fiund vs Shaip

The core differences

The overlap is speech: Shaip cites 70,000+ hours across 65+ languages plus 250K hours of physician dictation; fiund licenses real-world audio and video without the healthcare specialization.

Shaip mixes collection-to-spec from 60+ countries with an off-the-shelf catalog, and AI-training licence terms vary per asset; fiund’s licence shape is uniform — explicit training rights and consent, documented per asset.

Neither publishes a price list. Shaip quotes per project and catalog asset; fiund scopes each licence against a dataset brief.

Where Shaip wins

Healthcare-leaning data collection and annotation, plus off-the-shelf datasets.

• Healthcare/speech depth
• Both collection and catalog

Consider for healthcare/speech collection; verify licensing terms for training use.

Where fiund wins

• Provenance you can defend. Every asset carries a signed licence with explicit AI-training rights and separate voice/likeness consent.
• Not already in the crawl. Non-public material sourced from owners, so you aren't paying for what your model has already seen.
• Sourced to brief. If the dataset doesn't exist yet, fiund goes and sources it.

How to choose

Pick on the axis you actually care about. If defensible chain of title and documented consent are the gate, fiund is built for that. If your need matches Shaip's core model — crowd annotation — the full Shaip review is honest about where it leads.