★★★★★
The Checkout Loom team were easy to work with and brilliant at the work itself. I highly recommend them to anyone who needs dependable automation.
IveraAutomation client
Verified
AI review moderation for ecommerce
AI review moderation for ecommerce that removes spam and fakes and never hides honest criticism
AI review moderation for ecommerce screens every incoming review against clear written rules, which cover spam, personal data, abuse and off-topic content, flags the patterns that suggest fake or paid reviews for a person to judge, drafts replies in your voice and groups what customers say into product themes, while publishing positive and negative reviews alike, because the FTC’s 2024 rule on reviews makes selective filtering and fake reviews a legal risk as well as a trust problem.
- Fixed price from $890 with no fee per review
- Negative reviews published as fairly as positive ones
- A person decides every doubtful review
- Platforms supportedJudge.me, Yotpo, Loox, Okendo, Shopify product reviews, WooCommerce reviews and marketplace review exports.
- Typical storeStores with hundreds to tens of thousands of reviews and a person who spends hours each week approving and answering them.
- Screens includedSpam, personal data, abuse, off-topic content, competitor mentions and patterns that suggest fake or incentivised reviews.
- Fairness ruleReviews are never removed or hidden because they are negative, and every removal cites a written rule.
- OutputsA moderation service, a review queue, drafted replies, theme reports and a rules document.
- Delivered inSix to twenty-one working days depending on the plan, with the rules agreed with you first.
The costly disconnect
Why review management turns into either a time sink or a legal risk
Reviews help conversion, and managing them by hand either swallows hours or tempts a store into choices that regulators and shoppers dislike.
Approval queues that never empty
A person reads every review before it appears, which delays the proof that helps new customers and wastes time on obvious cases. The fix is not more effort but a better rule, and that is exactly what the review moderation build for ecommerce puts in place.
Spam and personal data
Reviews with links, phone numbers or addresses expose customers and damage trust, and they need removing fast. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of review moderation for ecommerce.
Fake and paid review patterns
A burst of near-identical five-star reviews from new accounts is a sign of purchased feedback, and publishing it risks the store’s credibility. Left alone, this problem repeats with every order, which is why review moderation for ecommerce is built to handle it by rule and not by memory.
Replies that never get written
Unanswered negative reviews tell prospects that nobody is listening, and writing thoughtful replies at volume is hard. It is easy to overlook until a busy week exposes it, and by then the cost to ecommerce is already counted in refunds, hours or lost sales in the review moderation work.
Themes buried in the text
Dozens of reviews saying a product runs small is a product page fix, but nobody reads far enough to see it. The fix is not more effort but a better rule, and that is exactly what the review moderation build for ecommerce puts in place.
Rules on fake and filtered reviews
The FTC’s 2024 rule bans fake and AI-written reviews, bought reviews and incentives tied to a sentiment, with civil penalties, so what a store hides or buys matters. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of review moderation for ecommerce.
- 2024is the year the FTC finalised its rule against fake and misleading consumer reviews, including AI-generated ones.
- 92%of reviews can usually be screened and published automatically when the rules are clear, in our examples.
- 0reviews are removed for being negative, because every removal must cite a written rule.
Sources: the FTC’s August 2024 final rule on consumer reviews and testimonials as summarised by Davis Wright Tremaine, Manatt and Wilson Sonsini; this is not legal advice, and civil penalty amounts adjust each year.
What changes in your review process once every review is screened by clear rules
Most reviews appear within minutes, and your team spends its time on the few that need judgement and on replies.
How reviews are handled today
- Every review waits for a person.
- Spam and personal data are found late.
- Suspicious bursts are noticed by chance.
- Replies are written when someone has time.
- Themes live in the heads of the support team.
How reviews are handled afterwards
- Reviews that pass the rules publish within minutes.
- Spam is removed and personal data is masked by rule.
- Patterns that suggest paid reviews are held for a person.
- Replies are drafted in your voice for approval.
- Themes are reported monthly with counts and quotes.
What your review team sees
- Faster social proof.
- Safer customer data.
- Fewer fake reviews published.
- More replies answered.
- Product fixes found sooner.
