Data integrity is earned, not assumed.
Check where Physical AI data comes from, whether its technical signals are intact, and whether its actions and annotations match the task. Scope supplier provenance, authenticity screening, technical QC and human acceptance review together.
Know what you are training on.
Physical AI datasets are useful only when the recordings, sensor streams, annotations and rights are suitable for the training objective. Our managed QC workflows are defined around these specific risks.
Technical integrity
File decoding, duration, resolution, duplicates, black or static frames and basic capture integrity.
Synchronized streams
Timestamp consistency, missing-stream detection and sensor health checks where relevant.
Semantic review
Task correctness, outcome agreement, label consistency, schema conformance and manual spot checks.
How we scope and deliver.
Your target task, available source signals and acceptance criteria determine what is included in a dataset.
Source & rights checks
Supplier documentation, permitted-use confirmation, provenance evidence and data minimisation.
Technical checks
Corrupt files, duplicates, low-information video, unexpected formats and sensor-level gaps.
Annotation acceptance
Sampling plans, reviewer flags, correction loops and decision logs.
Delivery manifest
Structured file inventory, schema documentation, versions and exception notes.
Data authenticity is assessed through multiple forms of evidence: supplier documentation, capture context, technical integrity checks and human review. AI-based screening alone cannot establish that footage is genuine.
Common questions.
Do you use Foxglove for MCAP?
Foxglove can be used to inspect supported robotics recordings, topics and synchronized signals; scripted checks remain important.
Is Gemini your only quality-control tool?
No. Video-integrity checks can be handled by standard tooling such as Python and FFmpeg. Vision models are evaluated where useful.
Do you certify that all data is perfect?
No. We document agreed checks, findings and acceptance criteria rather than implying error-free footage.
Start with your training objective.
Tell us the data modality, hardware or camera setup, target task, volume and deadlines. We will scope a realistic pilot.