Problem

AI labs need enormous volumes of high-quality video to train multimodal and computer-vision models, but assembling a clean, task-specific dataset is expensive and slow: Shofo says such datasets can cost millions of dollars and take months to collect. The core pain is not simply access to video; it is finding the right examples, removing unusable footage, and adding the labels or reasoning annotations needed for model training. The killer use case is a lab requesting a highly specific corpus, such as 100,000 hours of cooking videos showing hand-object interactions, with suitable scene structure and annotations.

Product / Service

Shofo is a B2B video-data service that combines public videos from the open web with private videos aggregated from thousands of sources. It maintains a continuously updated, searchable index of billions of videos, then uses agents to locate matching material, extract the relevant subset, and route it through a cleaning, segmentation, and labeling pipeline. Shofo's human-in-the-loop process is intended to turn a natural-language data requirement into a clean, structured, custom dataset delivered in days rather than months.

The benefit is faster and potentially less costly access to specialized training data without an AI lab having to build its own collection and annotation operation. Shofo also maintains public activity on Hugging Face, including curated talking-head and TikTok datasets, while offering in-house labeled datasets and custom builds through a direct contact model.

Market

Shofo competes in AI training-data infrastructure, specifically custom video and multimodal datasets for AI labs. Its closest alternatives are adjacent rather than identical: Scale AI's Data Engine offers broad data collection, curation, and annotation, while Hugging Face provides an open dataset ecosystem that is generally less tailored than Shofo's on-demand, custom-data workflow. Traditional video-annotation providers such as RWS/TrainAI are another adjacent substitute, particularly for labeling rather than sourcing and indexing billions of videos.

Shofo is an early-stage company founded in 2025 and accepted into Y Combinator's Winter 2026 batch; the available company profile lists four employees in San Francisco. A third-party funding profile reports $500,000 raised in one round, consistent with YC's standard investment amount. Public evidence shows product activity, including three Hugging Face datasets and a roughly 10,000-video, 186-hour talking-head dataset, but it does not identify paying customers or revenue. The best-supported characterization is therefore an early, funded, pre-scale company with revenue not publicly disclosed, rather than a business with demonstrated commercial traction.