Train AI models to classify customer feedback with your own labels. Upload your training set, build your model, and start analyzing—all no code!
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Kimola offers two different ways to build and deploy custom models. You can either upload your training set in .xlsx, .xls, .csv, or .tsv formats or enter label names & descriptions to train a model by prompt -and let Kimola analyze its suitability. Once your model is built, choose the model name after uploading your dataset to analyze and follow the advanced menu to use it. Developers can also integrate Kimola's AI into their applications without infrastructure investment.
Kimola's AutoML ensures the highest accuracy, letting social scientists focus on content rather than technical complexity. When you update your training set, the model is automatically rebuilt using the best statistical method, keeping it accurate and up-to-date.
Collect and centralize feedback.
Make sense of large-scale feedback.
Turn data into meaningful insights.
Find out how Kimola can improve your feedback analysis process.
Uncover customer needs, likes, and dislikes from product reviews and feedback.
Analyze customer reviews and ratings to optimize online shopping experiences.
Extract insights from social media conversations and online discussions.
Make sense of free-text survey responses with AI-powered analysis.
Understand customer sentiment and concerns from chat and call transcripts.
Identify workplace trends and employee sentiment from internal feedback and reviews.
An AI model is a system trained to recognize patterns in data and make predictions or classifications based on those patterns. In Kimola, AI models help categorize customer feedback according to custom labels.
A custom AI model is trained specifically on data relevant to your industry, brand, or research goals—unlike generic models that provide broad, one-size-fits-all insights. With a tailored model, you get highly accurate, context-aware classifications that reflect the language, products, and priorities unique to your business. For example, if you're in the fashion industry, the model can be trained to recognize and differentiate subtle color terms in customer reviews—like distinguishing "red" from "blue"—giving you more precise insights into how customers perceive your products.
Yes, just focus on creating a well-prepared training set. It may be in Excel format or, if you're not a fan of Excel, use your favorite software and export as CSV or TSV. Kimola accepts all these file formats to build your machine learning models. If anything is wrong with your training set, the wizard will notify you.
Kimola allows you to enter labels and descriptions for each label to help you create a custom model. You don't need any training set to create a model by prompt, you just need to know the labels.
You can upload a training set with a minimum of two labels and 100 samples for each label. A training set can contain a maximum of 60,000 rows and up to 15 labels.
No, Kimola is only available on the cloud. However, you can manage your custom model through a visual user interface or a well-documented API. If you have any concerns about data safety, contact us, and we'll provide all the necessary information.
Kimola's AutoML works to achieve the best accuracy, but accuracy depends on the training set. If your model has a low accuracy rate, add more samples for your labels and upload your training set again. These experiments don't affect your package usage since we know they're a natural part of the process.
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