What the moderation does
- Screening each review against the written rules.
- Masking personal data and removing spam.
- Flagging patterns that look like fake or paid reviews.
- Drafting a reply in your voice.
- Grouping reviews into themes.
Review decisions that stay with a person
- Deciding every doubtful review.
- Approving replies before they are posted.
- Setting and updating the rules.
- Contacting customers about problems.
- Acting on product themes.
Which stores gain most from AI review moderation
If reviews are a real sales driver and your volume is rising, screening by rule gives you speed without giving up fairness.
Stores with high review volume
Hundreds of new reviews a month are impossible to read one by one without delaying them. If this describes your business, the review moderation work for ecommerce is likely to pay back quickly, and we will tell you plainly if it will not.
Stores that use review apps
The build reads from your review app and returns decisions to it, so your widgets keep working. Owners in this position often say the biggest change is the time returned each week, and review moderation is built to deliver that first for ecommerce.
Stores that sell in sensitive categories
Health, beauty and children’s products draw reviews with claims and personal details that need careful handling. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for review moderation for ecommerce.
Stores running review requests
More requests mean more reviews to handle, and the same screening makes the programme safe to scale. We look at this situation in the audit and give ecommerce an honest view of whether the build is worth doing now or later in the review moderation work.
Stores with small support teams
Drafted replies and themes give a small team a way to engage with reviews at scale. If this describes your business, the review moderation work for ecommerce is likely to pay back quickly, and we will tell you plainly if it will not.
Stores selling in several languages
Reviews in other languages are screened with the same rules and held for a reader where needed. Owners in this position often say the biggest change is the time returned each week, and review moderation is built to deliver that first for ecommerce.
The steps through which a review is screened and answered
Every review follows the same short path, and only the doubtful ones stop for a person.
review_receivedA review arrives
The review text, rating, product, reviewer and order link are read from your review app or marketplace export. Because this trigger is handled separately from the others, a problem with it never delays the rest of the review moderation work for ecommerce.
rules_appliedThe written rules are applied
Spam, personal data, abuse and off-topic content are checked, and each match cites the rule number. You decide how much of this runs without approval, and for ecommerce we usually begin with a review step until the first results look right in the review moderation work.
pattern_checkedFake patterns are checked
Timing, wording similarity, new accounts and unverified purchases are compared across recent reviews. Anything this trigger cannot decide safely is passed to you with the record attached, so review moderation for ecommerce never acts on a doubtful reading.
review_routedThe review is routed
Clean reviews publish, masked reviews publish with the change noted, and doubtful reviews wait for a person. We test this moment on your own records before launch, so the review moderation build for ecommerce has met it before it meets a real customer.
reply_draftedA reply is drafted
A reply in your voice is drafted for negative and mixed reviews and held for approval. The timing of this step is written down in the rule table, which means you can change when it happens without asking us to rebuild anything in the review moderation build for ecommerce.
themes_updatedThemes are updated
The review is added to the monthly themes for the product, with counts and example wording. If the same event arrives twice, the second copy is recognised and ignored, so review moderation for ecommerce never repeats an action by accident.
One day of reviews followed from arrival to publication
This shows how a skincare brand handles a day of reviews without hiding anything honest.
- 01
Reviews arrive
Eighteen reviews arrive overnight for a vitamin C serum, including one with a link to a discount site and one with a phone number. The same step is repeated for every item in the batch, so the tenth record receives the same care as the first when review moderation runs for ecommerce.
- 02
The rules run
The link is removed under the no-promotional-links rule, and the phone number is masked under the personal data rule. Anything unusual at this point is flagged in plain words, which saves you from reading logs to understand what the review moderation work found for ecommerce.
- 03
A pattern is spotted
Five five-star reviews from new accounts arrive within an hour with near-identical wording, and they are held for a person. At the end of this stage you can pause, adjust a rule or carry on, so review moderation for ecommerce always moves at the pace that suits you.
- 04
An honest critique is published
A two-star review saying the serum stung her skin is published untouched, with a reply drafted that thanks her and offers help. Every step like this is recorded, so the review moderation work for ecommerce can be traced from the first input to the final result without any gaps.
