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Pretrained models for customer reviews, complaints, app reviews and social media messages. Free, no sign-up; they understand Turkish and English.
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General analysis
Core models that work in every industry: sentiment, intent, topics and entities.
Sentiment Analysis
Scores the overall tone on a five-level scale and shows the breakdown across eight basic emotions. It also captures the balance in mixed reviews (e.g. bad delivery, great product).
8 labelsOpen toolCustomer Feedback Classifier
Flags each topic raised in retail store reviews separately; a review can mention several. The overall sentiment is shown alongside.
10 labelsOpen toolCustomer Intent Detector
Flags the purpose of the text and whether it calls for action, so you can separate customers waiting for a reply or about to leave.
8 labelsOpen toolEntity Extractor
Finds the proper names, handles, dates and amounts in the text and decides the type of each. Ideal for catching the branch, product and staff names in reviews.
7 labelsOpen toolNews Classifier
Gives every text its best-fitting news category and the probability of the others; ideal for media monitoring, content tagging and agenda analysis.
14 labelsOpen tool
Industry classifiers
Label sets trained on your industry's language; find yours and try it now.
E-commerce Review Classifier
Flags the typical problems of online shopping reviews separately and helps you tell seller-side issues from product-side ones.
9 labelsOpen toolMobile App Review Classifier
Flags the technical and product issues in App Store and Google Play reviews separately so you catch complaints after a release quickly.
9 labelsOpen toolMobile Game Review Classifier
Twelve core topics for game studios and marketers: see at a glance what players love, why they quit and how monetisation is perceived.
12 labelsOpen toolSoftware Feedback Classifier
Ten core topics for SaaS and software products: see which features users love, why they churn and which integrations they expect.
10 labelsOpen toolOnline Course Review Classifier
Eleven core topics for learning platforms and instructors: see what students find useful and why they drop out.
11 labelsOpen toolRestaurant and Hotel Review Classifier
Flags the topics in hospitality and food-service reviews separately, making comparisons across locations and periods easy.
9 labelsOpen toolBanking Feedback Classifier
Classifies complaints and reviews with a topic set made for finance and flags security and fraud reports separately.
9 labelsOpen toolAutomotive Feedback Classifier
Thirteen core topics for carmakers, distributors and dealers: separates everything customers say about your vehicle, from product perception to service experience.
13 labelsOpen toolMobile Network Feedback Classifier
Twelve core topics for telecom operators: separates network problems from commercial complaints and surfaces regional outages and billing disputes early.
12 labelsOpen tool
Moderation and trust
Filter out harmful content and reviews that aren't genuine.
Hate Speech Detector
Shows how likely the text is to be harmful and of which kind. It tries to tell harsh but legitimate criticism from abuse.
6 labelsOpen toolFake Review Detector
Shows how likely the review is not a genuine customer experience and the warning signals behind it. It tries not to flag short but sincere reviews.
6 labelsOpen tool
Visual inspection
A photo instead of text: product and vehicle damage, fire, shelf stock, food quality, venue cleanliness, logo visibility, vehicle recognition and return–listing matching.
- New
Product Damage Detector
Upload a photo: the AI says whether the product or its packaging is damaged, lists damage types with probabilities and marks the damaged area on the photo. Ideal for pre-screening return and damage claims.
6 labelsOpen tool - New
Logo Detection
Upload a photo and type the brand: the AI says whether its logo appears, how visible it is and where, and marks it on the photo. Ideal for measuring sponsorship visibility and brand mentions in social photos.
6 labelsOpen tool - New
Shelf Audit
Upload a shelf photo from your field team or store staff: the AI says whether there are out-of-stock gaps, how full and tidy the shelf is, and marks the largest gap.
6 labelsOpen tool - New
Food Presentation Check
Upload a plate photo attached to a complaint or taken in the kitchen: the AI judges whether there is a quality problem, what kind, and how appetising the food looks.
6 labelsOpen tool - New
Vehicle Damage Detection
Upload a vehicle photo: the AI judges whether the vehicle is damaged, the damage types and severity, and marks the most damaged area. Ideal for pre-assessment in rental, insurance and service workflows.
6 labelsOpen tool - New
Venue Cleanliness Check
Upload a photo of a branch, hotel room or toilet: the AI judges whether there is a cleanliness problem, what kind, and the overall cleanliness level, and marks the most problematic area.
6 labelsOpen tool - New
Fire and Smoke Detection
Upload a photo: the AI says whether there is an uncontrolled fire or smoke, how dangerous it is and what is burning (a building, a vehicle, vegetation), and marks the source on the photo. Fire that is meant to be there, such as candles, fireplaces and barbecues, does not count.
6 labelsOpen tool - New
Return vs Listing Match
Upload the listing image and a photo of the item you received: the AI says whether they are the same product, how closely they match and where the difference is. It ignores angle, lighting and background, and looks for another model, colour, wear and signs of a fake.
6 labelsOpen tool - New
Vehicle Make and Model Recognition
Upload a vehicle photo: the AI ranks the body type (sedan, hatchback, SUV …), make, model and colour with their probabilities and marks the vehicle on the photo. It focuses on the models most common in Turkey and says “another model” for the rest.
6 labelsOpen tool
Ready-made models, labels made for you
Use the models on this page as they are, change their labels for your industry, or ask for a classifier built just for you. To connect them to your own systems through our API, reach us at [email protected].
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