LabelSprint

LabelSprint is a web app (with optional desktop helper) that helps small teams create high-quality ML training labels without building an internal labeling operation. It ingests raw text, images, or audio, proposes labels using lightweight models, and routes uncertain items to humans with clear guidelines and QA checks. The core value is speed with accountability: you get measurable label quality, audit trails, and drift alerts when incoming data no longer matches the labeled distribution. It includes active learning to prioritize the most informative samples, inter-annotator agreement scoring, and a simple workflow for reviewers to resolve conflicts. Exports go directly to common formats (COCO, YOLO, JSONL) and to popular training pipelines. This is an AI app + traditional workflow app: AI accelerates labeling, but humans stay in control so you don’t ship garbage labels that break models in production.