- 05
A person decides
The reviewer checks the held burst, finds that the accounts have no purchases and removes the five reviews under the verified-purchase rule. If the step cannot finish, the record says why and waits for you, so review moderation for ecommerce never moves on with a half-completed task.
- 06
Themes are updated
The monthly theme shows thirty-one reviews mentioning a slight sting for sensitive skin, which becomes a product page note. You can see the outcome of this step before the next one starts, which keeps the review moderation build for ecommerce under your control throughout.
Pricing
Price your AI review moderation build
Choose a plan, tell us where your reviews live and how many you receive, add any extras, and the total and delivery date update as you go.
What AI review moderation costs and what raises the price
The build is paid once, and what keeps running afterwards is hosting and the model usage that screening needs.
Review volume
Higher volume needs more testing and heavier storage. This is one of the factors we price in writing before work starts, so the review moderation quote for ecommerce reflects your real situation and not an average.
Review sources
Each app or marketplace is another connection. If this factor is small for you, it adds little to the price, and the written quote shows exactly how much it did add for ecommerce in the review moderation work.
Review languages
A language beyond your main one needs worked examples and a reader who can judge doubtful cases. You can lower the cost by tidying this part first, and we say so plainly in the plan when the review moderation work for ecommerce would benefit.
Reply drafting
Reply drafts in several voices add tests. Anything extra that this factor creates later is quoted before it is built, so the fixed price for review moderation never grows by surprise for ecommerce.
Theme reporting
Product-level theme reports add analysis, and we explain this factor in the quote in plain words, because ecommerce should understand what drives the price before agreeing to anything in the review moderation work.
People who moderate
More than one moderator means routing rules and separate permissions for each. Changes to this factor after the quote are handled as a written change, with its own price, and never as a hidden charge on review moderation for ecommerce.
Hosting and model usage reach you as separate bills from their own providers, and the written scope estimates each.
Next step
Ready to price AI Review Moderation for Ecommerce?
AI review moderation for ecommerce reads every incoming review, removes only what breaks clear rules such as spam, personal data and abuse, flags the patterns of fake reviews for a person, drafts replies in your voice and turns the rest into themes about products, and it never hides honest criticism.
- Fixed price from $890 with no fee per review
- Negative reviews published fairly
- A person decides every doubt
Three more review jobs the same screening can carry
Once reviews flow through one set of rules, other uses follow naturally.
Review request audit
Requests are checked to make sure they go to all customers alike and are not filtered by sentiment. The job follows the same approval rules as everything else, so nothing reaches a customer of ecommerce without passing the checks you already trust in the review moderation work.
Product page improvements
Common questions and complaints become additions to descriptions, size guides and photos. If the job does not pay back, it can be switched off without affecting the rest of the review moderation build, and nothing needs to be unpicked for ecommerce.
Escalation to support
Reviews that describe a problem with an order are passed to support with the order attached. The wording and the limits for this job come from the same rule table as the main review moderation work, so one change updates both for ecommerce.
Where each review signal comes from and where it stops
Each signal has a named source and a stated limit, and every removal cites the rule behind it.
| Store object | Fields we use | Direction | Source of truth |
|---|---|---|---|
| Review text | Words, rating, language | Exact | Your review app or marketplace export |
| Reviewer | Name, account age, verified purchase | Where available | Your review app and store |
| Order link | Whether the reviewer bought the product | Exact | Your store |
| Timing and similarity | Bursts and near-identical wording | Calculated | Your recent reviews |
| Rule matches | Which written rule applied | Exact | Your rules document |
| Themes | Topics, counts, example wording | Calculated from the above | Your delivered report |
We use only the review data you already hold, and we do not scrape other sites or buy information about reviewers.
Edge cases in review moderation that we test before going live
Review rules fail at the edges, so we test the awkward cases on your own past reviews.
Harsh but honest criticism
Strong language about a product is published, and only abuse aimed at people or breaches of the written rules is removed. A rule for this case can be tightened or relaxed in minutes in the rule table, without waiting for a rebuild or a new quote in the review moderation build for ecommerce.
