The future of healthcare is here
All together, we can unlock the value of health data and continuously optimize the algorithms of our health
We live at the edge of the network.
We live in a world where more and more people will collect and consume data at the edge of the network, on smartphones with its built-in sensors. By leveraging every component of the smartphone, we put the human journey at the center of the experience while intelligence is computed where the data and the experience intersect. Collectively, we are building the private automated edge of our health.
Our distributed architecture automates the edge and its applications to allow our neural nets to use edge AI to store, compute and train the data locally and therefore expand the limits of what’s possible four our health with less friction and more engagement– with minimal latency and optimal privacy.
Manage privacy with choice and transparency.
Health data is doubling every 70 days and the explosion of data processed on the edge is putting privacy center stage of conversations about AI. Regulators are asking the industry to treat privacy as a first-class citizen, but more importantly, it is the right thing to do. With our edge AI and Federated Learning technologies for mobile and cloud, we are at the forefront of privacy-preserving techniques in digital healthcare. Our distributed machine learning approach uses differential and preferential privacy to allow individuals to train new health algorithms in the palm of their hands — so only the model learnings are shared, not the data. We give individuals and organizations the choice to manage their privacy to their own level of comfort and compliance.
ML for Data fluency
Discovery, fluency and explainability
Data engineering and data fluency are major pain points in healthcare. Our AI engine for discovery augments any machine learning environment for data fluency and explainability. It is a cloud native Machine Learning (ML) and ML Ops environment, an AI discovery engine that enables cross-functional teams to evaluate clinical models in real time for quality and costs of care in a few weeks instead of months.
Our environment has been validated using data from over 300 million individuals and more than 1 billion data elements.Our solution partially solves explainability issues and can train models in parallel from automated multi-omics pipelines.
Never trust, always verify.
In a world of multi-tenancy and billions of identities, security and compliance have never been so important. With the growing complexity of network and compute infrastructure attributable to the advent of cloud computing and services as well as the dynamic and heterogeneous environments in edge computing, 5G, and IoT, old assumptions such as defending static, well-defined perimeters are no longer valid.
Zero Trust allows organizations to mitigate, detect, and respond to risks much more effectively because every workload is cryptographically signed and communicates using declarative policies ensuring limited blast radius and least privilege access. With secure zero trust infrastructure, we can imagine organizations wanting to unleash new ways to collaborate using their data as an asset by exchanging derivative knowledge using federated learning while staying fully compliant.
This is our story, and this is why AI and deep tech matter in healthcare.
We give people the power to collect, own and train their own health data to generate models to help themselves and their communities in real-time using their phones all the while treating privacy as a first-class citizen.
We give people and organizations the tools and the choice to exchange learnings without moving the data: by breaking data silos you impact the quality and cost of care in real time.
We give organizations powerful data fluency tools to unlock the value on their data and generate actionable insights immediately using our Toniq platform.
We give organizations the dynamic environment that automates and scales security and compliance for a world that will soon count in yottabytes.