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Physical AI & Computer Vision Data Services

End-to-end image, video, and sensor data services for training and evaluating Physical AI and computer vision models.

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Training Physical AI Models at Scale with Speed and Precision

Modern AI systems do more than process text. They perceive and interact with the physical world. Robotics, autonomous systems, agriculture, and geospatial AI all depend on high-quality image, video, and sensor data delivered quickly.

Physical AI requires speed, precision, and scalable data infrastructure.

In practice, teams need large volumes of accurately labeled data they can iterate on as models evolve.

Innodata delivers end-to-end image, video, and sensor data services that support rapid retraining, rigorous evaluation, and reliable deployment at scale.

What We Do

Innodata helps AI teams turn raw visual data into production-ready training datasets.

You Give Us

Image stack icon representing computer vision data collection and video labeling.

Images

Raw imagery from cameras, satellites, microscopes, or devices

Video

Footage from cameras, drones, dashcams, or any feed

Sensor icon representing synchronized IMU, depth, RGB, and LiDAR data capture.

Sensor Data

(RGB, IR, LiDAR, egocentric, aerial, HSI, etc.)

Requirements

Requirements

for custom data collection when off-the-shelf datasets do not exist.

We Deliver

  • Precisely annotated frames based on your ontology and performance specifications
  • Custom keypoints and skeletal models
  • Quality-controlled labels ready for model training
  • Fast, scalable turnaround, even at massive volume
  • Production-ready datasets designed for real-world Physical AI use cases
We Deliver
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How We Ensure Speed and Quality:

  • Human-in-the-loop workflows combining automation and expert review.
  • Proprietary quality controls designed for motion-heavy and real-world data.
  • Turnaround measured in hours, even at large scale

In addition to annotation, we also support custom image, video, and sensor data collection. We work with partners and internal teams to source and capture domain-specific datasets, enabling customers to train models on exactly the scenarios they care about.

Core Capabilities

Image, Video & Sensor Annotation

We support complex computer vision tasks across people, animals, objects, and environments:

  • Object detection & tracking
  • Keypoint & skeletal extraction (custom skeletons)
  • Action & motion labeling
  • Small-object detection
  • Egocentric (first-person) video labeling
  • Geospatial & aerial imagery annotation

Our teams routinely process thousands of frames per night, turning around labeled datasets in hours, not weeks.

Tanner, F., “Evaluating prompt design choices for object detection, counting, and classification in overhead imagery,” In Proceedings of SPIE, Pattern Recognition and Prediction XXXVII, Vol 14032. 2026

Motion Analytics & Kinematic Modeling

Motion-aware analytics are a core part of our quality control approach for Physical AI. Beyond basic labeling, we analyze how things move.

Using keypoint motion and kinematic modeling, we:

  • Track skeletal movement across frames
  • Identify abrupt or impossible motion patterns
  • Automatically flag potential labeling anomalies
  • Improve dataset consistency before training begins

By detecting issues before training, this approach reduces downstream model failure risk and improves overall model reliability.

Wikimedia Commons: https://commons.wikimedia.org/wiki/File:Bench_press_-_exercise_demonstration_video.webm

Human‑in‑the‑Loop Automation

Speed doesn’t come at the cost of quality.

We combine:

  • Pre‑labeling models
  • Custom automation pipelines
  • Expert human annotators

This hybrid approach dramatically reduces annotation time while maintaining the precision data scientists expect for training and evaluation.

Video source: https://www.youtube.com/watch?v=2ldTdzNi4nk

Egocentric & Robotics‑Ready Data

We treat egocentric and robotics-focused data as a core capability, not a niche use case. As robotics and embodied AI adoption accelerates, demand for egocentric (first‑person) video labeling is growing fast.

We support:

  • Egocentric object detection & tracking in 2d and 3d across multiple modalities
  • Custom labeling for robotic workflows including affordances, intent prediction, and magnitude estimation
  • Robotics‑focused evaluation workflows

These datasets directly support manipulation, navigation, and real-world interaction models.

D. Damen et al., “EPIC-KITCHENS-100: A Large-Scale Dataset for Egocentric Action Recognition,” International Journal of Computer Vision (IJCV), vol. 130, no. 1, pp. 33–55, 2022.

Labeling Based on Target Ontology

What Are You Trying to Do?

Activity Annotation Ontology diagram showing object–state–action flow, with category lists, example triples, and hierarchical taxonomy below.

Similar pile of clothing, different state, different action space.

Wash the laundry?

Wicker laundry basket full of colorful clothes sits on the floor, with a washer and dryer behind it and detergent bottles on top.

Put laundry in hamper?

Pile of clothes on a tiled bathroom floor, with a toilet and sink cabinet nearby and a bathtub in the background.

Put laundry away?

Stack of folded towels on a neatly made bed, with white pillows and a beige bedside lamp and table in the background.

Yell at the kids?

Pile of clothes on a wooden hallway floor beside a stair railing with jeans hanging over the banister and a door ahead.

Why Innodata

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Quality at Speed. Our Care Advantage.

Without automation, a single annotated video can take hours.

Innodata delivers:

  • Thousands of frames processed per night
  • Turnaround measured in hours
  • Global teams ready to scale on demand

This speed enables faster experimentation, faster retraining, and faster deployment.

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Built for Scale

  • Thousands of trained annotators across the Philippines, India, Kenya, Sri Lanka, and beyond
  • Rapid team ramp‑up
  • Proven ability to support large, continuous annotation campaigns

Whether you’re labeling a pilot dataset or retraining models nightly, we scale with you.

Metallic document icon with flowchart boxes, next to a shiny pen, shown on a dark background with reflective highlights.

Proprietary Quality Controls

Our quality approach goes beyond spot checks:

  • Multi‑layer QA workflows
  • Model-based anomaly detection
  • Continuous feedback loops

The result: datasets that data scientists trust, even for the most complex physical AI use cases.

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35+ Years, Trusted by Leading AI Builders

Innodata supports some of the world’s most advanced AI teams, including leading hyperscalers and frontier AI organizations, across high-volume training data, evaluation, and quality assurance workloads.

Let’s Build Physical AI Together

Whether you’re training a new vision model or scaling production pipelines, Innodata helps you move faster with data you can trust.

Talk to us about designing and delivering a Physical AI data pipeline tailored to your models and deployment goals.

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