Emotion recognition from text and speech is now central to affective AI, a field focused on making machines more emotionally aware. From call center analytics to mental health apps, this capability is no longer experimental—it's a critical differentiator. But these systems only work if they’re trained on precisely labeled emotional data, spanning diverse languages, cultures, and modalities.

In this blog, we’ll explore the core methods used to annotate human behavior in surveillance footage, the challenges of scaling such annotation, and how FlexiBench enables security and analytics teams to turn raw video feeds into real-time behavioral intelligence.

In this blog, we explore the challenges of annotating historical handwriting, the methodologies being used to structure these collections, and how FlexiBench enables institutions to train transcription models with accuracy, integrity, and respect for archival context.

In this blog, we unpack how product tagging fuels e-commerce AI, the complexity of labeling product data across modalities, and how FlexiBench helps retail platforms build and maintain structured catalogs at scale.

In this blog, we explore the art and science of annotating legal briefs for argument extraction, the practical challenges involved, and how FlexiBench enables legal AI companies to structure arguments from raw legal text with accuracy and regulatory defensibility.

In this blog, we break down how radiology annotation works across modalities, why it’s critical to the development of trustworthy AI diagnostics, and how FlexiBench enables healthcare innovators to build high-performance training datasets with clinical rigor.