One in fifteen drafts caught before publishing: Sina Shahidi's Google review reply pipeline for five clinics

The problem

Each of the five clinics had its own Google Business Profile. He says each one generated “somewhere between thirty and a hundred reviews a month.” Each clinic was supposed to handle its own replies. In practice, he writes, “replies were getting written when someone remembered, in a tone that varied between bored and defensive, and never on a schedule.” He also writes that “Negative reviews sat there for weeks without acknowledgement.”

He priced out doing the job properly by hand. He estimated “Conservatively, an hour and a half a day across the group.” He puts that at “About forty hours a month of someone’s time, and that is only if they did nothing else.” He built an AI system instead.

The blueprint (as he describes it)

1. Pull reviews on a schedule. The pipeline pulls new reviews from each Google Business Profile through the Google Business Profile API on a schedule.
2. Classify before drafting. Each review goes through a sentiment and topic classifier “so we know what is actually being said before we generate a response.” The source does not name the classifier.
3. Draft with clinic context. The OpenAI API drafts a reply in the tone they want. The draft draws on context for the clinic, the treatment being discussed and the specific points the reviewer raised.
4. Route every draft to Slack for a human. A person approves, edits or rejects each draft before it is published. He writes: “Nothing goes live without a person clicking yes.”
5. Orchestrate in Make.com. He chose Make “because it is faster to wire scenarios in Make than to write a custom backend for something this size.”
6. Set tone per clinic, not per group. The system has clinic-specific context built in so replies sound like they came from the clinic team, “not a head-office voice.” He says this mattered more than he expected.
7. Spend the care on negative reviews. Positive reviews can get a slightly varied “thank you, see you soon”. Negative reviews need “a careful, specific, non-defensive reply that acknowledges what the reviewer said without admitting things that should not be admitted.” He says this is where the AI draft is most useful: “Having a draft to react to is faster than starting from a blank box.”
8. Favour speed. He writes: “A pretty good reply within an hour is worth more than a perfect reply three days later.”
9. Maintain the prompts. Since launch, most of his work on the system has been refining the prompts and adding edge cases.

Stated results

• Response rate “close to 100% across all five clinics, up from somewhere in the thirty per cent range.”
• Average response time “inside a few hours.”
• Team time of “around five minutes per day per clinic to approve drafts.”
• “We catch and handle perhaps one in fifteen drafts before publishing.” He says that ratio justifies keeping a human in the loop.
• He says the system “pays for itself many times over in saved time alone.” No cost or hours-saved figure is given.
• No review volumes after launch are stated. No change in rankings, bookings or ratings is stated.

Caveats

• He has since left Pulse Light Clinic. He now does growth work for law firms, medical and dental practices and clinics, and co-founded PracticeYield for UK dental and healthcare practices, so the post doubles as a portfolio piece.
• All figures are his own self-report. The forty-hours-a-month manual cost is his own conservative estimate, not a measured figure.
• “Google rewards profiles that reply quickly” is his claim, with no source given.
• His warning about fully automated reply systems going wrong is a general observation. He does not cite a specific case.
• He says that if he started again he would build the approval interface inside the CRM rather than Slack.
• This is an automated pipeline with LLM drafting and human approval. The source does not describe autonomous agents.
• Handles: LinkedIn linkedin.com/in/sina-shahidi-73344973 (verified 2026-10-27: linked from his site; his post announcing the Pulse Light Clinic Director of Marketing role is under it). X: @SinaShahidi (verified 2026-10-27: bio links to truflair.co.uk, the jewellery brand his About page says he co-founded). Bluesky, Threads, Instagram, Mastodon and TikTok are not verified and not tagged.

Sources

• https://sinashahidi.com/insights/ai-google-business-profile-review-responses/ (primary: first-person build write-up on his own site, 15 May 2026)
• https://sinashahidi.com/about/ (supporting: role, dates and PracticeYield)

---

Want agents like these working your distribution? Get It Built. Get new teardowns weekly: subscribe (double opt-in).