Connect Human Insights and AI into Workflows

Optimize your training data operations for maximum efficiency. Boost the speed of AI model delivery and maintain QA through intelligent automations.

Maleeha Nawaz
Maleeha Nawaz
Manager of Quality and Data Curation
After conducting an extensive research of annotation tools, we chose V7 as it fits our needs best due to its customizable workflows and automated QA.
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Raman Muthuswamy
Raman Muthuswamy
Director, Translational Research at Genmab
We use V7 to make our annotation and model training workflows more efficient. It's much easier to use than other software thanks to its intuitive UI.
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Ryan Watson
Ryan Watson
Segmentation Manager at Intelligent Ultrasound
We did a comparison when we started and noticed an increase in efficiency when we changed the workflow. Thanks to V7, we no longer have to waste time.
Read case study ->
ML pipeline

The Assembly Line for Enterprise AI Products

Use a model to label more data
Send edge cases back to be labeled
Collect Data
Label Data
Train Your Model
Manage Data
Deploy AI
Workflow stages

All the flexibility you need for your data labeling projects

Set up workflow stages and human-in-the-loop quality control to develop secure and reliable AI

The dataset stage is a starting point for any data workflow. Datasets can be browsed and exported for training.
Learn more about dataset management ->
The annotation stage is where the bulk of annotation work happens, and you get access to all V7 annotation tools. You can add multiple annotation stages and assign different users to complete them.
Learn more about auto annotation ->
The consensus stage lets you compare independent annotations. You can decide what level of overlap between different annotations determines whether they get approved or rejected.
The model stage lets you connect an AI model to annotate images automatically. You can import a pre-trained public model (e.g., COCO), your own model, or use V7 to train computer vision models from scratch.
Learn more about model management ->
In the review stage, you decide who is responsible for reviewing the annotation process. You can add multiple review stages in various parts of the workflow.
The webhook stage triggers an automated event. For example, a webhook can trigger Slack notifications when a reviewer rejects an annotation. You can use the stage in combination with Zapier to turn webhooks into Zaps.
Logic stage lets you control how your data flows by checking for specific tags or annotations. Based on these, it can send data to other stages. You can add multiple rules to logic stages, and they are useful in automating quality control. The feature is currently in development.
This is the final stage of any workflow. You can define what “complete” means to you—for example, it may mean that your data is ready for export or model training.
This is where you can keep all images whose annotations don’t meet a certain accuracy threshold
Annotation systems are playing catch up trying to project where research and cutting-edge development would be and how to make it configurable.

Luckily, solutions like V7 came up, which are maturing in the industry, helping companies like ours to scale up and stay ahead with our R&D

Suchet Bargoti
CTO, Abyss Solutions
Try V7
Ryan Watson
Segmentation Manager at Intelligent Ultrasound
Try V7

Develop Production-Ready AI in Hours, Not Weeks

Automate costly and repetitive tasks to focus on developing high-performing, production-ready AI models.

Full visibility over your data

Gain insights into your data at every stage of its journey. Spot bottlenecks and resolve them without communication overhead.

Workflow templates

Create your own workflow or pick one from our library of workflows templates. From simple to complex data projects - V7 allows you to customize your data pipelines to solve any computer vision task, at scale.

Automate data pipelines

Easily build and automate a workflow process to take your data from its rawest form to being enriched with annotations. Set up your workflow once and use it for multiple data projects.

No-code required

Drag and drop stages to connect them into custom workflows. Leverage no-code interface and complete your AI projects without dev help.

Train models iteratively

Turn your labeled data into models and use them to label more data. Run and improve models iteratively - get immediate results from inference API. You can also register your own model via REST API.

Integrate with any ML pipeline

Build custom workflows and integrate them with any part of your data pipeline. Connect external storage, collaborate in real-time, label, manage, and push data externally in a few clicks.


Custom ML Workflows for Your Use Case

Workflows is V7’s way of helping you structure your ML pipeline—from uploading your data, assigning roles, labeling and reviewing it, and training your models.

Simple data workflow
Complex data workflow
Real-Time collaboration

Built for Teams of All Sizes

All stakeholders, including annotators and ML engineers, can collaborate in real-time. Assign user roles, correct any data discrepancies, and launch your AI products with ease.

the data engine

Manage Your AI Data in a Single Ml-Ops Platform

Solve any labeling task 10x faster, train accurate AI models, manage data, and hire pro labelers that care about your computer vision projects.

Ready to get started?
Try our trial or talk to one of our experts.
Asked Questions
Reach out to our support team or contact us for further questions
Can I test V7 before making a purchase?

Yes, V7 provides a complimentary free tier that lets you explore some of its basic features and capabilities. However, to access the full suite of advanced functionalities and harness the platform’s complete potential, it is worth considering one of the paid plans. You can see the full feature breakdown and pricing of V7 here.

Can you integrate the trained model into our existing system?

The V7 platform allows you to bring your own custom models, hosted on your own infrastructure. You can use them alongside the models trained using V7's own neural networks. The minimal requirements for the custom models are that they must be exposed via HTTP and make predictions in the form of JSON.

Can multiple people label the same asset in V7?

Yes, multiple people can label the same asset in V7, making it a powerful collaboration platform for your data labeling projects. V7 also includes comment tools, user permissions, or consensus stages that measure the level of agreement between different annotators, allowing you to quickly identify any discrepancies in annotations. These features help to improve the quality of your data labeling process and ensure that your annotations are accurate and consistent. With V7, you can manage large-scale data labeling projects with many collaborators.

What type of data does V7 support?

V7 supports image, video, and text data. The file formats you can use with V7 include: JPG, PNG, MP4, MOV, AVI, BMP, SVS, TIFF, DCM, ZIP, DICOM, NIfTI.

What is medical image annotation?

Medical image annotation involves labeling the medical imaging data such as X-Ray, Ultrasound, MRI, CT Scan, etc., for training machine learning models.

Does V7 offer labeling services?

Yes. V7 works with a trusted network of partners and professional annotators who will help you turn your data into ground truth. Go to V7 Labeling Services to submit the form and we will send you a proposal within hours.

What type of support does V7 offer?

We offer in-app chat support and email technical support to all of V7 users. We will make sure to take good care of you and your team. You can get in touch with us at: