Typical situation
Large-scale egocentric and demonstration data varies in segmentation, boundaries, and semantics across batches.
Typical Solutions
Organize data delivery around each team’s role in the embodied AI stack
These are common customer situations, applicable workflows, and expected value. They do not imply an engagement with any specific company.
01
For embodied AI model developers
Build a consistent task-semantic and action hierarchy for VLA training.
KeenTruth approach
Govern raw data, define a task hierarchy, and combine VLM pre-annotation with expert refinement and consistency checks.
Expected value
Reduce data cleaning and guideline interpretation while improving reuse across model iterations.
02
For robotics OEMs
Move complex, multi-source real-robot data into training faster.
Typical situation
ROS bag, MCAP, and HDF5 data varies across devices, software versions, tasks, structures, and topics.
KeenTruth approach
Profile structures, check completeness, filter anomalies, decompose tasks, and validate output.
Expected value
Reduce repetitive preprocessing and anomaly investigation for data and algorithm teams.
03
For embodied AI data providers
Scale production through VLM assistance and disciplined operations.
Typical situation
Large-scale parallel production must manage progress, productivity, guideline consistency, and quality.
KeenTruth approach
Combine intelligent pre-annotation and risk triage with expert refinement, tiered review, and progress management.
Expected value
Apply 3× industry-benchmark production efficiency with traceable quality and issue distribution.
Contact
Move raw embodied data into training faster.
Tell us about your data, annotation requirements, and quality expectations.