Reputation doesn’t collapse overnight; it accumulates—one post, one review, one offhand tweet at a time. Modern ORM treats the internet like a living sensor network and reads it with machines.

Start by wiring a listening graph: forums, news, TikTok captions, app reviews, support tickets. Normalize language, strip PII, and enrich with entities (brand, product, execs, competitors) plus facets (price, service, safety). Run LLM classifiers fine-tuned on your voice of customer to tag mentions by topic, emotion, and risk. Pair them with anomaly detection (seasonal baselines + Bayesian change points) to flag velocity shifts—not just volume spikes.

Now, route signals through your response SOPs. For low-risk issues (shipping delays), trigger templated replies and FAQ links; for medium risk (feature failures), open a public status note and assign an owner; for high risk (harm, privacy, safety), escalate to a war-room with legal and PR. Every ticket should carry evidence snapshots (original post, model scores, entity map) and a time-to-first-response SLA.

Close the loop: attribute outcomes to actions—did sentiment recover, did search autosuggest change, did support volume drop? Feed learnings back into classifiers to reduce false alarms and sharpen tone.

ORM with AI isn’t spin. It’s early detection, disciplined triage, and accountable fixes—before a whisper becomes the headline.

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