Breathe intelligence into your data: Compute predictions and forecasts based on anomally detection, classification or regression models.
Enteprises today extract data and evaluate this data externally in different technical platforms. This creates 3 main problems:
Our AI engine solves this problem with two different types of AI-Deeplearning Scenarios
No human interaction required. Predictions are self-correlating based on actual data.
Example: Compliance Augmentation
Goal: Identifying Illict trade routes
AI-Neuron Output: Retail outlets with high anomality scores
Example: Delivery Team Augmentation
Goal: Leverage private persons as delivery drivers
AI-Neuron Output: Next day route prediction
Human interaction is required. Humans label data to train neuronal networks.
Example: Inspection Augmentation
Goal: identifying the right follow-up action after an inspection
AI-Neuron Output: classyfing the outputs of the seizure by actions
Example: Stock Augmentation
Goal: avoiding out of stock without compromising working capital
AI-Neuron Output: forecasting sales by SKU and Country for production planning
Our AI engine make both processes easy: The data feeding as well as the definition and parametrization of the AI networks. Data scientists can also use Python for scripting and even import their existing AI networks.
The fact that the AI engine is integrated within the business process is key. This way AI outputs can be applied in real-time to raw data instead of hours or even days later after data extraction as typically done in regular AI scenarios.
The osapiens Hub also ensures highly resource-efficient preprocessing and preparation of data, as well as optimized staging of data as input to neural networks in our AI Engine.
Our AI engine enables "responsible" AI through rich options for analysis and visualization of processed data and retroperspectives of learned business processes.
