Workplace narratives shift over time—what was called "restructuring" becomes "performance management" becomes "cultural fit concerns"—and contextual embeddings let AI track these semantic changes across months of communication. This technique reveals how language is used to reframe situations or build a particular story about your performance, even when the underlying circumstances haven't changed.
Contextual embeddings are numerical representations of text that capture meaning based on surrounding words and context, not just keywords. When applied to workplace communications over time, they can detect when the framing of a situation, a project, or your performance has quietly shifted in ways that are difficult to articulate but professionally damaging.
Gaslighting and narrative revision often happen gradually through language rather than overt statements. By comparing embeddings of earlier communications with recent ones, AI tools can surface measurable semantic drift, giving you documented evidence that the story being told about you has changed.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
Explore related journeys or tell Peri what you're working through.