HOW IT WORKS

Authentic Data. Zero Friction.Built Into the Equipment Workers Already Trust.

Most physical AI training data is collected in labs, on volunteers, or generated synthetically. It looks nothing like the real world. Harness Robotics fixes that by capturing data exactly where skilled work actually happens.

01

STEP 01

Embed capture in existing PPE

We integrate camera and sensor hardware directly into standard hard hats. Workers wear exactly what they already wear on site. Nothing changes about their job. Nothing changes about their behavior. That authenticity is the product.

02

STEP 02

Capture egocentric video and motion data

Two cameras on each unit capture what the worker sees (first-person perspective) and how their upper body moves throughout the task. This dual-view approach gives AI training pipelines both the visual context and the physical motion data they need — without requiring full-body motion capture suits.

03

STEP 03

Process and validate data quality

Raw footage is processed using computer vision and pose estimation tools to extract structured motion data, validate quality, and filter out unusable captures. Every dataset we deliver meets defined quality benchmarks before it reaches a buyer.

04

STEP 04

Deliver ready-to-use training datasets

Buyers receive labeled, structured datasets in formats compatible with their training pipelines. We handle collection, processing, quality control, and licensing. You get data that is ready to use.

WHAT MAKES THE DATA DIFFERENT

AUTHENTIC

Collected during real work, not staged scenarios. The environment, tools, conditions, and pressures are exactly what they are on a real job site.

IN-CONTEXT

The environment, tools, and conditions are real. There is no lab setup, no volunteer behavior, and no artificial constraint.

SCALABLE

Passive capture means volume without proportional cost. Every hard hat on every site is a potential data collection point.

EGOCENTRIC

The first-person perspective humanoid robots actually need. Not third-person observation, but the view from inside the task.