AI for Customer Support · Solutions

See which complaints keep coming back, and where.

AI Feedback and Review Analytics reads reviews and survey answers from sources you may use. Comments are grouped by theme, and counts show each theme by product, branch and month. Each theme links to real comments. A person drafts and approves every reply.

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What it is

Feedback sits in many places. Marketplace ratings, Google reviews, the survey sent after delivery and the notes agents type at the end of a call are read by different people, if at all. Patterns hide in the gaps.

GullySystem sets up a reading layer over those sources, so that a comment about a late courier and a comment about slow delivery land under the same theme. You review the themes before anyone acts. Wrong themes are easy to fix.

Where it goes wrong today

The problems this is meant to ease.

Feedback scattered across places

Marketplace ratings, map reviews, survey forms and agent notes are read by different people. Nobody sees that three channels describe one fault.

Complaints counted by feeling

The loudest review sets the mood of the meeting. Without counts by month and branch, a rare angry comment gets the attention that a quiet, repeated problem deserves.

Branches compared unfairly

One outlet has many more reviews than another, and the averages are set side by side as if they were equal. Managers argue about numbers that were never comparable.

Public reviews left unanswered

Reviews wait for weeks because no one owns them. When someone finally replies, the facts are missing and the answer reads like a form letter.

Fixes never checked

A supplier was changed or a delivery step was rewritten, but nobody looked at whether customers stopped mentioning the problem afterwards.

Surveys without follow-through

Customers answer a post-purchase form and hear nothing. Free-text answers pile up in a spreadsheet that nobody has time to read from top to bottom.

Inside the module

What AI feedback and review analytics does.

Authorised sources, one reading

Reviews, survey free text, chat transcripts and ticket closing notes are pulled together, each keeping its source, date and the product or branch it concerned. Only sources your business may use are connected, and original wording stays intact.

Themes you can name

Comments are grouped into themes such as late delivery, size runs small or app logs me out. You can rename, merge or split any theme. Issues that keep recurring rank first.

Satisfaction trend by product and service

Counts and star averages run by month, product and service line. A complaint that rose after a supplier change shows as a bend in the chart, not as a feeling in the room.

Locations and periods side by side

Two branches, or the same branch before and after a refit, can be set next to each other. Differences in volume are shown so a small branch is not judged by a handful of comments.

Evidence under every number

Open a theme to read its comments. One click shows the original text, the reviewer’s star rating and the date, because a count means little without the words behind it.

Sentiment as a rough guide

Each comment gets a rough leaning. Sarcasm, mixed languages and short remarks such as fine can be misread, so read a sample of each theme before trusting its label.

Reply drafts for your approval

For a review waiting for an answer, a draft is prepared from your own tone notes and the facts on file. Nothing is posted until a named person edits and approves it.

Suggestions for the owner

Each month the leading themes arrive as a short list, with a suggested owner such as the warehouse, the app team or the branch manager. Nobody is assigned automatically, and every suggestion is only that.

Where it gets used

What this looks like in a working week.

A multi-branch restaurant group

Reviews from each outlet are grouped into waiting time, portion size and billing mistakes. The owner sees which outlet gets the same complaint month after month.

An online retailer

Marketplace reviews and return reasons are read together. A rise in size complaints on one product line is shown to the buying team with the original comments attached.

A hospital or clinic

Post-visit survey comments are grouped into waiting time, billing and staff manner. Clinical remarks are left for a qualified person and are never summarised into a score.

A software company

App store reviews and support notes are matched to releases. The product head sees which update brought a spike in login complaints and reads the exact words.

A hotel

Reviews from booking sites are split by room, breakfast and front desk. Managers compare the same month across two years and answer pending reviews from approved drafts.

Who touches it

The people who open this every day.

Customer experience manager

Reads the monthly theme list. Compares branches and products, and decides which two or three problems go to the people who can fix them this quarter and which can wait.

Product or operations head

Wants the evidence first. Opens a theme, reads the real comments, and takes them to the supplier, the warehouse or the app team with dates attached.

Support lead

Looks for repeat causes. Sees which themes also drive tickets, so a confusing invoice layout or delivery message can be fixed once instead of answered daily.

Owner or branch manager

Checks one page monthly. Sees whether last month’s fix changed what customers say, and which branch the complaints cluster around, before the next review meeting with the staff.

