Mosaic Raises $47M for Spatial Intelligence in Surgery

By Amara Osei

Mosaic Intelligence Labs emerged from stealth with $47 million in seed funding on November 10, 2026, to develop sensing-enabled spatial intelligence and robotic systems for surgery. The San Francisco company named 8VC, Fundomo, Lingotto Innovation and Obvious Ventures as backers.

Chief executive Ileana Pirozzi and chief technology officer Alison “Ali” Kight Mackanic founded Mosaic after meeting during their doctoral studies at Stanford. Mosaic said their work alongside clinicians exposed how difficult less-invasive procedures can remain when critical knowledge stays with individual operators.

Pirozzi said: “We kept returning to one question: what would it take to scale the knowledge of the very best surgeons?” She added: “We believe that starts with making our machines aware, and turning surgical repetition into experience we can learn from.”

Why start with endovascular procedures?

Mosaic’s first target is endovascular care, in which physicians thread flexible wires and catheters through blood vessels to reach treatment sites. The company describes blood vessels as routes to virtually every organ, yet operators have no direct line of sight inside them.

Physicians determine their position and what lies ahead through live X-ray imaging, preoperative scans and the feel of their instruments. Mosaic identifies radiation exposure and outcomes that vary with operator experience as limitations of that workflow.

According to Mosaic, interpreting those signals can take years of practice, and much of the resulting knowledge is hard to record or hand down. The company says surgical access has become less invasive while navigation still depends heavily on the perception and judgment of the person holding the instrument.

Mosaic’s website compares a 20–30 cm incision for open surgery and 1–2 cm ports for keyhole surgery with a 2 mm needle puncture for endovascular surgery. It lists these current endovascular applications:

• Stopping strokes
• Clearing blocked coronary arteries
• Repairing aneurysms
• Restoring blood flow to limbs

Mosaic says the range makes vascular navigation a reusable capability across multiple procedures.

How does Mosaic plan to learn from surgery?

Mosaic intends to instrument the point where surgical tools meet anatomy. It plans to combine sensor readings with computation that reconstructs the vascular environment from within, maintaining a three-dimensional picture of anatomy and instrument position as it changes.

A robotic interface would support precise instrument control. Mosaic’s learning architecture would record the environment, instrument movement and physician actions as synchronized streams.

Physical models would help the system relate what it senses to what happens when it moves. Mosaic connects this design to a world model, which represents an environment and helps a machine predict how its actions will change it.

Mosaic supplied no patient-outcome findings or clinical-performance results with the announcement. It describes every major capability as a development goal.

The nearer objective is to assist physicians’ perception and control, Mosaic says. Recorded procedures are intended to become a source of cumulative machine learning for machines over time.

What must Mosaic prove next?

Mosaic said it is recruiting engineers, physicians and creatives in San Francisco. Its open roles cover:

• Hardware engineering
• Sensing
• Perception
• Machine learning
• Clinical engineering

The breadth matches an effort spanning physical instruments, software, robotics and clinical practice, Mosaic said.

Mosaic’s longer-term goal is to widen treatments delivered through vascular routes. The company says catheters can deliver several therapies directly to a target site:

• Drugs
• Energy
• Biologics
• Other therapies

More reliable navigation could expand both the range of treatments delivered through blood vessels and the settings in which clinicians can offer them, Mosaic said.

Dependable navigation must work in individual procedures before those procedures can supply a useful foundation for cumulative machine learning, according to the company. That leaves Mosaic to improve navigation and physician control first, then use recorded procedures to support broader vascular treatment.