TELEFOLLOW

Real-World Data
for Physical AI

Human demonstrations. Real environments.
Custom data collection across Latin America.

Discuss a Data Collection Project Explore Our Capabilities

Built for robotics and Physical AI teams

Robotics Companies

Train robots with real human demonstrations.

AI Labs

Improve generalization with diverse real-world data.

Data Teams

High quality, structured and traceable datasets.

Global Impact

Underrepresented environments for stronger models.

The physical world can’t be scraped. It has to be captured.

The Data Gap

AI can learn from the internet. Robots need the physical world.

Physical AI requires more than digital information. Robots need to understand how humans move, manipulate objects, use tools and interact with real environments.

We capture that world.

The internet documents what we know.

The physical world shows how we act.

Telefollow turns those actions into training data.

What We Collect

Human behavior, captured for machines.

From simple everyday actions to complex professional workflows, Telefollow captures real-world human demonstrations according to your training requirements.

Your tasks. Your protocol. Real-world execution.

01 — Egocentric Video

See the world from the human perspective.

First-person video capturing hands, objects, tools and environments while people perform real tasks.

  • POV
  • Hands
  • Objects
  • Tools

02 — Human Manipulation

Capture how humans interact with objects.

Demonstrations involving grasping, moving, opening, assembling, sorting, preparing and using physical objects.

  • Grasp
  • Move
  • Use
  • Interact

03 — Motion & Sensor Data

Add synchronized physical context.

When required, collection protocols can include synchronized motion and sensor information alongside video.

  • IMU
  • Motion
  • Time
  • Sensor

04 — Metadata & QA

Structured, traceable and validated.

Collection data can include task, participant, environment and timing metadata together with project-specific quality validation.

  • Task
  • Environment
  • Time
  • QA

Real-World Environments

The world is our data environment.

Physical intelligence needs diversity. Telefollow sources participants and operating environments according to the tasks your models need to learn.

From everyday homes to specialized workplaces.

Egocentric view of hands cooking in a home kitchen

ENV / HOME

Homes

Cooking · Cleaning · Laundry · Everyday Tasks

Cook preparing food in a commercial kitchen

ENV / FOOD

Food Service

Commercial Kitchens · Restaurants · Coffee Shops · Food Preparation

Worker handling boxes in a warehouse

ENV / LOGISTICS

Logistics

Picking · Packing · Sorting · Inventory Handling

Person restocking products on a store shelf

ENV / RETAIL

Retail

Shelving · Restocking · Product Handling · Store Operations

Hands using tools during workshop repair work

ENV / WORKSHOP

Skilled Work

Tools · Maintenance · Assembly · Repair Workflows

Hands harvesting produce in an agricultural field

ENV / FIELD

Agriculture

Crops · Tools · Handling · Field Operations

How It Works

You define the data. We build the collection.

Every Physical AI program is different. Instead of offering a fixed dataset, Telefollow builds each collection operation around your requirements.

From specification to real-world execution.

  1. 01

    Requirements

    Tell us what your models need.

    Define the tasks, environments, viewpoints, sensors, volume and collection protocol required by your training program.

    • Tasks
    • Environment
    • Sensors
    • Volume

  2. 02

    Sourcing

    We build the real-world network.

    Telefollow sources the participants, businesses and environments required to execute the collection.

    • People
    • Businesses
    • Locations

  3. 03

    Collection

    Real people perform real tasks.

    Participants execute defined tasks following the project's collection protocol and capture requirements.

    • Video
    • Action
    • Motion
    • Sensor

  4. 04

    Quality Control

    Every collection is validated.

    Captured data is checked against project-specific requirements before being prepared for delivery.

    • Validation
    • Metadata
    • QA

  5. 05

    Delivery

    Data prepared for your pipeline.

    Validated collection data and associated metadata are structured according to the agreed delivery specification.

    • Dataset
    • Metadata
    • Delivery

Start with one task.

A collection program doesn't need to start at scale. We can begin with a controlled pilot, validate the data and operational process, and expand from there.

1 Task → 1 Environment → Controlled Pilot → Validate → Scale

Why Latin America

Different worlds create stronger models.

Physical AI must operate beyond controlled environments. Latin America offers access to diverse people, objects, workflows and physical environments that can expand the real-world distribution represented in training data.

More diversity. More environments. More ways humans interact with the physical world.

One model → Many realities

01 — Diverse Environments

More variations of the physical world.

Homes, businesses, workshops, farms and urban environments introduce different layouts, objects and operating conditions.

  • Layouts
  • Objects
  • Conditions

02 — Different Workflows

Humans solve the same task differently.

Cultural, operational and environmental differences create variations in how people manipulate objects, use tools and complete everyday tasks.

  • Actions
  • Tools
  • Workflows

03 — Underrepresented Data

Go beyond familiar data distributions.

Expanding collection into new regions can introduce environments and behaviors that may be less represented in existing training datasets.

  • Diversity
  • Generalization
  • Real World

04 — Operational Efficiency

Collect more efficiently.

Latin America can provide competitive operating conditions for human data collection while maintaining project-specific quality requirements.

  • Scale
  • Efficiency
  • QA

Starting point: Ecuador

Expansion: based on project requirements

Start in Ecuador. Scale when the data proves valuable.

Start with a Pilot

Give us one task. We'll build the collection.

Start with a controlled data collection pilot in Ecuador, built around your tasks, environments and technical requirements.

Your specification. Our operation. Real-world data.

  1. Your requirement
  2. 1 Task
  3. 1 Environment
  4. Controlled collection
  5. Validate
  6. Scale

No predefined dataset. No unnecessary scale. We start by proving that the collection produces the data your team needs.

Pilot Objectives

Test the data before scaling the operation.

  1. 01 — Data Quality

    Does the captured data meet your training requirements?

  2. 02 — Collection Protocol

    Can the required tasks be captured consistently?

  3. 03 — Environment

    Does the selected real-world environment provide useful variation?

  4. 04 — Operation

    Can the collection process be executed reliably and expanded?

Validate first. Scale second.

Discuss a Collection

What data are you looking for?

Tell us what your models need. We'll evaluate how Telefollow could build the collection.

No commitment. Start with a conversation.

Real-world data starts with a real-world task

What should your models learn next?

Discuss a Pilot

Starting in Ecuador · Built to expand across Latin America based on project requirements