Bloods.ai algorithms are purposefully designed for analytical accuracy. Trained by big data acquired through our global data collection network, our algorithms trace a growing number of blood substances with precision.
We classify our algorithms by a variety of factors, making it simple for participating data scientists to find the right fit.
All models offered in our marketplace are developed by independent data scientists using our proprietary data sets, ensuring adaptability and accuracy.
Browse our marketplace of blood-aware AI models to find the right fit for your next project.
Classifications include:
Every sensor sees the world through a unique lense we train algorithms using native data collected by specific sensor types.
There are more than 4,000 traceable compounds found in human blood, and bloods.ai aspires to provide algorithms capable of detecting every single one - from rare to commonplace and everything in between.
Each substance in our blood exists in a specific concentration, most often falling within a predictable or “normal” range. Bloods.ai algorithms are classified by their range of accuracy and produce reliable results when tested substances have concentrations similar to those used in training algorithms.
We’re working tirelessly to expand outside of “normal” ranges to include sufficient data for the accurate detection of anomaly ranges, and we’re eager to overcome barriers of limited training data that restricts the expansion of an algorithm’s accuracy range.
We classify each algorithm by its intended scan target - including fingers, arms, wrists and feet. Bloods.ai algorithms are trained on data captured from specific scan targets, optimizing the reliability and accuracy of every reading.
Each detectable compound in our blood requires a unique scan duration length to produce accurate results. The bloods.ai marketplace conveniently categorizes algorithms by scan duration, making it easier to find an algorithm suited to your vision.
English 
Portuguese
Spanish