Physical AI data infrastructure

The industrial data layer
for Physical AI.

Tyvori helps robotics teams acquire the real-world physical data their models can't easily get on their own. We source industrial environments, capture skilled tasks, failures and recovery behavior, and turn them into model-ready datasets.

Custom data acquisition for robotics, manipulation, humanoids, embodied AI, and industrial automation.

Acquisition pipeline

live

  1. 01

    Industrial Environment

  2. 02

    Skilled Human Task

  3. 03

    Multimodal Capture

  4. 04

    Task Segmentation

  5. 05

    Edge Cases + Recovery

  6. 06

    Structured Dataset

  7. 07

    Robotics Model

You build the intelligence. We supply the experience.

Tell us what physical skill your model needs to learn. Tyvori handles the physical-world data collection required to teach it.

The bottleneck

Physical AI has a data problem.

The internet gave language models billions of examples. Robots do not have that luxury.

The most valuable physical experiences happen inside factories, warehouses, workshops, service environments, and other real-world settings that are difficult to access and expensive to recreate.

01

Hard to access

The right task may only exist inside specific industrial environments.

02

Expensive to reproduce

Building internal collection operations takes facilities, workers, equipment, sensors, permissions, and time.

03

Generic data is not enough

Deployment failures often come from rare situations, edge cases, task variation, and recovery behavior.

The bottleneck is not just more data. It is the right physical experience.

How it works

Give us the task. We handle the physical world.

STEP 01

Define the skill

The robotics team tells Tyvori what physical behavior, environment, failure mode, or task distribution the model needs.

STEP 02

Source the environment

Tyvori identifies and coordinates access to relevant factories, warehouses, industrial facilities, operators, and physical workflows.

STEP 03

Design the collection

Define operators, objects, variation, sensors, success criteria, failures, metadata, and acceptance requirements.

STEP 04

Capture real-world behavior

Collect demonstrations in realistic environments using the required camera and sensor configuration.

STEP 05

Structure and validate

Segment, synchronize, annotate, classify outcomes, identify failure and recovery sequences, and perform QA.

STEP 06

Deliver model-ready data

Deliver structured data aligned to the robotics team's training and evaluation requirements.

Data programs

Built around the data your model actually needs.

Human demonstrations

Skilled operators performing physical tasks in real environments.

Failure and recovery data

What happens when the expected action does not work—and how humans recover.

Egocentric video

First-person task capture for manipulation and embodied learning.

Multi-camera capture

External viewpoints for object, scene, and task context.

Depth and motion

RGB-D, IMU, pose, trajectory, and movement data where needed.

Force and tactile

Force, contact, grip, tool-state, or tactile signals for more complex manipulation programs.

Task metadata

Structured labels around tools, objects, environments, states, outcomes, and task variations.

Evaluation datasets

Separate real-world datasets for measuring model behavior and generalization.

Task coverage

Built for the physical tasks that matter.

cable routingconnector insertionfasteningassemblytool usemachine tendingpackaginginspectionsortingbin pickingdeformable object handlingparts handlingmaintenance workflowserror recoveryquality checkswarehouse manipulation

Tyvori is not limited to a fixed dataset catalog. Programs are designed around the task your team needs.

Differentiation

Not another generic data vendor.

Generic data providers

  • sell broad collections
  • optimize for volume
  • use staged or loosely controlled environments
  • focus on successful demonstrations
  • deliver predefined formats

Tyvori

  • acquires task-specific physical experience
  • sources relevant industrial environments
  • designs around model requirements
  • captures failures and recovery behavior
  • supports custom sensor configurations
  • delivers structured, model-ready outputs

We are building access to the physical world as infrastructure.

Failure & recovery

Success teaches the task. Failure teaches deployment.

Robots do not fail only because they have never seen the correct action. They fail because the world changes.

Objects slip.

Parts arrive misaligned.

Threads do not catch.

Connectors refuse to seat.

Tools jam.

Materials deform.

Workers recover from these situations instinctively. Tyvori captures those moments too.

Task timeline

episode / 00:00–00:42

Attempt

t00

Failure

t01

Detection

t02

Adjustment

t03

Retry

t04

Recovery

t05

Success

t06

Pilot program

Start with one hard physical skill.

Tyvori's pilot programs are designed to test whether external physical-data acquisition can accelerate your robotics program.

No large platform commitment. Start with the data bottleneck your team already has.

Suggested structure

Tyvori Custom Task Data Pilot

Includes

  • one target task
  • agreed environment requirements
  • multiple operators
  • defined task variation
  • success and failure examples
  • requested camera or sensor configuration
  • task segmentation
  • structured metadata
  • QA and acceptance criteria
  • model-ready delivery

Industrial network

A growing network of real production environments.

Tyvori is building relationships with manufacturers, suppliers, warehouses, machine shops, service environments, and industrial operators to make hard-to-access physical workflows available for responsible AI data programs.

DetroitMidwestUnited StatesGlobal
DetroitMidwestUnited StatesGlobalDetroit

Long-term vision

From datasets to physical intelligence infrastructure.

Over time, Tyvori is building a structured representation of industrial skills:

TaskObjectsToolsEnvironmentInitial StateActionsFailure ModesRecoveryOutcome

The concept

The Tyvori Industrial Skill Graph

A growing map of how real physical work is performed, varied, interrupted, and recovered—built for the next generation of Physical AI.

Long-term direction — not a currently complete product.

Start the conversation

What physical data is hardest for your team to acquire?

If your robotics team has a physical task, environment, edge case, or failure mode that is difficult to collect internally, tell us what you need.

Tell Tyvori what you need

Reviewed by the Tyvori program team.