SPARC LABS

Building the Spatial Foundation Model for the Real World

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The next generation of AI will not just read, write, or watch the world. It will understand, model, and simulate space itself.

S p a r c _ A I

At Sparc Labs, we are building SPARC – the Spatial AI Platform for Real-World Construction: a foundation model and platform that learns a persistent, metric, and semantic understanding of the physical world in 4D (space + time).

Not as a demo. Not as a dataset. But as real infrastructure.

The problem

Today’s AI systems are fragmented
Language models understand text, but not space.

Video models generate pixels, but lack geometry and persistence.

3D systems reconstruct scenes, but don’t reason, predict, or generalize.

Most systems

Operate on static snapshots, Rely on small, frozen datasets, and break when the world changes.

But the real world is spatial, dynamic, and evolving.

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Our thesis

Spatial intelligence requires an explicit world model.

We believe the winning systems will be built around a persistent 4D world representation that is

  • Explicitly spatial, metric, and semantically grounded
  • Consistent and persistent over time
  • Generative and predictive by design
  • Continuously updated from real-world use
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What we are building?

SPARC is a Spatial Foundation Model and platform that unifies:
3D / 4D reconstruction (geometry and structure),
Open-vocabulary semantics and language grounding
Temporal dynamics and change modeling
Generative world simulation

into a single, queryable world model.
Downstream tasks are not separate pipelines. They are queries on the world model.

Why we can build it - our unfair advantage

Productive software as a data engine
We already operate productive spatial software used in real workflows.
Users scan environments, upload spatial data, and continuously update real-world scenes.
This creates a living data loop:
  • real users generate real 3D/4D data
  • across time, versions, and conditions
  • without manual labeling.

Training signals emerge naturally from multi-view consistency, temporal alignment, change detection, and real usage patterns.

Digitized spatial archives at scale
In parallel, we are digitizing entire spatial archives — historically grown datasets of buildings and environments.
This gives us:
  • long temporal depth,
  • rare long-tail structures,
  • real-world variability

that simply does not exist in public datasets.

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We already have and continuously generate enough data to train a true Spatial Foundation Model.

Backing

Spark Labs is backed by strong investor conviction.
  • We have soft commitments and clear signals for funding worth of
€ 0

M+

Contingent on assembling the core technical leadership.

Compute, data, and hiring resources are not the constraint.
Defining the right abstraction is.

Who we are looking for​

Founding AI Lead /
Chief Scientist

We are looking for someone who:
has deep experience in 3D / 4D representation learning
understands generative and world models
cares about explicit spatial structure, not just pixels
wants to own a technical vision end-to-end
and is excited to build something foundational.

This is a founder-level role, not a management title.

You will:
define the model architecture and learning objectives
shape the data and evaluation strategy,
build and attract a world-class research team,
and set the technical direction of SPARC.

Why now?

All the ingredients are finally here
scalable 3D representations
scalable 3D representations
powerful generative models,
real-world data at scale
and the compute to train foundation models.
and the compute to train foundation models.
But no one has yet defined the right spatial abstraction. We intend to.

Let’s talk
If you care about spatial intelligence, about building models that understand the real world, and about defining a new layer of AI infrastructure.
we should talk.​

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