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Transform unstructured photos and images into actionable business intelligence.
Power the next generation of smart cities and public spaces.
Rethink community in a world that is hungry for new ways to connect.
Every company using AI needs to label data to train AI models.
Data labeling a painstaking process for human workforces. We designed Labeler to make it easy to build, deploy and iterate on AI technology quickly.
We added automated evaluation tools to catch issues that may not be obvious to the human eye; identifying blindspots and biases due to overrepresentation/underrepresentation within datasets. Data visualizations help to identify “edge cases” - unusual or rare situations that can sometimes cause major problems in the real world.
Months into 2020, millions of people around the world are suddenly out of work, or facing reduced hours because of the COVID-19 pandemic.
Labeling data can be relatively low-skilled work (identifying objects in videos), or highly specialized (radiologists outlining the exact contours of tumors on a medical scan).
We think Labeler can help meet a demand for work that can be performed remotely or from a home office, while helping companies explore AI technologies that will create a competitive advantage well into the future.
Data designers can create high-quality training data more effectively with Labeler.
Labeler streamlines every aspect of the annotation process and integrates seamlessly with the entire AI lifecycle.
Custom AI models can be developed on day-one with a graphical user interface that is designed for plug-and-play operability.
Data designers can interact with their dataset with intuitive AI-powered search and visualization tools.
Even non-technical users can create multi-step annotation projects for cutting edge AI.
When a model is first created, people will have to manually label all of your data. But as your model learns, it can actually start labeling some data on its own.
Data designers can optimize this automatic labeling by setting and tuning Labeler’s prediction thresholds. These thresholds can separate inputs into buckets, and automatically write annotations in cases where predictions are confident, or send them to a worker for review.
Data designers can also collect new data from their model once it is in production. This “live” data can then be fed into their app with a backstop of human review in close-to real time.