StableBrowse GTM brain

StableBrowse builds multimodal physical-world datasets and processed training outputs for robotics and embodied AI teams.

category

Physical AI training data infrastructure

market profile

  • text: Physical AI model teams need real-world task demonstrations with synchronized multimodal data and inspectable labels.
  • text: The current website describes a physical AI data business; the YC launch search result describes an earlier browser-agent product, so historical positioning should not be treated as current.

personas

  • title: Head of robot learning or foundation models
    pains:
    • Needs diverse real-world manipulation examples suited to model training.
    • Must judge whether data represents the tasks and edge cases relevant to the model.
  • title: Robotics data engineering lead
    pains:
    • Synchronizing sensors and calibration across collection sessions consumes engineering time.
    • Raw clips require post-processing, validation, schemas and provenance before training.
  • title: Machine learning research lead
    pains:
    • Needs reviewable labels and geometry to assess training data quality.
    • Must obtain task-specific examples beyond generic web video.

icp

  • text: Robotics companies and physical AI labs training manipulation or embodied-agent models on real task demonstrations.
  • text: Model teams seeking custom collection and post-processing across industrial and everyday physical workflows.

message pillars

  • text: Provide the complete data layer for embodied models.
  • text: Turn raw recordings into usable training inputs.
  • text: Make the delivered episodes auditable.

objections

  • objection: Can the dataset match our task and model requirements?
    response: Agree a capture protocol, schema and representative sample with the model team before a larger collection.
  • objection: How can we assess data quality?
    response: Inspect a sample for synchronization, calibration, labels, provenance and suitability for the intended training pipeline.
  • objection: Why not collect raw video ourselves?
    response: Compare internal capture and post-processing costs with a scoped delivery that includes synchronized signals and review artifacts.

competitors

  • name: Scale AI
    why: Provides physical AI data collection and multimodal annotation services.
  • name: Macrodata Labs
    why: Processes robot and egocentric recordings into quality checks and action data.
  • name: Labelbox
    why: Offers multimodal annotation and data management for robotics datasets.

keywords

  • physical AI training data
  • robotics demonstration datasets
  • egocentric manipulation data
  • multimodal robot data collection
  • robot learning data annotation
  • hand pose training data

voice

  • technical and precise
  • research-oriented
  • infrastructure-focused

signals

  • text: A robotics lab is hiring for data collection, robot learning or multimodal data engineering.
  • text: A model team announces a manipulation model or expands into new physical tasks and environments.

stack

  • RGB
  • stereo depth
  • IMU
  • tactile sensors

pricing

model: unknown

motion

sales_led

channels

  • LinkedIn
  • technical content
  • dataset samples
  • contact requests

confidence

medium

Founders

  • name: Deepit Shah
    title: Founder/CEO
    role: CEO
    bio: Founder @ StableBrowse | Ex-Amazon | UIUC - CS
  • name: Jay Mehta
    title: Founder/CTO
    role: CTO
    bio: StableBrowse | Ex-Amazon | Purdue CS
  • name: Somansh Shah
    title: Founder/COO
    role: COO
    bio: buildling @StableBrowse | Ex-Amazon | Ex-Founding Eng

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