Physical AI: NVIDIA Halos and Hailo

Research note. Reviewed 8 November 2026. Vendor documentation establishes the scope of the named platforms; the service recommendations below are Beeston’s proposed commercial approach.

Two similar-sounding names refer to different things. NVIDIA Halos is NVIDIA’s physical AI safety system and associated ecosystem. Hailo is a separate company producing edge AI accelerators and vision processors. They can arise in the same engineering discussion, but they are not one product or programme.

NVIDIA Halos

NVIDIA describes Halos as spanning physical AI safety technology, evaluation and an inspection ecosystem. Its robotics material connects hardware, system software, sensor integration and supporting evidence. NVIDIA Halos overview and robotics technical explanation.

The published process distinguishes inspection of a Halos integration from subsequent third-party system certification. For an advisory engagement, this means establishing the applicable route and preparing the evidence required for that route, rather than promising that a general review confers certification. NVIDIA inspection process.

NVIDIA’s Halos SAFE technical brief focuses on evidence for autonomous-vehicle safety cases. It can inform understanding of evaluation approaches, but should not be presented as a universal test protocol for all physical AI applications. Halos SAFE introduction.

Hailo

Hailo’s portfolio includes AI accelerators and AI vision processors. The accurate general description for our copy is edge AI accelerators and vision processors, rather than describing the company simply as a GPU manufacturer. Hailo product portfolio.

Its software suite includes a model-build environment and runtime components, including the Dataflow Compiler, HailoRT and Model Zoo. Compatibility needs to be checked for the selected device, model and software release; the current Model Zoo documentation distinguishes branches for different hardware families. Hailo software suite and official Model Zoo repository.

What a useful consulting offer could cover

Readiness for a physical AI trial. Define the intended operation, people exposed to the system, important failure scenarios, operating limits and responsibilities. Identify the evidence needed before the proposed trial.

Platform feasibility. Evaluate a NVIDIA or Hailo configuration against an actual workload. Examine model support, conversion effects, host integration, sustained performance and operational dependencies.

Evidence preparation. Organise requirements, assumptions, tests, limitations and open issues for a release review or specialist assessment. For a Halos-related project, begin by checking programme applicability and the evidence route.

These are proposed service scopes, not claims that Beeston is part of either vendor’s partner or certification ecosystem.

Questions that make the work concrete

• Is the AI advising a person, controlling an action or contributing to a safety function?
• Which operating conditions are within scope, and how is an out-of-scope condition handled?
• What happens when a sensor fails, a result is delayed or the model is uncertain?
• Which safety functions and safeguards sit outside the AI model, and how are they verified?
• Which model, host, accelerator, runtime and configuration produced the evaluation result?
• Who decides whether the evidence supports the proposed trial or release?

How to describe the offer publicly

“We help teams prepare physical AI systems for informed deployment decisions, connecting governance, platform evaluation and safety evidence. Our work includes NVIDIA and Hailo platforms, with advisory support for teams exploring NVIDIA Halos.”

Use the detailed capability page for the deliverables. Keep the homepage broad and the four service groups intact. Review vendor programme and toolchain details when scoping each project, since eligibility, access and supported configurations can change.