How it is set up

From first conversation to daily use.

Discovery of where feedback lives
List every place customers speak: review sites, app stores, survey tools, the helpdesk and agent notes. For each one, record who owns the account and what its terms allow.
Data readiness for comments
Export a sample of reviews and survey answers, mark the product and branch on each, and remove anything that does not belong to authorised sources before any reading begins.
A trial run on live comments
Start the theme sorting with a single product or branch. The first theme list is a draft, and the team merges, renames and drops themes until they match how people talk.
Reading the output alongside its users
The customer experience lead and a branch manager sample the comments under each theme. Misplaced ones get marked. Weak sentiment labels are noted and hidden if needed.
Going live with a monthly rhythm
Switch on the monthly theme list, the branch comparison and the reply drafts for one team first. Approval of drafts stays with a named person from the first day.
Reviewing afterwards
After a quarter, look at which themes fell after a fix and which did not. Retire themes no one needs and add sources only where the terms permit.

Measuring it

How you would know it is working.

Record where you stand before it goes live, so the change is measured against your own figures.

Review ratings are the average star rating by product and branch, taken from the review sites for the months before go-live.
Feedback response time measures how long a review waits for its reply, using dates your team already records.
Complaint frequency counts comments in each theme per month, against a hand-read sample from the previous quarter.
Share of reviews unanswered is the number of reviews with no reply at the moment go-live begins, counted from the sites themselves.
Theme coverage records how many comments land under no theme, which shows the team where the theme list needs work.

Used responsibly

Where a person stays in charge.

Only sources you may use

Reviews and surveys are collected through each site’s own interface or an export you provide. Sources whose terms forbid automated reading are left out, and the list of connected sources is shown to your team.

Replies are drafts

It writes a draft for a person to edit and approve. It never posts, and a reply to a serious complaint, a medical remark or a legal threat is held for a senior reader.

Personal details and privacy

Reviewers’ names and contact details are shown only where the source makes them public, and survey answers follow the consent given to the respondent. Retention rules for such data vary between countries, and your own lawyer should settle them. This tool only prepares data. Legal advice is outside its job.

Limits of the reading

Counts from a small number of comments are marked as thin. Sentiment labels are a guide, and the recommendations point to where to look, not to what a fix would cost or achieve.

How it fits

It is one part of a system, not an island.

A module earns its place by what it passes to the next one. These are the connections we set up most often.

Review sites and app stores you already use
Survey tools and post-purchase forms
Helpdesk and ticket management
CRM customer records
Dashboards and KPIs

Already built in

Products that include it.

Built for you

How we would put it in.

Common questions

Questions owners ask about AI feedback and review analytics.

How many comments are enough?

Counts need volume to mean much. Below a few dozen comments a month, reading them yourself is quicker and more honest than any theme list, and the software would only add a dashboard.

Does it reply to reviewers?

It does not post replies. Your staff write every public reply. It can prepare a draft and list reviews still waiting, so the answer is better informed, but a person approves each one.

Which languages can it read?

Major languages are usually handled, but text that mixes two languages in one sentence is harder to group accurately, and short slang is harder still. We try a sample of yours first. Where reading quality is weak, you hear it plainly before any counts are trusted.

Can it tell me what to fix?

It shows what is mentioned and rising. Why it happens, and what a fix would cost, are judgements for your operations and product people, who know the supplier, the system and the budget.

Will it connect to our review sites?

Some review sites and app stores offer an API, and others only send email alerts or allow an export that someone must upload by hand. We list what each allows first.

Which data must we hand over?

Access to the places where feedback already sits, plus the product and branch list so comments can be tied to something. Order numbers help but are not required.

How accurate are the themes?

They will be wrong sometimes, mostly on sarcasm, mixed languages and very short remarks. You read a sample of each theme and correct it, and the corrections are kept for later months.

How much work does it need?

Setup time goes mainly on connecting sources and agreeing the first theme list with your team. Afterwards it is a monthly read, and the number of connected sources sets the running effort.

Who needs it most

Businesses that ask for this.

Next to it

Modules that work with it.

Talk to us

Tell us how you handle AI feedback and review analytics today.

A spreadsheet, a register or another system: say which, and we will tell you plainly what is worth changing.

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A reply within one business day
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Start with your requirements

Seven short steps that tell us what you need. An engineer reads them before getting in touch.