Deployment-specific data capture for embodied AI

The missing data layer for physical AI

Real-world edge cases that simulation can't generate. We capture deployment-specific data for embodied AI and robotics — closing the sim-to-real gap.

Protocol → capture → QC → delivery

From protocol design to field execution, we deliver datasets with provenance — ready for training and evaluation.

protocol.yaml
target:
  task: "robot grasping"
  conditions:
    - low_light
    - clutter
    - reflective_surfaces
capture:
  sensors: ["rgb", "depth"]
  metadata: ["pose", "location", "timestamp"]
qc:
  pii_removal: true
  provenance: true

Embodied AI & robotics

Data for perception, manipulation, and scene understanding in real environments.

Compliance built-in

PII removal, consent tracking, and audit-ready metadata where required.

How it works

A protocol-driven pipeline from your deployment to measurable model improvements.

Get a quote →

End-to-end in 4 steps

  1. 01

    Share your model or task

    We align on deployment conditions, failure modes, and what “better” means for your model.

  2. 02

    Design a capture protocol

    Targeted protocol: what to collect, where, how, and which metadata you’ll need for training + eval.

  3. 03

    Collect real-world data

    Vetted field teams execute the protocol. We validate, clean, and standardize with provenance.

  4. 04

    Evaluate and improve

    Use the dataset to fine-tune and evaluate—closing the sim-to-real gap on the edge cases that matter.

We focus on

Embodied AI & robotics — perception, manipulation, and scene understanding in real environments.

RGB/VideoDepthSensor metadataProvenance

Outputs you receive

  • Capture protocol + training guide
  • Dataset with metadata + provenance
  • QC report + acceptance criteria

Compliance-ready

PII removal, consent tracking, and region-aware collection where required.

Why work with us

You get a repeatable pipeline for real-world edge cases—without building a capture org.

Built for model outcomes

Protocols are designed around failure modes: lighting, clutter, sensor noise, environment variation—so capture directly maps to improvements.

Targeted capture

No generic datasets—only what moves your eval curves.

Provenance

Audit-ready metadata and lineage where required.

QC systems

Acceptance criteria, spot checks, and re-capture loops.

Enterprise-ready

Compliance controls and region-aware execution.

Faster than building in-house

Stand up capture ops and QC in days—not quarters.

Add your existing evaluation harness or we can deliver a scoped acceptance spec.

Designed for edge cases

Capture the rare conditions simulation can’t generate reliably.

Low lightReflectiveOcclusionMotion blurClutter

Request a quote

Tell us about your model, deployment environment, or performance gap. We’ll respond with a scoped proposal.

What we’ll need

Secure intake
  • Model/task + deployment context
  • Failure cases you care about
  • Approximate volume + timeline
  • Any compliance constraints

Prefer email? founder@comoxai.com

Field operations partners

We work with vetted capture specialists and field teams who execute protocols in real environments—reliable execution, quality standards, and timely payment.

Clear protocols

Step-by-step instructions and examples—capture the right edge cases.

Quality standards

Checks for framing, lighting, motion blur, and metadata completeness.

Reliable payment

Structured task payouts and prompt resolution for re-captures.

Privacy first

Consent + PII handling requirements when a task demands it.

About COMOX AI

AI models often fail in real environments because training data doesn’t match deployment conditions—lighting, clutter, sensor noise, environment variation. Comox is real-world model-tuning infrastructure: we design capture protocols, collect deployment-specific data through our network, and deliver it so you can evaluate and improve model performance. We focus on embodied AI and robotics, where the sim-to-real gap is hardest. Our team brings data pipelines, quality systems, and compliance so enterprises and research labs can close that gap without building it all in-house.

What we are

Real-world model-tuning infrastructure: protocol design, field operations, QC, and delivery.

What we deliver

Deployment-specific datasets with metadata + provenance—ready for training and evaluation.