Filtra.AI
Analyze reviews with AI
Understand the meaning behind your reviews without reading every comment manually.
What AI adds to review analysis
AI processes volume that humans cannot read line by line. It classifies reviews by theme and sentiment, groups similar cases and highlights what changed between periods. The team starts from the prioritized list instead of a random sample.
- Theme and sentiment classification at scale.
- Grouping of similar complaints across units.
- Period comparison and trend detection.
- Prioritization by frequency and severity.
A method that works
Start with a taxonomy that reflects your operation: waiting time, communication, price, quality, staff, result. Review a sample of classifications at the beginning and adjust. Then combine AI output with business context: a rare complaint about safety may matter more than fifty comments about parking.
From analysis to decision
End each cycle with a short action list: what to fix, who owns it and when. Measure the same theme in the next period to check whether the action worked. The value of the analysis is the decision it triggers, not the dashboard it fills.
What to look for
Use the themes below as management signals: frequency, customer impact, recurrence and the team responsible for the next action.
Frequently asked questions
Can AI replace manual review analysis?
It can handle the volume and find patterns, but context, priorities and public responses still need human judgment. AI accelerates the reading; the team decides.
How accurate is theme classification?
It depends on the taxonomy and the language of the comments. With clear categories and a reviewed sample at the start, accuracy is usually enough for prioritization.
Does it work with reviews in different languages?
Yes. Multilingual analysis is one of the main advantages for operations that serve international customers.
What do I need to start?
A few months of reviews and comments plus a clear list of themes that matter to the business. The taxonomy is the foundation.
How do I know the analysis is working?
When the prioritized themes become actions and their recurrence drops in later periods. Track fixes, not only sentiment scores.