Kimola lets you scrape and collect feedback from 30+ channels, then analyze, classify, and summarize it all—from product reviews and survey responses to chats and call-center conversations. Whether it’s e-commerce reviews, CSAT responses, or support tickets, Kimola transforms raw feedback into structured insights you can act on.
Trusted by clients across 90+ countries
Trusted by clients in 90+ countries, 1000+ businesses, Kimola is used by global enterprises like P&G Singapore, Pizza Hut Spain, Michelin Brazil, Honda Netherlands, Costa Coffee UK, Lufthansa Airlines as well as growing SMBs including Plan3, Astropay and Blueberry Markets. Our users range from product and #CX teams to mobile applications, museums, restaurants, and even pilates studios—proving that understanding your customers matters in every industry.
Here are TOP features why 1000+ companies choose Kimola:
- Collect reviews and conversations across web, social media, mobile App Stores, e-commerce sites, Tripadvisor, Trustpilot, Google Business and more or upload your custom dataset:
Your customers are talking everywhere. Kimola makes it easy to gather their voices from websites, social media, mobile app stores, e-commerce platforms, Intercom, Zendesk, and trusted sources like Tripadvisor, Trustpilot, and Google Business—all in one place.
- Auto-Classify instantly and analyze themes with multi-labels & multi-sentiments:
No need for prior AI training to analyze your reviews. Just upload your dataset and analyze reviews instantly with multi-aspects and multi-sentiments. Because all researchers will know that single labels won't work for the best insights!
- Create Custom Models without even training
- Create Summarizations
No more sifting through thousands of reviews. Kimola automatically generates structured summaries—from feature requests and pain points to usage motivations and executive-ready reports—so you can take action faster.
- Export reports to Powerpoint, PDF, Excel, CSV
Easily share your findings across teams. Export your insights in PowerPoint, Excel, or CSV formats to plug directly into your reporting workflows.
- Analyze in 30+ languages over 95,4% accuracy rate.
Kimola analyzes customer feedback in over 30 languages with a very high accuracy rate, helping you understand your audience like never before.
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KimolaLanguages Supported
Arabic, German, English, French, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Chinese (Simplified), Chinese (Traditional)
Product Description
Kimola Cognitive is a no-code machine learning platform for marketing and research professionals to analyze & classify consumer reviews or any text data automatically.
Kimola Cognitive offers the most comprehensive gallery of pre-built machine learning models. Social scientists the of Kimola Team regularly publish new models and update the training sets of existing ones. That means without hiring a data science team, companies can have and maintain ML operations.
When it comes to analyzing data, Kimola is the only ML platform that provides a tool to grab consumer reviews from supported Internet mediums like Youtube, Trustpilot, Tripadvisor. This tiny browser extension allows you to create consumer review datasets, which we call “airsets”. Airsets are disabled for data export and only usable on Kimola Cognitive for data analysis purposes.
Kimola Cognitive also supports creating custom machine learning models, host and serve on the platform via user interface and API!
Kimola Cognitive is perfect for:
- Customer success teams who want to detect the issues of their customers.
- Customer experience teams who want to detect complaints of their customers. You can either use a pre-built model that is prepared by Team Kimola or start your own custom model to analyze complaints.
- Sales Teams who want to detect opportunities to reveal consumer insights in conversations, social or e-commerce conversations, mails, chats.
- Support Teams who want to understand the most frequent issues the customers are having.
- AI/Analytics teams who don't want to spend months building and deploying custom ML models to process their data.
Overview by
Beybin Esen (Managing Partner @Kimola I Strategist I )
