Cortex AI GTM brain

Cortex AI supplies real-world human and robot training data plus supervised deployment and evaluations to embodied AI teams.

category

Robotics training data and evaluation

market profile

  • text: Embodied AI teams need real-world demonstrations, robot trajectories and evaluation beyond simulation and lab settings.
  • text: Data vendors compete across egocentric capture, teleoperation, annotation and real-world robot demonstrations.

personas

  • title: Robotics foundation model research lead
    pains:
    • Sourcing diverse real workplace data with useful pose, depth and task annotations
    • Evaluating model generalization across tasks and environments
  • title: Robot learning or data engineering lead
    pains:
    • Obtaining embodiment-specific trajectories for fine-tuning
    • Converting field interventions and failures into usable recovery data
  • title: Robotics deployment lead
    pains:
    • Running physical pilots while retaining human oversight
    • Securing representative workplace sites and trained operators

icp

  • text: Frontier robotics labs and general-purpose robot developers training embodied or foundation models.
  • text: Teams moving from model development to physical workplace evaluation or fine-tuning on a specific robot embodiment.
  • text: Workplaces able to host collection or evaluation can be supply-side partners to the robotics data program.

message pillars

  • text: Train on actual work environments.
  • text: Obtain robot-specific trajectory data.
  • text: Turn supervised deployment interventions into useful data.

objections

  • objection: Will the data match our robot, task and environment?
    response: Scope embodiment, capture environments, annotation schema and a representative sample before committing to a collection program.
  • objection: How can robots be evaluated when they fail in the field?
    response: Cortex describes human oversight and recovery capture; establish the intervention and evaluation protocol with the team.
  • objection: Can we use public datasets instead?
    response: Compare public coverage with the specific workplace tasks and embodiment data needed for the pilot.

competitors

  • name: Claru
    website: https://robottraining.jobs/research/robot-training-data-vendors
    why: Offers robotics and egocentric training data collection and enrichment.
  • name: XDOF
    website: https://roboticsmap.org/l/data-aggregation
    why: Collects teleoperation and wearable-sensor robotics data.
  • name: Sensei Robotics
    website: https://robottraining.jobs/research/robot-training-data-vendors
    why: Uses a contractor network to collect task demonstrations for robotics teams.

keywords

  • robotics training data collection
  • egocentric video dataset robotics
  • embodied AI data provider
  • robot trajectory data
  • robot learning evaluation
  • human in the loop robot deployment
  • robot manipulation demonstrations

voice

  • technical and research-oriented
  • ambitious
  • practical about real-world deployment

signals

  • text: A robotics lab is training a foundation model and seeking diverse workplace egocentric data.
  • text: A robot developer is launching real-world pilots and needs human recovery and evaluation data.

hiring

  • Software Engineer, Internal Tooling and Infrastructure
  • Robot Operator Manager
  • Robot Operator

pricing

model: unknown

motion

sales_led

channels

  • research partnerships
  • website contact
  • LinkedIn
  • X

confidence

medium

Founders

  • name: Lucas Ngoo
    title: Founder/CEO
    role: CEO
    bio: Founder and CEO of Cortex AI. We're building the world's most diverse real-world, real-workplace, and industry-scale egocentric + robot dataset. Prior to Cortex AI, Lucas was the co-founder and CTO of Carousell (valued at $1B+).

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