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BotsFeed is a real-world data production platform for AI and robotics teams. We combine structured task design, contributor operations, and quality control to help transform collection demand into usable datasets.

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Real-world data production for AI systems operating in the physical world

BotsFeed helps AI and robotics companies run structured real-world data programs across environments, interactions, and human demonstrations. From workflow design to contributor execution to review and delivery, the platform is built to produce data that is easier to trust and easier to use downstream.

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Real-world dataset coverage
Data Production

Collect real-world data through structured workflows

Workflow and review
Workflow icon

Run a quality-controlled data production system

Connect workflows, contributors, and quality to produce more consistent and usable data.

Quality-driven contributor system

Task design → Review → Accepted dataset
Scalable supply network

Align incentives, workflows, and quality to improve data outcomes.

positioning

Built for data production, not generic crowdsourcing

BotsFeed is designed to help AI companies produce usable real-world datasets, not just collect raw task outputs.

Instead of separating task posting, contributor supply, quality review, and reward logic into disconnected tools, BotsFeed brings them together into one production workflow.

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Structured task design

Structured task design

Clear instructions, collection rules, and output requirements reduce ambiguity at the source.

Integrated quality control

Integrated quality control

Review is part of the operating system, not a manual cleanup step added at the end.

Scalable contributor network

Scalable contributor network

Supply grows through repeatable workflows, contributor development, and quality-linked participation.

Production-ready datasets

Production-ready datasets

Accepted outputs are organized for downstream training and evaluation use.

BotsFeed dataset platform

Real-world data for vision, action, and embodied AI — collected through structured workflows, reviewed against quality standards, and organized for production use.

for ai companies

A data partner for real-world collection programs

BotsFeed

BotsFeed is built for AI companies that need more than isolated freelancers or generic crowd platforms.

The platform is designed to support custom real-world data programs across environment capture, interaction recording, and human task demonstrations.

That makes BotsFeed useful not only for collecting more data, but for operating a more reliable data production process.

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For AI companies

For AI companies

  • Turn data requirements into structured collection workflows
  • Operate reviewable programs instead of unmanaged task volume
  • Receive cleaner, more usable real-world datasets
Contributor supply that improves
Contributor supply icon

Contributor supply that improves over time

BotsFeed is also designed around contributor clarity and long-term participation.

For scalable operations

For scalable operations

The platform is designed for programs that need to grow without losing structure.

workflow

From data need to structured delivery through a workflow designed for clarity, control, and dataset usability

01

Define your data objective

Start with the environments, scenarios, interactions, and formats your model actually needs.

02

Design collection workflow

Requirements are turned into structured task design, contributor instructions, and review expectations.

03

Collect and review

Contributors generate data in real settings, and outputs can be reviewed before acceptance.

04

Deliver usable datasets

Accepted outputs are organized into structured datasets that are easier to use for training, evaluation, and internal AI workflows.

quality system

Quality is built into the workflow

Structured instructions

Structured instructions

Clear task definitions, environment requirements, submission rules, and output examples improve consistency before collection begins.

Review and acceptance

Review and acceptance

Submitted outputs are evaluated before becoming part of the final dataset.

Operational visibility

Operational visibility

Clear workflow states make production easier to manage, audit, and trust.

Useful-output incentives

Useful-output incentives

Platform logic encourages quality, consistency, and longer-term contributor improvement rather than shallow activity.

Scalable supply operations

Scalable supply operations

Contributor supply can scale through repeatable operating rules, clearer expectations, and feedback loops without turning into low-signal task farming.

Structured delivery

Structured delivery

Accepted data is organized for downstream training and evaluation use rather than left as disconnected raw submissions.

Marquee Section

Structured real-world data production for AI and robotics teams. Structured real-world data production for AI and robotics teams.
engagement

Flexible collaboration for real-world data programs

BotsFeed is built primarily for AI and robotics teams that need structured real-world data collection.

Because real-world data production often changes as models and product priorities change, the platform is designed to support iteration.

  • Custom workflow design for real-world collection
  • Reviewable outputs with quality-linked acceptance
  • Contributor operations that can scale with the program

For AI Companies

Custom real-world data programs
  • Launch pilots or ongoing collection programs based on your model and product needs
  • Work from clearer task structures, contributor execution, and review logic
  • Receive structured outputs that are easier to organize for training and evaluation

Contributor access

Sign in to participate in live workflows
  • Contributors join the app experience through guided tasks and structured submission flows
  • Quality, review, and platform progression help improve output reliability over time
  • The public site speaks to AI customers, while contributor activity happens inside the product
use cases

Examples of real-world data programs BotsFeed can support

Environment data

Environment data

  • Indoor and outdoor scene collection from real operating environments
  • Household spaces, work areas, object layouts, and environmental context
  • Perception data that reflects real-world messiness instead of synthetic simplicity
  • Collection programs designed to improve scene diversity and downstream training value
Interaction data

Interaction data

  • Object handling, task execution, and manipulation-oriented recordings
  • Step-by-step interaction flows captured in realistic human environments
  • Human use of tools, spaces, and objects under real operating conditions
  • Interaction traces that can support embodied AI, robotics, and evaluation workflows
Behavior data

Human behavior data

  • Human demonstrations with observable intent and real-world task structure
  • Behavior patterns tied to workflows, routines, and environment-specific actions
  • Motion and action signals useful for embodied AI training and evaluation
  • Programs designed to capture useful real-world behavior rather than abstract lab-only signals
quality system

A supply model aligned with useful outputs

Task-based contribution

Task-based contribution

Core contributor value comes from completed work and accepted outputs, not empty participation signals.

  • Real completed work inside structured workflows
  • Visibility into accepted versus unaccepted outputs
  • Operational clarity around submission states
  • More emphasis on useful outputs than raw activity
  • A stronger foundation for dataset quality over time
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Network growth with quality logic

Network growth with quality logic

Invitation and network effects are intended to support contributor supply and better participation quality, not to create passive extraction loops.

  • Encourages stronger contributor onboarding pathways
  • Supports healthier supply expansion over time
  • Avoids relying only on sign-up inflation
  • Creates more accountability inside the network
  • Better aligned with durable production quality
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Collaboration and guidance

Collaboration and guidance

Collaboration structures can reinforce responsibility, contributor improvement, and better quality outcomes.

  • Supports higher-responsibility roles in the network
  • Encourages guidance instead of passive traffic generation
  • Helps contributors improve through structure and support
  • Reinforces quality expectations at larger scale
  • Designed for long-term operating stability
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BotsFeed FAQ
faq

FAQ

BotsFeed can support real-world environment data, interaction recordings, and human behavior collection programs.

Yes. BotsFeed is intended for custom real-world data programs rather than one fixed collection template.

Quality is managed through a combination of structured task design, review logic, contributor expectations, and acceptance workflows.

Yes. A pilot is often the best way to validate task design, contributor execution, quality thresholds, and delivery expectations.
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Need a structured real-world data program for your AI system?

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