Disciplinary actions cluster in time or space for legitimate reasons (end-of-quarter reviews, policy rollouts, seasonal hiring cycles) or for illegitimate ones (targeting someone after they complained). Temporal patterns expose the difference.
Temporal pattern mapping uses AI to plot disciplinary actions, negative feedback, and policy enforcement against a timeline of events such as complaints filed, accommodations requested, or union activity, revealing whether consequences cluster suspiciously around protected actions.
Courts and HR investigators look for causation between protected activity and adverse treatment, and a clearly visualized temporal map generated from your own records gives you a concrete, data-backed narrative that is far more persuasive than memory alone.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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