This is an example of an agent your team can build in Sylvie: a Review Responder agent that reads incoming reviews across your marketplaces, app stores, local listings and product pages and drafts a reply to each one that sounds like your brand and gets the facts right. It runs on your brand brain, so it knows your tone of voice, your real product specs, your returns and warranty policy, and exactly which topics must go to a human instead of a polite apology. You build it once, and a backlog nobody owns turns into a daily habit that takes minutes.
Sylvie does not ship this agent pre-built. It is an example of an agent your team can build with Sylvie, powered by your brand brain, so it runs your workflow the way you actually run it.
Review Responder is an example of what a marketing team or agency can build in Sylvie, not a product we ship you finished. It watches the review streams that actually matter to a brand (Amazon, Google Business Profile, Trustpilot, the App Store and Play Store, your own PDPs) and drafts a response to each one: thanks that do not read as automated, answers that cite the correct spec or policy, and a documented route for the complaints that need a real person.
Reviews are the copy your buyers read most and the copy nobody is assigned to write. Responses drift between whoever has time, the four star review asking a simple sizing question goes unanswered for six weeks, and the one star review that is really a safety report gets a template. Response rate and response speed also feed marketplace ranking and local visibility, so silence is not neutral, it is a cost you are paying quietly.
The agent starts from your brand brain, which is what separates a real reply from a canned one. The brain holds your tone of voice (how you apologize, whether you use first names, what you are never allowed to promise), your product facts and specs so an answer about battery life, sizing or ingredients is correct, your returns, warranty and shipping policies, your past responses so you stay consistent with what the brand already said, and your escalation rules covering safety, injury, legal, privacy and press.
From there it runs the workflow end to end inside Sylvie: it pulls new reviews, classifies each by product, theme, sentiment and language, drafts a reply in the right voice and market language with the source fact attached, then either queues it for approval or posts it inside the limits you set. Anything hitting an escalation rule goes to a named owner with the review, the order context and a holding reply ready. Recurring themes are rolled back into the brain, so product and content teams see the pattern instead of hearing about it a quarter later.
Four star reviews with a question, three star reviews with a fixable complaint and the long tail of five star mentions all get a reply, not just the fires. Response rate stops being a metric you avoid in the QBR.
Because it reads product facts and policies from the brand brain, a reply about sizing, compatibility or the returns window quotes the correct answer with the source attached. You stop shipping vague apologies where a buyer needed one concrete number.
The Amazon reply, the Google Business Profile reply and the App Store reply all sound like the same company. Whoever is on rotation this week no longer changes how your brand speaks in public.
Safety, injury, legal and privacy topics are routed to a named human with full context in minutes instead of receiving a template. The reviews that could become an incident are the ones a person always sees.
Reviews come back answered in the reviewer's language, with the tone adapted to that market rather than machine-translated from English. Regional listings get local-quality responses without a local hire.
Recurring issues are clustered and fed back into the brand brain as evidence, with volume and dates attached. The zipper complaint reaches the product team while it is still cheap to fix.
Reviews pile up fastest exactly when a brand is doing well. Here is where a Review Responder agent you build in Sylvie proves itself.
An agency picks up a retail account whose reviews have gone untouched across four marketplaces for two years. The agent works the backlog by theme and priority, drafting replies for approval in batches. Response rate goes from single digits to near total inside a fortnight, and the account team has something concrete to show at the first review.
A new apparel drop starts pulling three star reviews that all say the same thing about fit. The agent recognizes the pattern, answers each reviewer with the real measurement guidance, and flags the cluster with volume and dates. The PDP sizing copy and the size chart get fixed within days instead of the following season.
Every location has its own Google Business Profile, its own reviewers and its own manager who does not have time. The agent drafts location-specific replies that reference the right store, hours and policies, routed to each manager for a one-click approval. Every location responds inside a day, and local pack visibility improves alongside it.
A shaky update triggers a run of one and two star reviews about a specific crash. The agent replies with version-aware answers that name the fix and the release it landed in, and escalates the reports engineering has not seen. Users update their reviews because they were actually answered, and the store rating climbs back.
Buried in a normal week of feedback is a review describing a burn, an allergic reaction or a child hazard. The agent stops it, routes it to the named owner with the review text, order context and a careful holding reply drafted. It reaches a human in minutes rather than receiving a cheerful template that would have made everything worse.
The agency has to explain what happened to sentiment this quarter and what was done about it. The agent produces a rollup of volume, themes, response times and the issues escalated to product. The QBR runs on evidence instead of impressions.
Templates match on star rating and say nothing specific, which reviewers and buyers both notice. This is an agent you build on your own brand brain, so it answers the actual question with your real spec or policy, in your tone, and knows which reviews it should not touch at all. The output reads like a person from your company wrote it, because it was built on what your company actually knows.
You build it. Review Responder is an example of what a marketing team or agency can assemble in Sylvie, not an out-of-the-box product. We help you stand it up on your brand brain and connect it to the review sources you care about, so it responds under your rules and your approval flow.
It supplies everything a good responder needs and a generic tool lacks: tone of voice and the phrases you never use, product specs so factual answers are right, returns, warranty and shipping policy, your past responses so you stay consistent with what the brand already said publicly, and the escalation rules that decide what a human must handle. That context is why the reply lands as helpful rather than as an obvious bot.
Only if you decide it should. Because you build the agent, you set the policy: everything queued for approval, auto-posting for straightforward positive reviews with a human on anything under four stars, or per-client rules if you run several brands. Thresholds can change over time as trust builds, and every draft and post is logged.
You write those escalation rules into the agent, and they take priority over everything else. Reviews touching injury, safety, legal threats, privacy, press or a possible recall are never auto-answered: they go to a named owner with the review, the customer context and a holding reply prepared. The point of building it yourself is that you decide where the line sits.
Yes, and that is the normal setup for an agency. Each client gets its own isolated, permission-aware brand brain, and the agent built on it only ever draws on that client's voice, specs, policies and past responses. Nothing leaks between accounts, and each agent can follow that client's own approval and escalation rules.
Book a demo and we will map the workflows worth turning into agents, and show how each one runs on your brand brain.
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