The operating system for physical AI to accelerate
product development.
The intelligence layer
that sits beneath your engineering
workflows.
what it is
Most organisations already have the data required to create meaningful AI outcomes. vetta builds company-specific intelligence from your historic data, creating an intelligence layer.
vetta is structured in three layers. Each has a specific technical function. Together they form a closed system that compounds in value with every deployment.
Engineering data is structurally messy.
Most AI vendors skip this work.
Engineering data is structurally messy. Most AI vendors skip
this work.
the foundation
vetta ingests raw engineering data — sensor streams,
test logs,simulation outputs, CAD metadata — and produces
model-ready inputs. Harmonisation, quality checks, deduplication,
vectorisation. Solving this correctly is the prerequisite for
everything above it.
data pipeline
Running domain-specific physical AI models
is an operational engineering problem,
not a research one.
Running domain-specific
AI models is an
operational engineering
problem, not a research one.
the engine
vetta hosts and orchestrates physics-informed foundation models
trained with physical laws as constraints — electrochemistry,
thermodynamics, structural mechanics. General LLMs do not speak the
engineering language and do not leverage the historic data. vetta
models do. Compute routing, inference scheduling, model versioning,
efficient storage of outputs.
model orchestration
This is where business
value is visible.
This is where business
value is visible.
the interface
Domain-specific agent workflows and interfaces trained on your historic
engineering decisions that surface model outputs as actionable decisions —
SOH diagnostics, ECM parametrisation, test planning, predictive maintenance.
Embedded directly into your existing engineering workflows. Not another
dashboard. Intelligence where decisions are made.
agent workflow interface
CAD designFEM simulationtestingvalidationproductioncost calculationcustomer requirementsCAD designAn organisation that only builds
applications on top of rented AI has
no flywheel — it has a feature
The three layers are mutually reinforcing. The Application layer generates proprietary engineering data. That data flows back into the Data and Modelling layers, improving the foundation models. Better models make the Application layer more accurate and more trusted.
the vetta flywheel
how we deploy
A permanent operating relationship.
A permanent operating relationship.
deployed in your
infrastructure
designs and deploys vetta inside your infrastructure — data pipeline, foundation model stack, application layer workflows. Built to be owned by you from day one.
duration: 60–180 days
running alongside
your team
Sphere runs and optimises the platform — monitoring, model updates, capability additions. Your team builds confidence alongside us.
duration: 12–36 months
long-term operating
partner
You own the infrastructure. We run the intelligence layer. Model governance, continuous improvement, new capability additions.
duration: indefinite