Appen

Appen collects and labels data at scale using a large, global crowd, with particular depth in language, speech, and locale coverage that most vendors cannot match.

The company has been doing this since the 1990s, long before the current wave of AI, which is part of why its reach into languages and regions runs deep. The workforce numbers in the hundreds of thousands to over a million contributors across many countries, which is what lets it staff projects that need specific languages, dialects, or local knowledge.

The work spans the full range of data types:

• Speech and audio collection, transcription, and annotation
• Text labeling, classification, and sentiment across many languages
• Image and video annotation for computer-vision models
• Data collection to spec, where you need new samples gathered rather than existing data labeled
• Human feedback and evaluation data for language models

Appen tends to fit when the challenge is scale and coverage: a speech model that needs recordings across dozens of accents, a search engine tuning relevance in markets you do not staff, a dataset that has to be gathered fresh in several countries at once. Being a public company on the ASX also gives larger buyers a level of transparency and process that a young startup cannot offer yet.

The model is a managed service. You describe the data you need and the quality bar, and Appen staffs, trains, and runs the crowd, then delivers the data with quality control applied. Your team does not run the tooling or recruit the people.

Pricing is enterprise, quoted per project based on volume, the languages and locales involved, and the type of work.

Best for teams that need large-scale, multilingual data collection or labeling handled as a managed service. Less of a fit for a team that wants a self-serve platform to run in-house, where a tool like Labelbox, V7, or SuperAnnotate gives you more direct control.