SentiSight.ai is a web-based platform for labeling images and developing image-recognition applications. It supports classification, object detection and segmentation models, with annotation tools for labels, boxes, polygons, points, polylines and bitmaps. Smart labeling uses selected foreground and background points to isolate an object. Teams can label images in shared projects, while managers track progress and time and manage roles and permissions. Image search can find results similar to an uploaded query or similar pairs within a dataset. Pre-trained models cover content moderation, goods classification, automatic hashtags and people counting. Models can run through the web interface or REST API, or offline after downloading a model and setting up a local REST API server. Android and iOS apps make predictions from uploaded images or camera photos. The mobile apps cannot train models or label already uploaded images, and they support classification labels but not object labels. The platform has a free plan and a 30-day trial. Its listed pay-as-you-go details include €5 monthly complimentary credits and usage charges for predictions, labeling and training.

Who it is for

SentiSight.ai suits teams and researchers labeling image datasets or building image-recognition applications. Its listed use cases include retail product tagging, manufacturing quality control, e-commerce visual search and research labeling.

What is good

Supports classification, detection and segmentation models
Multiple annotation shapes and label types
Shared projects include manager progress and role controls
Online, REST API and offline deployment options
Pre-trained models cover several image-analysis tasks

What to know first

Mobile apps cannot train new models
Mobile apps cannot label existing uploads
Mobile labeling supports classification, not object labels

Verdict

SentiSight.ai combines image annotation, model development, search and deployment options for a range of image tasks. Mobile apps are limited to predictions, so training and labeling require another supported route.

SentiSight.ai plans and pricing

All plans

Offline model license Not published Single time payment; custom quote based on model speed Offline model download · 30-day free trial sentisight.ai · 30 Sept 2026

Pay-as-you-go Not published €5 monthly complimentary credits; €20 free credits for new users 5000 predictions or 5000 labels or 83 minutes of training within free credits · prediction €1 / 1000 predictions · image labeling €1 / 1000 labels · training €3.6 / hour · 5 GB disk space included sentisight.ai · 30 Sept 2026

Compared on AI image recognition software

Free plan: Yes
Object detection: Yes
Image classification: Yes
OCR: Yes
Custom models: Yes
Deployment options: hybrid

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The record

Purpose

SentiSight.ai is a web-based platform for image labeling and developing AI-based image recognition applications.

Model types

The platform supports classification, object detection, and segmentation models.

Annotation

Its labeling tool supports classification labels, bounding boxes, polygons, points, polylines, and bitmaps.

Smart labeling

The smart labeling tool uses selected foreground and background points to let AI extract a labeled object from an image.

Collaboration

Shared projects let multiple users label images, while project managers can track progress and time and manage roles and permissions.

Similarity search

Users can search for images similar to an uploaded query image and run NvN searches to retrieve similar image pairs in a dataset.

Pre-trained models

Pre-trained models are available for tasks including content moderation, goods classification, automatic hashtags, and people counting.

Deployment

Models can run online through the web interface or REST API, or offline after downloading a model and setting up a local REST API server.

Mobile apps

The mobile app is available for Android and iOS and supports predictions using uploaded images or photos taken with the device camera.

Mobile limitations

The app cannot train new models or label already uploaded images, and it supports classification labels but not object labels.

Privacy

The privacy policy says personal-data safeguards are handled in accordance with GDPR and other applicable data protection laws.

Data processing

The data transfer agreement identifies Neurotechnology UAB as a GDPR data processor and says it will process personal data on the user's instructions and bind authorized personnel to confidentiality.

Support

The site provides a FAQ, user guides, video tutorials, and a contact page for assistance.

Intended use

The maker describes use cases including retail product tagging, manufacturing quality control and defect detection, e-commerce visual search, and research dataset labeling.