholders.
Onboarding is fast whether you're starting fresh or expanding an existing edge program. Moxo AI builds on your existing hardware infrastructure and system integrations, so there's minimal disruption. Most teams go live within a few weeks, and the platform is intuitive, making adoption easy across hardware engineering and program teams.
Explore the Moxo AI intelligent edge platform for government & commercial clients
-
Edge ML pipeline automation
Explore edge ML pipeline automationStreamline every step of your on-device model lifecycle, from data ingestion and training to edge evaluation and hardware-optimized retraining.
-
Hardware and software system integration
Explore hardware integrationConnect AI models to custom hardware with integrations built for the complexity of government and enterprise edge environments.
-
Edge AI analytics and telemetry
Explore edge analytics and telemetryNever lose sight of your on-device model performance with accurate, actionable edge metrics and real-time deployment dashboards.