Partners/OEMs
For OEMs

Beyond hardware.

Compute-ready isn't enough. Bud Novaria ships pre-integrated inside your devices so they're AI-ready at power-on, evolve over the air, and earn recurring revenue long after the sale.

Become an OEM partner See the economics
The opportunity

AI is moving to the edge

The value is shifting on-device — and the systems that arrive AI-ready capture it.

Edge-AI hardware by 2030
$500B+
A market forming around systems that ship intelligence, not just compute.
Workloads at the edge by 2028
85%
Of enterprise AI runs at the edge or on-device — where your hardware lives.
Margin multiplier
10×
For AI-native systems versus bare hardware sold by the box.

Illustrative; varies by region, workload, and configuration.

The frame

"AI in a Box" — the OS-preinstalled moment

In the 1990s the OS shipped on the machine. AI is at the same moment: compute, the full software stack, security, and OTA — assembled and shipped as one.

01 · Hardware

Compute hardware

Your silicon — CPU, GPU, NPU — tuned and ready at the factory line.

02 · Software

Full software stack

Inference, models, agents, and orchestration pre-integrated, not bolted on.

03 · Security

Security & compliance

In-runtime guardrails and confidential computing, on by default.

04 · Lifecycle

OTA evolution

Devices improve over the air — new models and skills without a hardware swap.

The squeeze

Why "ship & forget" is obsolete

Shipping compute-ready boxes gets harder every cycle. Four forces close in at once.

Complex integration

Every customer rebuilds the AI stack on top of your hardware — slow and fragile.

New attack surface

On-device AI opens an edge attack surface the old supply chain never had.

"Ship & forget" ends

A one-time sale leaves all the recurring value on the table after shipment.

Out-of-box expectations

Buyers now expect AI to work at power-on — not after a six-month project.

The shift

What customers now demand

The questions buyers ask have changed — from specs to outcomes.

"Run our models out of the box?"
"Keep our data private on-device?"
"Improve without replacing hardware?"
"Let our teams build their own AI?"
The math

Traditional OEM vs. AI-native

Same product line. A different relationship — and a different margin.

Metric
Traditional OEM
AI-native OEM · Bud Novaria
What ships
Compute-ready only
AI-ready immediatelyPower-on intelligence
Revenue model
One-time hardware sale
Hardware + recurring licenseRecurring revenue
Gross margin
15–25%
40–60%2–3× expansion
Customer relationship
Support tickets only
Continuous OTA engagement
Device evolution
Static at shipment
Continuous via OTA
Time-to-value
3–6 months
Power on and goInstant
Gross margin
40–60%
Revenue model
Recurring
Time-to-value
Power-on

Illustrative; varies by region, workload, and configuration.

Security, on by default

Protection that ships inside

Bud SENTRY enforces in the runtime, on the device — not as an afterthought in the cloud.

Guardrail latency
<10ms
In-runtime enforcement that doesn't slow the device down.
Confidential computing
TEEs
Intel, NVIDIA, and ARM trusted execution environments.
Prompt-injection
Defended
Multi-layered protection against injection and exfiltration.
Integration path

From silicon to ship & evolve

Bud Novaria folds into your manufacturing line — the customer's integration work disappears.

1
Engineering

Hardware integration

Bud Pod is tuned to your silicon — CPU, GPU, or NPU — with reference firmware and drivers.

2
Manufacturing

Factory pre-loading

The full stack — models, agents, security — is pre-loaded on the line, so devices ship AI-ready.

3
In market

Ship & evolve

Devices power on intelligent and improve over the air — earning recurring revenue post-sale.

Get started with Bud

Put your data on it.

The fastest way to see what an integrated AI operating system does for your enterprise is a proof-of-concept on your infrastructure, with your data.

Request a POC Talk to the team
01 Identify a use case where complexity, cost, or governance is a known pain point.
02 Joint discovery — Bud maps your AI pain points to platform capabilities.
03 POC in days, on your hardware, with your data.