Reviews that mention competitors
A mention of another brand is allowed in a comparison and held when it reads as an advertisement. Handling the case early protects the margin and the reputation of ecommerce, which is worth more than the hours it takes to write the rule in the review moderation work.
Reviews in other languages
Reviews are screened in their own language and held for a reader where the model is unsure. We test this case before launch on your own records, so review moderation for ecommerce has met it before it meets a real customer on a busy day.
Incentivised reviews
Reviews linked to a free product or discount are labelled as such, as consumer protection rules expect. When the case appears, the workflow follows a written rule and records the choice it made, so you can review it afterwards if you wish in the review moderation build for ecommerce.
Reviews of the wrong product
Reviews that are really about shipping or service are routed to support and, where you wish, published on the right page. If a rule cannot settle the case safely, it goes to you with the record attached, and review moderation for ecommerce waits instead of guessing.
Health and safety reports
Reviews that describe harm are flagged for urgent human attention and are not treated as ordinary feedback. Cases like this are the reason the review moderation build for ecommerce includes a trial run, because a tidy example never shows them but real records do.
Manual approval, a review app’s filters and a built moderation layer compared
Manual approval is careful and slow, an app’s filters are fast and generic, and a built layer applies your written rules, cites them and keeps a person on doubts.
| Manual approval | App filters | Checkout Loom build | |
|---|---|---|---|
| Applies written rules to every review | Partly | Partly | ✓ |
| Cites the rule behind every removal | – | – | ✓ |
| Flags fake patterns across reviews | – | Partly | ✓ |
| Never hides honest criticism | ✓ | Partly | ✓ |
| No fee per review | ✓ | – | ✓ |
| Cost pattern | Your hours | Monthly fee | A fixed fee from $890 |
We build on top of the review app you already use, so your widgets, schema and emails keep working.
What is behind review moderation and what it costs to run
The build reads reviews from your review app and applies rules in a small service, with a language model for explanations and replies.
Review app connection
Reviews read from Judge.me, Yotpo, Loox, Okendo or your store, with decisions written back. If you already pay for something that covers this need, we use it, and we only add a new service when review moderation for ecommerce genuinely requires one.
A rules engine
Your written rules turned into checks, versioned so every change is recorded. Access to this layer is granted from your own account and can be removed at any time, so you stay in control of the review moderation build for ecommerce.
A pattern checker
Timing, similarity and purchase checks across recent reviews to spot bursts. Its limits and prices are checked again at the start of each project, because tools change their plans, and ecommerce should not pay for last year’s terms in the review moderation work.
A language model
Classifies doubtful reviews, drafts replies and groups themes, using your own key and only the fields it needs. Everything this layer does for review moderation is logged, so you can see what ran, when it ran and what it cost on any given week for ecommerce.
A review queue
A simple screen where held reviews and drafted replies wait for a person. We test the layer on your own data before launch, so review moderation for ecommerce is proven on real records and not on tidy examples.
A monthly report
Counts of reviews published, removed by rule and held, plus the themes. If the layer fails or changes its terms, the handover guide explains how to swap it, so ecommerce is never stranded by one supplier in the review moderation work.
How we keep moderation fair and consistent
A moderation system loses trust if it is arbitrary, so every decision cites a rule and is open to audit.
A rule behind every removal
No review is removed without a rule number, and the report lists removals by rule. During the month of corrections after launch, any gap in this safeguard is fixed in writing and the fix is added to the handover guide in the review moderation build for ecommerce.
Sentiment is never a reason
The rules contain no sentiment test, and a check confirms that negative reviews are published at the same rate as before. Reports on how often the safeguard fires show ecommerce whether the review moderation work is getting steadier or whether a rule needs attention.
Tested on your past reviews
The rules are run on past reviews and compared with what your team approved. Because this protection is written as a rule and not as a habit, it keeps working when staff change or the business gets busier in the review moderation build for ecommerce.
Alerts on bursts
A sudden wave of similar reviews raises an alert at once. When something goes wrong in review moderation for ecommerce, the failure is named, retried within safe limits and listed for you, instead of vanishing into a log nobody reads.
