Business Tools
Build a customer feedback and review management system
Kliv is an AI app builder. Describe the feedback and review system you want — verified orders, moderation, photos, tags, search, replies, and reports — and it builds it around your data.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a review widget you paste onto a site — it’s an AI that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the database, the moderation queue, the customer screens, the team views, and the reports. What you get is a real app that’s yours. You can use it, change it later by asking, and keep the feedback in your own system. It isn’t a template.
On this page, that app is a customer feedback and review management system. Kliv builds all kinds of web apps; this is one example.
Why build your own review system?
Review widgets usually treat feedback as decoration for product pages. For a product team, reviews, support threads, and post-purchase surveys are research data. A complaint about lid fit on a five-quart pan matters more when you can connect it to product line, order, and batch.
Kliv builds feedback as data you own. Reviews link to the orders behind them, support threads and survey answers can live in the same database, and the team can ask questions by product, batch, month, or theme.
What the system remembers
Feedback is useful when it stays connected to the thing it is about.
Reviews and orders
A review links to the order that earned it. “Verified buyer” comes from a matching order record, not from a manual badge.
The moderation queue
New reviews and photos wait for approval, rejection, or reply before publishing. The storefront only shows what passed your policy.
Themes and tags
Tags such as “lid fit,” “seasoning,” and “shipping damage” can apply to reviews and support threads, so one chart shows where a product line hurts.
Responses and follow-ups
Replies are tracked with author and status. A low rating can become a follow-up task, and answered reviews remain part of the public record.
The searchable corpus
Reviews, surveys, and support threads can be indexed together. The team can ask what customers say about enamel on dark colors and get an answer drawn from actual feedback, with transcripts kept.
Built for how feedback arrives
Customers leave feedback in more than one place. Kliv can keep those signals connected.
Post-purchase requests
Fourteen days after delivery, a scheduled job can send the review request on your own email template, timed from shipment instead of sale.
Photos in moderation
Customers can attach photos of the product in use. Images wait in the same moderation queue as text and publish only after approval.
Search by meaning
“Handle gets hot” and “burned my hand” can be found as the same complaint when you search by meaning.
Weekly digest
A summary of the week’s reviews, ratings by product line, and unanswered items can land in Slack every Monday morning.
Metrics from your rows
Average rating by product, response time, and review volume per launch are measured from your own records.
An example: Priya’s cookware brand
Here’s how one brand might use it. It’s only an example — you would describe your own products, review policy, tags, and team workflow.
The review request sends after delivery
Priya’s cookware brand asks for a review 14 days after a Dutch oven ships, using the brand’s email template.
Verified reviews link to real orders
A customer reviews the five-quart oven. The review joins to the order, so the “verified buyer” state is based on the purchase record.
Photos wait for approval
The customer uploads a photo. It waits in the moderation queue with the review, where the team can approve, reject, or reply before anything reaches the storefront.
Theme tags show a product issue
Several reviews and support threads mention lid fit. The tag appears across feedback sources and points the product team to a casting issue.
Semantic search finds related language
The team searches for “handle heat” and finds reviews that say “burned my hand.” Different words surface the same problem.
The Monday digest keeps follow-up moving
Slack gets a weekly summary of ratings by product line and unanswered reviews, so low scores do not sit untouched.
Describe the feedback system you want
Kliv builds from your description, so the more detail you give, the closer the first version. Include sources, moderation policy, tags, search, reports, and customer actions. Here are three to build on:
Cookware review hub
Verified orders, moderation, themes, and search.
“Build a review system for our cookware brand. Link reviews to verified Shopify orders, hold text and photos in moderation before publishing, tag feedback by product line and theme such as lid fit or shipping damage, and let the team search reviews, surveys, and support threads by meaning.”
Service feedback tracker
Per-visit ratings with follow-up tasks.
“Build a feedback tool for our cleaning company. Clients should rate each visit from 1 to 5, low scores below 3 should open a follow-up task for the account manager, and managers should see trends by crew, client, and month.”
App feedback board
Requests, votes, status, and shipped notices.
“Build a feedback board for our software. Users should file feature requests, vote on requests, see planned and shipped statuses, and get an email when something they requested ships.”
How it comes together
Moderation is a recorded decision. Approve and reject are platform operations. Each decision records the reviewer. Your policy decides what passes; the queue makes sure nothing skips review.
Agents can work inside the same boundaries. A team-facing agent can read the whole feedback corpus. A customer-facing agent can read only that customer’s own orders and reviews. Both conversations can be stored as transcripts.
Published reviews are readable. Product pages can prerender published reviews for search engines and AI assistants, so the reputation you earn is visible outside your app.
Feedback tools often grow into dashboards, queues, and focused internal tools. Here’s how teams build the rest.
Common questions
What exactly is Kliv?
Kliv is an AI app builder for custom web apps. You describe the feedback system you need, it builds the app, and you own the result.
Do I get a real app, or a template?
A real app. Kliv builds the database, moderation workflow, customer screens, team views, search, and reports around your feedback process.
Can I change it after it’s built?
Yes. You can add tags, change moderation steps, add a report, import another feedback source, or change customer permissions later.
Can we stop fake reviews?
You can reduce them with verified-buyer links and moderation. A verified review must match an order, and unverified submissions can be held, labeled, or rejected before publishing.
What happens to a bad review?
Your policy decides. Many brands publish it with a reply. The moderation decision and reviewer are recorded either way.
Can customers edit or delete their reviews?
Yes, within rules you set. A review is the customer’s own row, editable under your policy, and earlier versions can be kept for disputes.
Does the agent talk to customers or only to us?
Either. A team-facing agent can read the whole corpus. A customer-facing agent is limited to the signed-in customer’s own orders and reviews.
Can we bring in reviews from our old tool?
Yes. Reviews are ordinary rows, so an export from an old platform can import into your schema.
Can photos be moderated too?
Yes. Photos can wait in the same queue as review text and publish only after approval.
Will reviews show up in search?
Yes. Published review pages can be prerendered and readable to crawlers and AI assistants.
Do I need a separate CMS or review widget?
No. The app can be its own admin for reviews, tags, replies, pages, and reports.
Can I move off Kliv later?
Yes. The code syncs to your own Git repository, so leaving is a git pull rather than a rebuild.
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Turn feedback into a system
Describe your products, review policy, tags, search needs, follow-up rules, and reports in detail. Kliv builds the feedback system around them.