The AI Engine
We use a multi-stage analysis pipeline designed specifically for customer feedback (B2B and B2C).
1. Sentiment Analysis
We don't just look for "good" or "bad" words. We look at context.
- "The app is fast, but the support is slow."
- Result: Mixed Sentiment. Positive for "App Performance", Negative for "Support".
2. Driver Extraction
This is our propriety logic. We identify the noun or concept that is driving the sentiment.
- Comment: "I can't believe how expensive the enterprise plan is."
- Driver: "Enterprise Pricing" (Negative).
3. Urgency Detection
We categorize every comment into specific urgency levels:
- Critical: churn risks, legal threats, severe bugs ("I'm cancelling", "Data loss").
- High: broken core features, login issues.
- Medium: feature requests, minor bugs.
- Low: general praise, minor UI feedback.
4. Summarization
For every question in your survey, we generate a "TL;DR" (Too Long; Didn't Read).
- Instead of reading 100 comments, read: "Users generally like the new dark mode, but 15% report contrast issues on the settings page."
Privacy & Security
Your data is processed in a secure environment. We do use LLMs (like OpenAI) to process text, but check our Privacy Policy for details on data retention and training (we don't train on your data).