Data Engine: The Data Layer for Physical AI.

Turn real-world data into high-quality training and eval sets – fast, automated, and under your control. Repeatable pipelines with guided quality checks.

Build from Real Operations.

The Data Engine structures multimodal video into auditable training data – one foundation for labels, SOPs, knowledge, and agents.

Connect your data

Bring Bring all operational data into one place. Batch upload or stream live sources into the Data Engine, configure ingestion pipelines, and keep multimodal data under enterprise controls including GDPR support.

Details

  • Multi-view and egocentric video
  • Narration and audio
  • Sensor streams
  • Robotics datasets
  • PDFs, manuals, and technical documents
  • Batch upload and live streaming
  • Configurable ingestion pipelines
  • Access control and GDPR-ready data handling

Build your knowledge base

Create the domain context that powers annotation automation and live agents. Embed documents, transcribe video, and link drawings and procedures so every later step runs on your operational knowledge.

Details

  • Embed PDFs, manuals, and SOPs
  • Transcribe and index operational video
  • Link technical drawings and asset references
  • Multimodal RAG over documents, video, and structured data
  • Domain vocabulary and procedure context
  • Access-controlled retrieval for teams and sites

Annotate and structure

Annotation puts control into Physical AI. Ramblr automates labeling so you move faster from collection to deployment, with human review when confidence is low. All data lands in one structured representation ready for training, evaluation, and agents.

Details

  • AI-powered annotation pipeline automation
  • Spatio-temporal labels (masks, boxes, tracks, set-of-mark)
  • Activity annotations and SOP extraction
  • Sensor and state attributes (e.g. gripper, IMU)
  • Skill and sub-skill annotation
  • Human-in-the-loop review (when needed)
  • One structured representation across sources

Generate training datasets

Automatically produce grounded instructions with ground-truth annotations at scale. Purpose-built to train and validate vision-language models on real physical operations – not synthetic desktop tasks.

Details

  • Grounded instruction generation from annotated video
  • Training, validation, and test dataset splits
  • Evaluation sets from real operational scenarios
  • Scene graphs, skills, and procedure-linked samples
  • Export formats for VLM and detector training
  • Full lineage from source clip to training sample

Train and validate

Fine-tune and test models on your data before anything gets deployed. Run quantitative evals, find gaps, and feed results back into collection and annotation. Bring your own models or use supported foundation models – no model lock in.

Details

  • Fine-tune on your operational datasets
  • Model-agnostic training (Ramblr, foundation, or BYO models)
  • Automated performance metrics and test runs
  • Failure analysis to guide new data collection
  • Compare model versions on the same eval set
  • Promote only models that meet your thresholds

Manage your models

Keep every model versioned, comparable, and deployable. One place to register models, track lineage, set rollout rules, and send approved models into Agent Studio and production runtimes.

Details

  • Model registry and versioning
  • Model ecosystem: open-weight, frontier, and BYO
  • Lineage from dataset → training run → model
  • Approval gates and environment promotion
  • Deploy to Agent Studio, edge, cloud, or customer infra
  • Rollback and audit history