Your Reviews Already Told You. Now You Can Read Them.
Sentiment scoring, theme tracking, top complaints and compliments, a review-volume heatmap, staff and product mentions — plus an AI consultant that answers questions about your feedback in plain language.
The answer is in the pile nobody reads
Feedback arrives one review at a time, which is exactly the wrong shape for spotting what keeps happening.
Reading everything, learning nothing
Three hundred reviews read one at a time leaves an impression, not a finding. The pattern is in the pile, and the pile is what nobody has time for.
The rating hides the reason
A rating that slips from 4.6 to 4.3 tells you something changed. It does not tell you it was the weekend wait, the new menu, or one member of staff.
Found out far too late
By the time a recurring complaint is obvious enough to notice by eye, it has already been repeated in public for a month.
From a stack of reviews to something you can act on
Every review that arrives goes through the same five steps, so the picture is current rather than assembled once a quarter.
Reviews sync
New reviews land from your connected Google Business Profile, across every location you manage.
Read and scored
Each review is scored for sentiment and its language is detected, whatever it was written in.
Themes extracted
Recurring subjects, staff names and products mentioned are pulled out and grouped.
Patterns surface
Top complaints and compliments, volume by day and hour, and the trend line over time.
You ask questions
The AI consultant answers in plain language, citing the reviews behind the answer.
Sentiment, themes, and what keeps coming up
Not a word cloud. The specific subjects your customers return to, scored and counted, each one traceable back to the reviews it came from.
Sentiment analysis
Every review scored for tone, not just star rating — because a three-star review can be warm and a five-star one can carry a real complaint in the last line.
Top complaints, surfaced
The subjects your unhappy reviews keep returning to, ranked by how often they come up rather than by how loudly they were said.
Top compliments, surfaced
What people reliably praise. Useful for knowing what not to change, and for lifting language customers already use into your own marketing.
Theme tracking
Recurring subjects grouped into themes and followed over time, so a rising complaint is visible while it is still small.
Language detection
Reviews are identified by the language they were written in, so multilingual feedback is read and grouped rather than skipped.
Every finding traces back
Each theme, complaint and compliment links to the actual reviews behind it. Nothing is a number you have to take on trust.
When feedback arrives, and who it names
The same reviews, read across days, hours, people and products instead of one at a time.
Review-volume heatmap
Which days and hours your reviews actually arrive, laid out as a grid — the busiest windows stop being a hunch.
Staff mention extraction
Names pulled out of review text and counted, so you can see who is being thanked by name and who keeps appearing in complaints.
Product mention extraction
The dishes, services and products customers name, with the sentiment attached to each mention rather than to the review as a whole.
Rating and sentiment trend
Both lines over time, side by side. Sentiment usually moves first, which makes it the earlier warning of the two.
An AI consultant that has read all of it
You do not need to know which report to open. Ask the question the way you would ask a manager, and get an answer drawn from your own reviews — with the reviews it used attached, so you can check the working.
- What are people complaining about most this month?
- Which location has the weakest sentiment right now?
- Which staff members get named in five-star reviews?
- Has anything changed since we updated the menu?
Example questions, not saved answers. Responses come from the reviews in your own account.
What the insight panel looks like
Sample data — illustrativeSentiment split, tracked themes, and the hours your reviews actually arrive.
- Reviews analysed
- 1,284
- Positive sentiment
- 78%
- Themes tracked
- 24
- Languages detected
- 5
Themes this month
- Staff friendliness142 mentions
- Wait time at weekends38 mentions
- Value for money61 mentions
- Parking17 mentions
Review volume by day and hour
In this sample the darkest cells fall on Saturday evening, which is where a business like this one would concentrate its staffing and its asking.
Every figure and cell above is invented to show the layout. They are not customer results and not a performance promise — your own panel is built from your own reviews.
Before you decide
The things people ask us most about the analysis, answered plainly.
Where does the analysis come from?
Your own reviews, synced from the Google Business Profiles you connect. Nothing is blended in from other businesses, and every theme, complaint and compliment links back to the reviews it was drawn from so you can read the source yourself.
How is sentiment different from the star rating?
The rating is what someone selected; sentiment is what they wrote. They disagree more often than you would expect — a four-star review can contain a specific, actionable complaint, and that complaint is exactly what a rating average buries.
Does it work if my reviews are not in English?
Language is detected per review, and multilingual feedback is read and grouped rather than dropped. If you serve customers in more than one language, the themes from each are surfaced rather than lost in the smaller pile.
What can I ask the AI consultant?
Plain-language questions about your own feedback — what changed last month, what people complain about most at one location versus another, which staff members are named most often. It answers from your reviews and points at the ones it used.
Keep going from here
Where the reviews come from, what to do with what you learn, and the job this capability exists to serve.
Understand Your Customers
The goal this capability serves, framed as a job rather than a feature list.
Read moreReview Generation
Where the reviews come from in the first place — requests, QR codes and campaigns.
Read moreAI Review Management
Turning what you learn into replies that sound like you, at the speed reviews arrive.
Read moreStop guessing what customers mean. Read the pattern instead.
Connect your Google Business Profile and the reviews you already have get scored, grouped and charted — then ask the consultant whatever you want to know.