The operating layer for advanced robot autonomy

We are building the cloud platform and autonomy systems enabling teams to transform real-world data into production-grade robot behavior.

Scope a pilot → Read the thesis

THE PLATFORM

A unified backend for data, operations, and change control

The platform pilot provides edge-to-cloud ingestion, structured cataloging, bounded multi-stream retrieval, and MCAP import and export. SDKs in Python, Rust, and TypeScript and a web-based console support integration, inspection, and data management. Teams can validate with both live streams and historical recordings.

Generic data platforms are not equipped for robotics. Robotics data is multidimensional, involving time, device identity, safety boundaries, and operational evidence. LatentWorlds treats these as core to the platform's architecture. We have engineered an ingestion system for fast, resilient uploads in bad connectivity conditions, and offer advanced compression for different sensor modalities.

AUTONOMY RESEARCH AND DEPLOYMENT

World models for robots that have to work

Classical perception, planning, and control approaches struggle with mundane uncertainty.

We are developing an autonomy stack grounded on robust world models that enable robots to predict the consequences of their actions and make decisions based on past experience. We are using the platform to create a data flywheel where each deployment provides data for the next.

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. We curate our own datasets and train models on the same stack.

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.