Canotic is a data labeling platform for AI that accelerates machine learning projects by using AI & humans to generate, structure, and label any type of data.
Our observation is that the power of AI is currently blocked not by the complexity of the algorithms. but by the availability of the clean, structured and labelled datasets needed to generate meaningful insights from ML.
Machine learning teams spend 80% of their valuable time cleaning, structuring and labelling data, instead of building models. Product managers want to apply ML, but are hamstrung by bad data and a lack of know-how. Labelling services exist, but the quality is often poor, service intransparent and the different offerings confusing.
Canotic uses your raw data as an input, then cleans, structures and labels your data, to generate your desired output. Any input (e.g. text, images, video, time series, satellite) can be converted it into any output (e.g. classification, tagging, anonymization, digitization, transcription etc). Outputs can include predictive models as well, if needed.
We are a team of Data Scientists/Machine Learning engineers with a background from MIT, Google, Amazon and Microsoft. We have received funding and have recently launched our beta site: www.canotic.com, and we are now looking to engage with prospective clients.