Upalgo Labeling
High quality labeling for complex data
Produce accurate, consistent labels for complex datasets. Combine expert human labeling with structured software workflows to ensure quality, traceability, and scalability.
Supported Data Types
Environmental audio, industrial sounds and underwater acoustics.
Sensor data, telemetry, monitoring systems, and multivariate time series.
Spectrograms, RF signals, SIGINT, ROEM, and other frequency-domain representations.
Upalgo Labeling solves labeling complexity
Labeling complex data is difficult. Audio streams, time series, and frequential signals often contain ambiguity, noise, and edge cases that automated tools alone cannot handle. Without human in the loop processes and proper validation, labeling quality degrades quickly and undermines model performance. Upalgo Labeling addresses these challenges with structured workflows designed for real-world data.
A clear and efficient UI designed for complex data, allowing experts to focus on annotation tasks rather than tooling.
Every label can be reviewed, validated, and corrected by experts to ensure consistent, high-quality results.
Interfaces and workflows adapted to audio, time series, and frequential data.
Upalgo Labeling can run on isolated or offline PCs, suitable for secure or restricted environments.
From early experimentation to long-term production labeling campaigns.
AI assisted labeling
Upalgo helps you propagate labels efficiently across your datasets. Once a subset of data is labeled, the system applies learned patterns to suggest labels on the remaining data, significantly reducing manual effort qnd increasing labeling speed up to 40 times.
Upalgo also detects inconsistent or conflicting labels and flags them for review. By identifying these issues early, teams can correct errors before they impact training. High-quality data is essential for reliable machine learning, and Upalgo is designed to protect that quality at every step.
Upalgo also detects inconsistent or conflicting labels and flags them for review. By identifying these issues early, teams can correct errors before they impact training. High-quality data is essential for reliable machine learning, and Upalgo is designed to protect that quality at every step.
