# Titus Data Exchange — The substrate simulation doesn't have

> For builders. The last mile from your model to a real factory floor.

Every model trained on synthetic data eventually meets a real factory floor. That meeting is where deployments die. We capture the structured, multi-modal, continuously updated ground truth that closes the last mile from sim to real.

Sourced from a live deployment footprint across construction sites, manufacturing floors, care facilities, and commercial kitchens. Spatially indexed. Temporally tracked. Semantically labeled. Delivered in formats your stack already understands.

## Why you can't skip this

Synthetic data covers the easy 80%. We cover the 20% that breaks deployments.

- **30-year-old conveyors.** Grease-stained labels. Unpredictable human movement. Variable lighting that changes by the hour. Synthetic data can't model what it's never seen. We capture the mess that real facilities actually are.
- **Structured, not raw.** Every dataset is spatially indexed, temporally tracked, and semantically labeled. Delivered in standard formats — OpenUSD scenes, annotated point clouds, labeled video sequences, structured JSON.
- **Continuously updated.** Most real-world datasets are one-time collections. Ours are continuously updated from live deployments. The data grows, evolves, and reflects how physical environments actually change.

## Who buys it

- **Robotics companies** — Real-world spatial data from active facilities — the exact environments where robots will deploy. Available as OpenUSD scenes for direct import into NVIDIA Isaac Sim and Omniverse.
- **World-model & digital-twin platforms** — Continuous ground-truth to stay calibrated. A persistent data stream that keeps your twin (or your latent world model) honest about how the real environment is changing.
- **Insurance & risk modelers** — Pricing physical-world risk from observation data instead of claims data. Near-miss events, safety compliance rates, environmental conditions — captured continuously.
- **Autonomous systems & AI researchers** — Diverse, large-scale, multi-modal training data from construction, manufacturing, and care environments. Consistent schema across all verticals — stop hand-rolling per-site collection.

## Data categories

- **Construction** — Trade activity, material flow, equipment positioning, progress tracking, safety events. (OpenUSD, Point Clouds, Labeled Video, JSON)
- **Manufacturing** — Machine cycles, operator workflows, material handling, quality events. (OpenUSD, Time-Series, Labeled Video, JSON)
- **Facility operations** — Staff movement patterns, environmental conditions, incident detection (anonymized). (Anonymized Sequences, JSON, Aggregated Analytics)

## Data quality

- **Three-tier labeling** — AI auto-labels first, human reviewers verify, precision geometric annotations added where needed.
- **Consistent schema** — Same sensor platform, same labeling pipeline, same schema across all verticals. Train once, deploy everywhere.
- **Continuously validated** — Labels are validated against real outcomes from the operational layer, not just annotator agreement scores.

## Formats & compatibility

Delivered in standard formats: OpenUSD scenes, annotated point clouds, labeled video sequences, structured JSON. Native compatibility with NVIDIA Omniverse, Isaac Sim, and the Cosmos data-curation pipeline.

## Status

Currently in private access. Onboarding the first cohort of data partners — robotics teams, world-model labs, and digital-twin platforms.

[Apply for data access](/contact) · sales@titusos.ai