A dated decision trail
Every decision is dated and linked to the rule version and the person who approved it. This safeguard runs on every batch and not only on a schedule, so a problem in review moderation is caught before it can spread to the next record for ecommerce.
Questions answered in writing
If a decision looks wrong, the owner answers in writing and shows the rule. Each alert names the rule, the record and the next step, which means you can fix the cause from the message alone without opening any tool in the review moderation build for ecommerce.
Reviewer privacy, honesty and access in a moderation build
Reviews contain personal details and public claims, so we protect people and keep the process honest.
Personal data masked
Phone numbers, emails and addresses in reviews are masked before publication. For review moderation on ecommerce, access is limited to what the job needs and can be withdrawn in a minute, so ending the work leaves nothing behind.
No fake reviews, ever
We never write, buy or encourage fake or incentivised-for-sentiment reviews, and we flag them when we find them. Nothing in this arrangement requires you to share a password, because access is granted from your own account in a form you can revoke in the review moderation build for ecommerce.
Your credentials
API keys to your review app are yours, stored in a secret store and revoked from your side at any time. We keep no copy of your keys, records or customer details in systems that belong to us, and the handover guide shows you how to confirm that in the review moderation build for ecommerce.
Reviewer data minimised
Only the fields needed for a decision are used and they are not shared with other parties. Only the fields that review moderation needs are read, and only for as long as the task takes, which keeps the exposure for ecommerce as small as it can be.
Not legal advice
We follow the FTC rule as summarised by law firms and recommend you take legal advice on your own practices. Every access is logged in your own accounts, so you can see who or what touched a record and when, without asking us for a report in the review moderation build for ecommerce.
Retention you choose
Decision logs are kept for a period you choose and then deleted. If a key is ever lost, it can be replaced in minutes without rebuilding the review moderation work for ecommerce, because the rules do not depend on it.
What you receive with your AI review moderation build
You finish with working moderation, a review queue and the documents to run them without us.
- A written rules document, agreed with you, listing exactly what may be removed or masked.
- A moderation service connected to your review app.
- Personal data masking and spam removal by rule.
- A pattern check for bursts that suggest fake or paid reviews.
- Drafted replies in your voice for approval.
- A review queue with the rule or reason beside each held review.
- A monthly themes report with counts and quotes.
- A runbook for each flow, opening with how to switch automation off.
- Written answers from the owner to any question about a decision.
Verified client reviews and the safeguards behind our moderation builds
Below are clients’ own words, unedited, and then the three safeguards that every moderation build we deliver carries.
- 0reviews are removed for being negative.
- 100%of removals cite a written rule.
- 1person decides every doubtful review.
Safeguards follow the FTC’s published rule as summarised by law firms and are not legal advice.
★★★★★
Everything was configured exactly as discussed, and the team made sure the automation was stable before delivering it. It has been a huge time-saver for our whole team, and it runs without any manual work.
BellamyUnited States
Verified
★★★★★
This was excellent automation work, and it is clear that the team has strong experience with AI and automation. I highly recommend the Checkout Loom team to other businesses.
FinnUnited States
Verified
★★★★★
I am genuinely impressed with the work the Checkout Loom team did on my AI and automation project. They are extremely professional, skilled, friendly and easy to work with, and I am delighted with the final result.
DavidUnited States
Verified
★★★★★
The Checkout Loom team built an automation that simplified my workflow and worked perfectly from day one. Communication was excellent, delivery was fast, and I would happily recommend the service.
RebeccaUnited States
Verified
★★★★★
They set up my automation remarkably fast and executed everything perfectly. Communication and service were top tier, so I could not be happier with the results and I highly recommend the Checkout Loom team.
AbbieGermany
Verified
★★★★★
The automation was set up in a clean and practical way by the Checkout Loom team. It saved me a lot of manual work and made the whole process much easier.
JanoUnited Kingdom
Verified
★★★★★
I highly recommend ordering from the Checkout Loom team. I had slow responses and poor communication with other providers, but this team was great and I am very happy with their service.
HarmetUnited States
Verified
★★★★★
I am a returning customer because my first AI automation workflow worked so well. Once again everything was built exactly as requested, and the whole process was reliable from start to finish.
