Multi-location brands · one signal, two AI jobs

Review response and crisis detection read the same signal. Most platforms sell them as two features.

Review velocity and sentiment, per location, is the only input either job needs. Inside a normal range it drafts a response for approval. Past a threshold, the same reading should stop drafting and escalate instead — one feed, two thresholds, instead of two tools watching the same number separately.

The desire, and the tool that gets bought twice

Nobody searching “reputation management software” wants software. They want two things at once: routine reviews answered without someone drafting forty replies a week, and a real problem — a bad batch, a location incident, a viral complaint — caught the morning it starts, not the week it is a headline. Those read as two different products. They are one signal at two thresholds.

1. 1. The signal — review velocity and sentiment. Per location, per day: how many reviews landed, and how they read. Podium, Birdeye and Reputation all already measure this; it is the baseline every review platform is built on.
2. 2. Job one — the routine response. Inside the location’s normal range, an AI layer drafts a response in the brand’s voice for a person to approve. This is the well-known half.
3. 3. Job two — the escalation. Past a threshold set from that location’s own baseline — not a company-wide average — the same reading stops drafting and routes to a person immediately. This half is usually a separate purchase, watching a copy of the same number.

Bought as two tools, the crisis threshold is set on a portfolio-wide average that a quiet location and a busy one share, so the quiet location’s ordinary week can read as a spike, and the busy one’s real problem can hide inside its normal noise. One feed with a per-location baseline fixes both.

Which platform to run both jobs on

• Reputation management platforms, by scale — Podium or Birdeye under fifty locations; Birdeye enterprise or Reputation past that, or with a compliance function.
• Customer review monitoring across locations — the signal, measured per location.
• Crisis detection across locations — job two, on the same signal.

Some links on the recommendation page are partner links: the vendor pays us if you sign up, your price does not change, and each button says which.

How to build it yourself, end to end

Five steps. Each names the vendor touchpoint from the platform pick above.

1. 1. Read the review stream per location, not per brand. Pull velocity and sentiment scoped to each location’s own history — a portfolio-wide number hides the location that actually has a problem.
2. 2. Set the baseline from thirty to sixty days of that location’s own history. Not a fixed number across the brand. A location that normally gets two reviews a day and one that gets twenty need different thresholds for the same word “spike.”
3. 3. Draft the routine response inside the baseline. Template per vertical, brand-voice gate before it renders for approval — the well-understood half.
4. 4. Route anything past the threshold to a person, not a draft. A volume or sentiment reading outside the baseline should stop the auto-draft entirely and page whoever owns escalations — the AI’s job past that point is surfacing the pattern, not writing the reply.
5. 5. Log both outcomes against the same location record. Response coverage and escalations-caught should live on one per-location dashboard, so a reviewer can see both jobs were fed by the same signal rather than reconciling two reports.

The baseline in step 2 is the part that takes longest to get right, and the part that degrades first without someone re-checking it as a location’s normal volume changes.

Or have it built and run

Three founding slots: a review-response agent built at a founding rate, per-location baseline included, in exchange for the right to publish the measured before-and-after.