Create and optimize data labeling processes
that save you money and boost data quality.
Seamless labeler onboarding and powerful project orchestration modules for increased efficiency and quality
Create efficient multi step workflows using our drag- and- drop builder and intuitive UI. Keep pace with evolving requirements as your AI becomes smarter.
Update your tasks whenever you need to and deploy them instantly to the teams that need them.
Create intuitive, dynamic workflows to make it simple for your labeling team. Increase speed, reduce errors and procure new data with ease
Design tasks for any data type, including product taxonomies and attribute lists that other data labeling tool don’t support.
Allocate tasks to a single team or multiple teams, set deadlines, and assign a team lead to your data labeling and classification projects.
Taskmonk lets you securely manage all of your data labeling teams in one place, whether they’re in-house, third-party, or a combination of both.
Localise the interface language to empower teams for seamless cross border collaboration
Bring in-house and third-party labeling teams onto a single platform to assign tasks and monitor performance easily.
Teams fueled by industry specific knowledge repositories and a minimal learning curve
Have dispersed global teams and enable each to add employees and manage their organisations
Codify SOPs and supercharge programatic labeling workflows
Use the power of our Active Learning AI models to assign tasks efficiently, automate labeling processes, and amplify the output of your team.
Access our active learning AI models to accurately prelabel data faster
Our AI data labeling models can learn from watching your human labelers and then assist with repetitive tasks to boost efficiency.
Our task allocation algorithms match your task to the ideal labelers to reduce your per label cost and improve data quality.
Taskmonk gives you all the tools you need to improve your label data accuracy and keep tabs
on all your labeling teams.
Choose the QC method that fits, create strategic sampling rules, and even error-proof your data labeling at the point of entry.
Use Maker-Checker, Maker-Editor, and Consensus QC methods. Apply the right strategy for each task and data type
Configure rules to create intelligent QC samples that account for high-risk teams, new users, or other critical factors
Track AI data labeling progress in real-time. Monitor individuals or teams and easily access analytics on labeling tasks.