Physical AI end-to-end walkthrough
This walkthrough illustrates how teams commonly use Encord to build, improve, and scale Physical AI systems.Step 1: Ingest multimodal sensor data
Start by bringing raw sensor data into a unified workspace:- Upload or sync cloud data
- Preserve timelines and sensor relationships
- Attach environment and scenario metadata
Step 2: Curate data intentionally
Before labeling everything:- Filter for edge cases
- Group rare or failure scenarios
- Create collections for targeted annotation
Step 3: Create annotation projects
Design projects that reflect real-world complexity:- Choose appropriate workflows
- Define clear ontologies
- Assign annotators and reviewers
Step 4: Annotate and review
Annotate data with temporal and multimodal awareness, then validate quality through structured review.- Automated labeling accelerates throughput
- Review stages ensure correctness
- Consensus resolves ambiguity
Step 5: Export and evaluate
Export labeled data for training and evaluation. Compare model predictions against ground truth to identify gaps. DocsStep 6: Close the loop
Feed evaluation insights back into curation:- Identify new edge cases
- Re-prioritize data
- Refine Ontologies and Workflows
What success looks like
You know your Physical AI pipeline is healthy when:- New failure modes are surfaced quickly
- Annotation effort is focused where it matters most
- Label quality is measurable and auditable
- Iteration cycles shorten over time
Next steps
- Explore Applied AI for production-focused workflows
- Dive deeper into Platform → Annotate for workflow configuration
- Automate repetitive steps using Agents