SuperviseLab helps video understanding and multimodal model teams turn raw video assets into structured, distilled, training-ready datasets for SFT, evaluation, and human-in-the-loop data production.
Video understanding model teams, multimodal foundation model teams, post-training teams, and benchmark/eval teams.
Training sets, preference sets, evaluation sets, clip-level supervision, OCR/ASR outputs, speaker attribution, and structured JSON/JSONL.
Generic annotation is not enough. Model teams need schema-driven, QA-controlled, training-ready data assets with clear downstream use.
Raw video → schema design → model draft / human correction → QA & arbitration → JSONL delivery for training or eval.
We published a public sample dataset on Hugging Face to demonstrate what a video understanding distillation schema looks like.
Use the website to request a sample pack, discuss pilot projects, or align schema and acceptance criteria.