Appen
VerifiedHuman-validated training data, annotation and evaluation services for AI models
Product preview
Appen
What it is
Appen is a long-established provider of human data for building and evaluating AI. AI labs and enterprises use it for custom data collection, annotation, and evaluation across text, speech, images, video and sensor data. Its offerings cover frontier model alignment such as RLHF, supervised fine-tuning data and red teaming, agent trajectories and RL environments, speech corpora, LiDAR annotation for physical AI, coding datasets, and audits for hallucination and bias. Work runs through the Appen AI Data Platform, which pairs automation with human review, and is staffed by a global crowd and subject matter experts. Appen also sells off-the-shelf datasets. Pricing is quoted per project.
Key features
- Custom data collection and annotation
- RLHF, SFT data and red teaming
- Model evaluation and bias audits
- Off-the-shelf datasets
What we liked
- + Decades of experience in AI data
- + Broad coverage of languages and modalities
- + SOC 2 and ISO 27001 certified
Worth noting
- · Enterprise sales process with custom quotes
- · Not a self-serve tool for individuals
Editor's notes
More context from AI BirdView
Behind most capable AI models sits a large amount of carefully labeled human data, and Appen has been supplying it for a long time. The company says it has around 30 years of experience in AI data and coverage of more than 500 locales, which makes it a familiar name for teams that need multilingual or multimodal datasets at scale.
- Frontier alignment work including chain-of-thought traces, expert RLHF and adversarial red teaming
- Agentic AI data such as golden trajectories, RL environments and tool-use logs
- Speech corpora, vision-language annotation and voice agent evaluation
- LiDAR, robotics and sensor fusion annotation for physical AI projects
- Model integrity services covering hallucination benchmarks, bias and compliance audits
Appen is best for AI labs, enterprise ML teams and research groups that need high-volume or specialist human data rather than a do-it-yourself labeling tool. A practical tip: arrive with a clear annotation guideline and a small pilot set, since a well-defined pilot helps both sides calibrate quality and cost before committing to a large engagement.
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