Get more reviews on autopilot, and actually read what customers tell you

The reviews you forget to ask for, the feedback nobody reads

Reviews are won or lost on timing, and humans are bad at timing. The moment to ask is right after a customer feels the win: the order arrived, the onboarding call went well, the ticket got solved. That window is hours, not days. When asking depends on a person remembering to send an email, it mostly does not happen, and the reviews you do get skew negative because unhappy customers are the ones motivated enough to write unprompted.

Feedback has the opposite problem: you collect plenty and act on almost none of it. CSAT comments, one-star review text, exit-survey answers, and support tickets all contain the same three or four recurring complaints, but nobody has time to read hundreds of free-text responses and tally them. So the signal sits in a spreadsheet, decisions get made on the loudest anecdote from last week's team call, and the pattern that is actually driving churn goes unnoticed for months.

Both are automation-shaped problems. Asking at the right moment is a trigger plus a rule. Reading feedback at scale is exactly what a language model does well. We build the pipe that connects them, so the ask fires automatically and every answer that comes back gets read, tagged, and rolled up into something you can act on Monday morning.

The ask, fired at the right moment

The review engine we build listens for the moment a customer is happiest, then asks through the channel they actually respond to. It watches your systems for the trigger event (a delivered order in Shopify, a closed-won deal in your CRM, a resolved ticket in Zendesk, a completed project) and, before it asks, checks that this customer is a good candidate: no open complaint, a positive last CSAT score, not asked in the last ninety days. Only then does it send.

Timing and targeting are what separate a request that converts from one that annoys. We route the ask by segment, email through Postmark or SendGrid for most, SMS via Twilio for the moments that need immediacy, and we send a private satisfaction check first where it makes sense, so genuinely unhappy customers land in your support queue instead of on your public profile. Happy customers get a one-tap link straight to the platform that matters most for your business, whether that is Google Business Profile, Trustpilot, G2, Capterra, or your Shopify review app.

• Trigger detection on real events: delivered orders, closed deals, resolved tickets, completed onboarding
• Eligibility rules that suppress asks to unhappy, recently-asked, or at-risk customers
• Multi-channel sending: email via Postmark or SendGrid, SMS via Twilio, in-app prompts
• Smart routing to the platform that counts: Google, Trustpilot, G2, Capterra, app stores, on-site widgets
• A private sentiment gate that sends unhappy customers to support, not to a public one-star box
• Timed, non-nagging follow-ups that stop the moment a review is detected

Turning raw feedback into a ranked to-do list

Collecting more reviews is only half the job. The other half is reading everything customers tell you and turning it into decisions. We build a pipeline that pulls feedback from every source you have (public reviews via platform APIs, CSAT and NPS comments, support tickets, cancellation reasons, survey exports) into one place, then runs each piece of text through an LLM to do the work a human never gets to.

The model does three things on every comment. It scores sentiment so you can track the trend, not just today's mood. It classifies the comment into your own themes (shipping speed, pricing, a specific feature, onboarding confusion, a support interaction) so recurring issues surface as counts instead of anecdotes. And it extracts the concrete complaint or praise in a line, so a report reads like a briefing rather than a wall of quotes. When ten customers say checkout is confusing in ten different ways, the pipeline sees one theme with a count of ten and moves it up the list.

The output is not another dashboard nobody opens. We push it where your team already works: a weekly themes digest in Slack or email, a live board in Notion, Airtable, or Looker Studio, and an instant alert when a new one-star review or a churn-risk comment lands, so a person can respond within the hour. The public reviews can even get a drafted, on-brand reply for a human to approve and post, closing the loop without the copy-paste grind.

What it looks like in a real business

The shape changes with the business, but the wins rhyme. An e-commerce brand connects the engine to Shopify: seven days after delivery, happy buyers get an SMS asking for a Google or Trustpilot review, unhappy ones get routed to support, and review volume typically climbs several times over within a quarter because the ask finally happens every time. A B2B SaaS company triggers a G2 or Capterra request after a customer hits an activation milestone, and feeds every NPS comment and churned-account reason into the analysis pipeline to see which feature gaps actually drive cancellations.

A local or multi-location services business points the same machine at Google Business Profile per location, so each branch builds its own rating, and management gets one rolled-up view of which location has a service problem before it shows up in the star average. Across all of them, the feedback side runs quietly in the background, converting the flood of text into a short list of what to fix, ranked by how often customers actually mention it.

• E-commerce: post-delivery review asks via SMS and email, sentiment-gated, wired to Shopify and Google
• B2B SaaS: milestone-triggered G2 and Capterra requests, plus NPS and churn-reason theme analysis
• Local and multi-location: per-location Google review generation with a single management rollup
• Marketplaces and apps: rating prompts timed to positive usage, negative signals intercepted first
• Any business: a weekly ranked feedback digest that replaces the loudest-anecdote decision loop

What we build, what you own, and what it costs

We build these on the tools that fit your stack. For most clients that means n8n, Make, or Zapier for the event triggers and routing, with custom code and an LLM layer where the feedback analysis needs real accuracy and your own theme taxonomy. We connect to your existing systems through their APIs and review platforms through theirs, nothing gets ripped out. A focused version, review requests for one channel plus a basic feedback digest, is usually live in two to three weeks. A full build across multiple sources and platforms runs four to six weeks.

You own what we build. The workflows run in your accounts, the feedback data stays in your systems, and you get a clear map of how every piece fits together, not a black box you have to keep paying to understand. We run it and maintain it so a platform API change or a broken trigger is our problem, not a fire drill for your team. This engine also feeds naturally into the rest of your customer operations: the sentiment gate reuses the same triage logic as our support automation, and churn-risk comments can trigger the retention nudges we build elsewhere.

On ROI, the review side tends to pay for itself first, because more and fresher reviews lift conversion and local search ranking directly, and a half-point rating gain on a high-traffic product is real revenue. The feedback side pays off slower but bigger: it is the difference between guessing why customers leave and knowing. We would rather be honest about the limits, though. If your total volume is a handful of reviews and a few comments a month, a human can read those, and automation is overkill. And never automate a fully hands-off reply to public complaints, negative reviews are exactly where a real person should step in. The machine drafts and flags; a human decides.

• Built on n8n, Make, Zapier, and custom code with an LLM layer for analysis
• Runs in your accounts on your data, with a clear architecture map, no black box
• Focused build live in two to three weeks, full multi-source build in four to six
• We run and maintain it, so broken triggers and API changes are our problem
• Honest limits: skip it at very low volume, and keep a human on every negative-review reply