Labelbox

Founded 2018

Train AI models and automate engineering tasks

You can use Labelbox to coordinate autonomous agents that handle recurring engineering, research, and data workflows. Teams deploy these agents to investigate continuous integration failures, resolve flaky tests, patch code vulnerabilities, and upgrade dependencies without manual intervention. For machine learning development, it audits training datasets for quality issues, tracks model evaluation regressions, runs ablation studies, and benchmarks candidate checkpoints against custom test suites. By connecting directly to repositories, cloud infrastructure, and issue trackers, it completes repetitive maintenance tasks and produces tested pull requests ready for review.

What Labelbox does

Reproduce bugs from Sentry issues and generate pull requestsRun parameter sweeps across machine learning experimentsAudit datasets for duplicates and label leakageTriage nightly regression build failures across commitsExtract customer feature requests from recorded sales callsReview cloud permissions and flag unused access privilegesAutonomous software engineering agents for bug fixing and flaky testsContinuous model evaluation and automated regression triageMulti-agent coordination across code repositories and cloud infrastructureAutomated dependency updates and deprecation cleanup with test runsAutomated incident investigation and postmortem draftingDataset quality auditing for duplicates, label drift, and data leakageAblation experiment running to verify model metric changes