Data labeling for GenAI
Supervised Fine-Tuning (SFT)
Reinforcement Learning with Human Feedback (RLHF)
Human Evaluation
API & SDK Integration
Managed Service
People For AI, Innovatiana, Surge AI, BasicAI Cloud, Label Studio, Dioptra AI Redlining, PromptLoop are the best paid / free ai assisted data labeling tools.






AI-assisted data labeling is a process that leverages artificial intelligence to streamline and improve the efficiency of data annotation tasks. By incorporating AI algorithms, the labeling process becomes more accurate and less time-consuming compared to manual labeling. This approach is particularly useful for large datasets in computer vision, natural language processing, and other AI-related fields.
Core Features
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Price
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How to use
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Surge AI | Data labeling for GenAI | To use Surge AI, you can sign up on their website to access their data labeling platform. You can integrate their services directly with native APIs and SDKs or partner with their expert data team for a managed service. They offer tools and an elite workforce to build powerful datasets. | |
Label Studio | Support for multiple data types (images, audio, text, video, time series) |
Community Edition Free to use
| Label Studio can be installed via PIP, Brew, Git, or Docker. After installation, you can launch the tool, import data, create projects, and start labeling using customizable tags and templates. |
Innovatiana | Data Labeling for Computer Vision | To use Innovatiana's services, you can request a quote by discussing your project needs. They will then study your requirements, propose a customized solution, conduct a free test, and mobilize a team of data labelers to process your data. They offer flexible pricing based on the task and deliver the prepared data securely. | |
PromptLoop | AI-powered text transformation, extraction, and summarization in Google Sheets and Excel |
Free $0 /mo Explore on your own with two workflows, unlimited edits, access to features with rate limits and daily usage limits, PromptLoop Google Sheets™ and Microsoft Excel™ plugin, guides and templates, limited to one user.
| Users define the specific datapoints they need, then upload data (e.g., spreadsheets of websites, companies, or leads) or connect their CRM. PromptLoop then runs AI research flows on thousands of inputs at a time, leveraging prebuilt flows and templates to extract and format the desired information. The process is designed for quick setup, often taking less than 15 minutes, by simply dragging and dropping spreadsheets. |
BasicAI Cloud | AI-powered annotation tools | New users can access BasicAI Cloud for free with 50 seats, 100GB storage, and 1,000 model calls. Use the AI-powered annotation tools to label data, manage teamwork, and scale projects. | |
Dioptra AI Redlining | Redlining | 1) Download Word Addin 2) Tell the assistant to compare, research, redline, draft, all in Microsoft word | |
People For AI | Data labeling for computer vision, NLP, and speech recognition | Annotation Projects €6 - €9 per annotation hour For production projects requiring more than 500 hours of annotation, the cost is usually between €6 and €9 per annotation hour. This price includes annotation, review and customer care. This price does not include the selection/training of the annotation team and the setup of the annotation tool (200-300€, depending on the complexity). | To use People For AI, you can contact them to discuss your machine learning project and data labeling needs. They will assign a project manager, put together a specialized annotation team, and start the annotation project after defining the tool and initial annotation instructions. |

Large Language Models (LLMs)
AI API
AI Developer Tools
AI Research Tool

AI Models
Large Language Models (LLMs)
AI Image Recognition
AI Text Classifier
AI OCR
AI Document Extraction
Self-driving car companies use AI-assisted data labeling to annotate road scenes, traffic signs, and pedestrians for training their perception models.
Healthcare organizations employ AI-assisted data labeling to annotate medical images, such as X-rays and CT scans, for developing diagnostic AI tools.
E-commerce platforms utilize AI-assisted data labeling to categorize and attribute product images for improved search and recommendation systems.
Users have praised AI-assisted data labeling for its efficiency, accuracy, and ability to handle complex labeling tasks. However, some users have noted that the initial setup and configuration can be time-consuming, and the cost of some platforms may be prohibitive for smaller organizations. Overall, the majority of users have found AI-assisted data labeling to be a valuable tool for accelerating their AI projects and improving the quality of their training data.
A user uploads a dataset of images and selects the object detection task. The AI model automatically pre-labels the objects in the images, which the user then reviews and corrects as needed.
A user assigns text classification tasks to multiple annotators. The AI-assisted platform intelligently distributes the tasks based on each annotator's expertise and performance.
To implement AI-assisted data labeling, follow these steps: 1) Prepare your dataset and define the labeling requirements. 2) Select an AI-assisted data labeling platform or tool that suits your needs. 3) Configure the AI models and task settings according to your project specifications. 4) Assign the labeling tasks to human annotators, who will review and correct the AI-generated labels. 5) Monitor the progress and quality of the labeling process, providing feedback to the AI models as needed. 6) Iterate and refine the AI models based on the human-validated labels to improve accuracy over time.
Reduced time and cost compared to manual labeling
Improved accuracy and consistency of labels
Scalability for large datasets and complex labeling tasks
Faster iteration and development cycles for AI projects







































