Physical AI needs evidence outside the robot.

The existing stack was not built to be a witness.

• Robot logs are useful, but they are self-reports. They describe its internal state: what it thought it saw, what it intended to do, what action it issued, what exception it raised.
• Video is useful, but raw footage is not a record. It still leaves people scrubbing timelines, comparing clips against logs, and reconstructing the sequence by hand.
• Point sensors are useful, but they see narrow channels. They do not cover every contact interface, every object, every failure mode, or every system that may enter a shared environment.

As physical AI becomes more heterogeneous, this gap gets worse. Many robot vendors, models, integrators, and controllers will operate across the same sites. The people carrying the risk need evidence that does not come only from the system being measured.

Haptica reads physical outcomes from video.

Haptica turns existing cameras into physical-event sensors. We read events such as contact, impact, rotation, and bind from standard video, then convert them into structured evidence packets: what happened, when it happened, how confident the system is, when it abstained, and which clip supports the verdict.

The goal is not to speak for the robot. The goal is to create an independent account of the physical outcome, so operators, integrators, model teams, auditors, and insurers can reason from the same record.

A record is only as good as its distance from the thing it measures.

Physical AI needs observability that sits outside the control loop.

If a robot claims a task succeeded, there should be a separate way to verify the physical outcome. If a model rollout creates more false successes, there should be a way to find them. If an exception happens every shift, there should be a reusable record of the sequence, not another manual investigation from scratch.

Haptica reports what the video supports. When the evidence is weak, it abstains. When it makes a verdict, it keeps the provenance attached.

That distance is the point.

One record, many readers.

• Operators use Haptica to understand what happened on the floor.
• Integrators use it to debug deployments across vendors, robots, and sites.
• Model teams use it to evaluate rollouts, find false successes, and improve training data.
• Auditors and insurers use it as physical evidence for systems that increasingly act in the real world.

Each group needs a different workflow. They should not need a different truth.

Why we can build this.

Haptica is built by a team that has spent its careers turning frontier technology into systems that enterprises and governments actually run.

Between the founders, the track record includes a product now part of Microsoft Azure, the platform behind a top US newspaper, AI shipped into defense and federal programs, and a Global Public Sector CTO role at a hyperscale data company. We have founded and exited enterprise software companies, built data infrastructure at national scale, and contributed to open-source large language models years before they were household names.

The collective record runs to nine company exits, more than ten patents across computer vision, machine learning, haptics, and streaming, federal research grants and SBIR funding, and engineering teams of more than two hundred. The backgrounds reach from West Point and Stanford to hyperscale data platforms, with advisory roles at NIST and the World Economic Forum, and two of the founders co-authored a Forbes book on operationalizing AI inside the enterprise.

We are still heads-down, so names and faces come later. What matters now is the shape of the team: invention, production-grade engineering, enterprise commercialization, and deployment in the places where performance, auditability, and trust are the whole point.