Data labeling is the process of turning raw text, audio, image, video, medical, and multimodal data into structured training examples that AI models can learn from. InfoBay combines AI-assisted workflows with expert human review so labels are accurate, consistent, and ready for production model training.
Teams use InfoBay for labeling work that needs stronger quality controls than commodity crowdsourcing, including domain-specific annotation, safety-sensitive review, and multilingual data operations.
Coverage for speech, text, and multilingual AI workflows.
Domain-aware contributors for expert review and QA.
Guideline checks, reviewer calibration, and delivery validation.
InfoBay labels text, speech, images, video, healthcare records, code tasks, and multimodal examples for AI systems that need reliable supervised signals.
High-quality labels determine whether a model learns the intended pattern or amplifies noise. InfoBay structures labeling outputs for SFT, RLHF, evaluation, and real-world deployment.
Answers for buyers
InfoBay combines AI-assisted workflows with expert human review, domain-specific rubrics, and multi-tier QA so labels are usable for production AI rather than only bulk annotation.
Yes. InfoBay supports multilingual labeling across major global, Indic, African, and Southeast Asian languages, including audio and text workflows.
Yes. Each project can use custom annotation guidelines, reviewer calibration, acceptance thresholds, and delivery formats.