AI Review Replies

AI Review Replies watches the review sources connected to the account, and when a new review arrives it drafts a response in the client's voice. Depending on how you configure it, that draft either waits in a queue for someone to approve, edit and post, or it is posted automatically when the review meets conditions you have defined.

The job it does is consistency and coverage. Most businesses reply to reviews in bursts, badly, and almost never to the positive ones. An AI drafter means every review gets a response that acknowledges what the reviewer actually said, within hours rather than never. It reads the review text, so a five-star review that praises a specific staff member gets a reply that mentions that, not a generic thank-you.

The mental model that keeps you out of trouble is a junior staff member drafting replies for a manager to sign off. They are fast and they get the tone right most of the time, and you would not let them post unreviewed to a one-star complaint. The sane default is automation for the clearly positive and human review for everything else. The risk here is reputational and public, which makes it unlike almost every other AI surface in the platform: a bad reply does not sit in one contact's inbox, it sits under the client's business listing permanently.

Sources and coverage

• Which connected review sources are monitored, such as Google Business Profile and Facebook
• Whether all new reviews are drafted for, or only those with text
• Whether historic unanswered reviews are drafted for in a backfill

Tone and content

• Voice and formality, and the sign-off used
• Length guidance, which for public replies should be short
• Whether the business name, staff names or service names may appear
• Business context and knowledge the drafter may draw on when a review asks a question
• Prohibited content: claims, admissions, anything about another customer, any discussion of specifics in public

Rules by rating

• Separate behavior per star band, typically auto-post for four and five stars and approval-only below
• Keyword or topic conditions that force human review regardless of rating, such as mentions of safety, legal action, refunds or named staff complaints
• Whether a reply invites the reviewer to continue the conversation privately, and through which channel

Approval and workflow

• Who is notified when a draft needs approval and how
• Delay before auto-posting, which gives a human a window to intervene
• Escalation when a negative review arrives: task creation, assignment, internal notification

Oversight

• A log of what was drafted, what was edited and what was posted
• Visibility of reviews that received no reply

1. Connect and verify the review sources first, and confirm the account actually has permission to post replies on each one. Read-only access produces drafts that can never be sent.
2. Agree the policy with the client in writing before configuring anything: which ratings may be answered automatically, who owns negative reviews, and what the business will never say in public.
3. Decide the voice. Read the client's existing replies if they have any, and the tone of their marketing, and match it.
4. Set the length guidance short. Long public replies read as defensive, and that is the most common tone failure.
5. Write the prohibited list explicitly: no admissions of fault, no clinical or outcome claims, no mention of another customer, no discussion of case specifics, no offers made in public.
6. Attach only the business context the drafter needs to answer a factual question in a review, such as hours or whether a service is offered.
7. Configure the rating bands. Start with approval required for everything, including five stars, so you can see the quality before you let anything post itself.
8. Add the forced-review keyword conditions for safety, legal, refund and staff-complaint language.
9. Configure notification and assignment so a negative review creates a task for a named person, not just an alert in a feed.
10. Draft against at least ten real past reviews, including the worst one the client ever received, and read every draft.
11. Fix tone by adjusting voice and prohibitions rather than by editing individual drafts. Re-draft the same set to check you improved it.
12. Turn on auto-posting for the top rating band only, with a delay, once the client has seen and approved a batch of drafts.
13. Never enable automatic posting for low ratings. Make this a standing rule in your own build standard, not a per-client decision.
14. Review the log weekly for the first month, watching for reviews that got no reply at all as well as replies that went out wrong.

This module sits on top of the Reputation and Reviews capability and leans on Workflows for escalation and on the AI knowledge for factual answers. It is usually the last AI surface you turn on, because it is the most public.

Post-visit review request with managed responses

A workflow requests a review a set time after an appointment. New five-star reviews are drafted and auto-posted with a short, specific thank-you. Anything three stars or below creates a task for the practice manager with the draft attached, so the client controls the sensitive replies while still getting them out the same day.

Multi-location coverage

A client with several locations has separate listings and separate volumes. The drafter covers all of them consistently, which is the only practical way a small team keeps up, while location managers approve their own negative reviews.

Negative review recovery loop

A low review triggers a workflow that creates an urgent task, notifies the owner, and where the reviewer can be matched to a contact, opens a private conversation. The public reply stays short and invites the conversation offline; the real resolution happens in the conversation view with a human.

• Automatic replies to negative reviews are a mistake. This is the single rule to hold. A drafted reply to a complaint, posted unread, is how a one-star review becomes a screenshot.
• Public replies carry legal and regulatory exposure. Regulated industries have rules about what can be said publicly and about acknowledging that someone is a customer at all. In healthcare in particular, confirming a reviewer was a patient can be a disclosure problem. Get this reviewed by the client, not by you.
• The drafter does not know what happened. It only has the review text. It will apologize for things that did not occur and thank people for services they did not receive if the review is vague or mistaken.
• Posting permissions break quietly. Expired or revoked access to a listing stops replies going out while drafts keep accumulating. Check the connection, not just the queue.
• Not every platform allows programmatic replies. Coverage differs by review source and changes. Confirm per source rather than assuming.
• Generic replies at scale look worse than silence. If every reply has the same shape, readers notice. Keep them short and make sure the drafter is actually referencing the review's content.
• Fake and mistaken reviews need a different path. Replying is not the remedy; disputing with the platform is. Route these to a human, not to a draft.
• Approval queues rot. An unwatched queue means weeks of unanswered reviews. Assign an owner and a cadence, and monitor unanswered volume as a metric.
• Never incentivize or filter reviews. Review gating and incentives violate the major platforms' policies and are a separate problem from drafting replies. Keep the request workflow clean.
• Edits are the signal. If the approver rewrites most drafts, the voice configuration is wrong. Fix the configuration rather than accepting the rewriting as the process.