WilliamUnited States
Verified
How review moderation is built from rules to live queue
We write the rules with you first, because the rules are the heart of fair moderation.
- 01
Read your reviews
We read a year of reviews and your past approvals to see what you remove and why. A short summary of the step is added to the handover guide, so the way review moderation was built for ecommerce is never lost.
- 02
Write the rules
We draft a short rules document, and you approve it line by line. You see the result of this step in writing before the next one begins, which keeps the review moderation build for ecommerce on schedule and under your control.
- 03
Build and test
We turn the rules into checks and test them on past reviews against your team’s decisions. If this step shows a problem, we say so at once and propose the fix, and the date for review moderation is moved in writing rather than quietly for ecommerce.
- 04
Run it in quiet mode
The checks run on live reviews without changing anything while you compare them with your own judgement. Nothing in this step touches your live customers, so ecommerce can review it calmly and ask for changes before anything goes public in the review moderation work.
- 05
Turn on publishing
We switch on automatic publishing and masking, with doubtful reviews going to the queue. The step has a clear finish line, which means you always know whether the review moderation work for ecommerce is on time or running behind.
- 06
Hand over the moderation
The runbooks and connection details come to you in writing, with written answers to later questions. Questions that arise here are answered in writing, so there is a record of every decision made about review moderation for ecommerce.
Estimate the hours review moderation could return each month
Enter how many reviews you receive, how long each takes you and how many replies you write, and the estimate shows the hours a screened and drafted process could return.
Hours a month spent today–
Hours returned each month–
Hours returned each year–
The estimate assumes screening removes most approval work and drafting halves the reply time, which is an assumption and not a promise.
AI review moderation for ecommerce questions, answered in full
These are the questions store owners ask about review management, fake reviews, rules and cost before they order a build.
What is AI review moderation for ecommerce?
AI review moderation for ecommerce screens each review against written rules for spam, personal data and abuse, flags patterns that look like fake reviews for a person, drafts replies and groups what customers say into product themes.
How does it differ from ecommerce review management software?
Ecommerce review management software collects, displays and requests reviews, while our build adds a moderation layer on top of the app you already use, with written rules, fake-pattern checks, drafted replies and themes at a fixed fee.
Can it hide negative reviews?
No, it is designed not to, because a rule that removes reviews for being negative is unfair to shoppers and risky under the FTC’s 2024 rule, so every removal must cite a written rule that has nothing to do with sentiment.
Do you offer fake review detection software?
Our fake review detection software checks timing, wording similarity, new accounts and unverified purchases across recent reviews and holds suspicious bursts for a person, but it cannot prove intent, so a human makes the final call.
What are AI review analysis tools able to find?
AI review analysis tools, and review sentiment analysis ecommerce stores run, group reviews into themes such as sizing, quality or delivery with counts and example wording, and customer review analytics of this kind feed back into product pages.
Can it reply to reviews automatically?
Review response automation here means drafted replies in your voice that a person approves, since replying automatically to sensitive complaints can do harm, and you can switch on automatic thank-you replies for simple five-star reviews if you wish.
Is automated review moderation allowed?
Automated review moderation is a normal practice when the rules are clear and applied fairly, and product review moderation and review content moderation of this kind also masks personal data, but you should take legal advice on your own practices.
Which product review software ecommerce stores use will it work with?
It works with Judge.me, Yotpo, Loox, Okendo, Shopify product reviews and WooCommerce reviews, and we confirm your app’s connection options at the start.
How soon will it be live, and what do you need first?
You will have moderation in quiet mode between five and twenty-one working days after you order, depending on the plan and the speed you choose. To begin we need read access to your review app and an export of the last year of reviews.
What do I still pay each month for review moderation?
Only the hosting and any model usage you choose, billed to you separately by their providers.
Publish reviews faster, remove only what breaks the rules and answer the rest well
Choose a plan in the pricing calculator and continue to checkout, or request a quote describing your reviews and how you handle them today. We answer in writing within a working day with a fixed price and a date.
- Fixed price from $890 with no fee per review
- Negative reviews published fairly
- A person decides every doubt