Surveys, chats, calls, reviews and social posts used to live in different tools with different labels. Here they go through one AI engine and come out in one language — so a complaint means the same thing wherever it was made.
Feedback is coded into topics built for your industry from day one — grouped into themes, tagged to the touchpoint they came from, and yours to edit. Key phrases show what's behind each topic.
A single comment can praise the crew and slam the delay in the same sentence. Each part is scored on its own, then the AI adds what the person wants and how they feel.
“The crew were lovely, but we sat at the gate for two hours and the app still said 'on time'.”
“Rötarı kapıda öğrendik, uygulama hâlâ 'zamanında' diyordu.”
Flight Delay/Cancellation Notification · negative“La app seguía diciendo 'a tiempo' y nos enteramos del retraso en la puerta.”
Flight Delay/Cancellation Notification · negative“Die App zeigte noch 'pünktlich' an – von der Verspätung erfuhren wir erst am Gate.”
Flight Delay/Cancellation Notification · negativeالتطبيق كان لا يزال يقول 'في الموعد'، وعرفنا بالتأخير عند البوابة.
Flight Delay/Cancellation Notification · negativeMany open answers say little more than 'fine'. When an answer is too short or too vague to act on, the AI asks one short, relevant follow-up — in any survey channel. When it isn't, the survey simply moves on.
Personas build up from every answer a customer gives, so you see which kind of customer drives a topic. And the engine keeps looking for patterns your topic model doesn't have yet — your team decides what joins it.
Severity rules pick out feedback that needs a person — safety, accessibility, data privacy, abuse or a valued customer about to leave — and create a task for the right owner as soon as it's analysed. Abusive language is masked before it reaches a shared screen.
Because every channel is coded the same way, topics can be ranked by their impact on NPS — and the ranking holds up.