Data Engine for Physical Intelligence workflows

Solving Physical Intelligence through scale

Scope a pilot → Read the thesis

PRODUCTION MODULES

Production Modules for Physical Intelligence at scale

A set of libraries to build and scale your end-to-end multimodal data workflows. Designed to be integrated and deployed within your existing stack, not to replace it.

Ingest, manage, and query multimodal data efficiently at scale. Caudal combines production data infrastructure with annotation pipelines for physical AI applications and model training.

Our Mission

Solving Physical Intelligence through scale.

The gap between model performance and market demands is still far from being closed, with data availability and infrastructure as the main bottlenecks. Our goal is to boost the model development cycle for every company in the space by providing building blocks that easily integrate with any existing stack.

We built every module around the following principles:

  • Existing infrastructure should be enriched, not replaced.
  • Data shouldn't leave your own network unless you want it to.
  • Agnostic by design: any robot, data schema, or deployment stack.
  • Petabyte scale should be the norm, not a privilege.

DATA WORKFLOWS

Solving Physical Intelligence through scale

Data corpus size and model iteration speed are the only metrics that matter. The most successful teams are the ones able to quickly iterate on model design, dataset creation, and downstream task evaluation. To support MLOps teams in these objectives, we designed a set of modules that can be easily integrated within an existing data stack.

Run continuous data collection in the wild with resumable uploads and optimal edge buffering. EdgeRelay organizes incoming streams into an optimized cloud storage layout, enriching existing indexes during upload.

Optimize retrieval of queried episodes at petabyte scale. Recordlake serves synchronized data to applications and creates datasets from queries over the stored recordings. Available hosted or customer-hosted.

TRUST

Production-level security and control

LatentWorlds enforces access control at the organization and project level, with defined roles, encrypted API traffic, and device authentication. Our multitenant architecture ensures that data is accessible only within the containing project and through fine-grained permissions.

Boundaries

  • Org and project-level access isolation
  • Separate identities for people, services, robots, and edge nodes

Access

  • Role-based access control with per-project API keys
  • Mutual TLS for all robot and edge-node ingestion

Audit

  • Audit logs for recordings, exports, incidents, and system changes
  • Configurable retention, deletion, and export policies

Data

  • Default managed cloud storage, or customer-hosted for full data ownership
  • Strict ownership and workflow separation for data in transit

About

The missing layer of the robotics stack

There is no gold-standard infrastructure for turning sensor data into intelligence reliably and at scale. Every robotics company builds their own ingestion, their own storage, their own pile of data-wrangling scripts. It takes years and it eats tons of engineering resources.

We are building that infrastructure. Our data engine makes petabyte-scale sensor data cheap to store and fast to work with, with advanced ML tooling that automates turning raw recordings into training-ready datasets. We support on-premise deployments and a high degree of customization.

Our team comes from

PILOTS

What is your biggest bottleneck?

PILOT 01

Get every byte from the field to the cloud.

An ingestion pilot for teams collecting data from deployed robots. Prove that recordings arrive intact and recoverable over the networks you actually have, without migrating your data pipeline.

Pilot capabilities

  • Integrate the Python or Rust SDK next to your robot, logger, or simulator.
  • Buffer to disk through outages and resume uploads from committed progress.
  • Durable acknowledgements end to end: data is deleted from the robot only once it is safe in the cloud.
  • Retrieve synchronized time windows across streams, and import or export MCAP.

PILOT 02

Make petabyte-scale data cheap to keep and fast to use.

A storage and retrieval pilot for teams whose datasets have outgrown their tooling. Bring recordings onto an engine designed around storage economics and precise, high-throughput reads.

Pilot capabilities

  • Modality-specific compression that can cut storage size by orders of magnitude.
  • Queries planned to read only the bytes they need from object storage.
  • Training sets defined as views over raw recordings, not copies, streamed at full throughput to GPUs.
  • Runs on our cloud, on your object storage, or fully on-premise.

PILOT 03

Turn raw recordings into annotated, searchable datasets.

An ML pilot for teams sitting on data they cannot fully use. We run annotation and indexing pipelines over your recordings and deliver enriched datasets you can inspect, search, and train on.

Pilot capabilities

  • Automatic annotation pipelines, from hand and object poses to action segments.
  • Semantic indexes that make months of footage searchable.
  • Coverage and distribution analysis to guide what to collect next.
  • Every output traceable to the recording, calibration, and model version that produced it.