Big day. We announce our brand new product - Katana. Today is first release, which is called 18.1. While working with many enterprise customers we saw a need for a product which would help to integrate machine learning into business applications in more seamless and flexible way. Primary area for machine learning application in enterprise - business automation.
Katana offers and will continue to evolve in the following areas:
1. Collection of machine learning models tailored for business automation. This is the core part of Katana. Machine learning models can run on Cloud (AWS SageMaker, Google Cloud Machine Learning, Oracle Cloud, Azure) or on Docker container deployed On-Premise. Main focus is towards business automation with machine learning, including automation for business rules and processes. Goal is to reduce repetitive labor time and simplify complex, redundant business rules maintenance
2. API layer built to help to transform business data into the format which can be passed to machine learning model. This part provides API to simplify machine learning model usage in customer business applications
3. Monitoring UI designed to display various statistics related to machine learning model usage by customer business applications. UI which helps to transform business data to machine learning format is also implemented in this part
Katana architecture:
One of the business use cases, where we are using Katana - invoice payment risk calculation. UI which is calling Katana machine learning API to identify if invoice payment is at risk:
Katana offers and will continue to evolve in the following areas:
1. Collection of machine learning models tailored for business automation. This is the core part of Katana. Machine learning models can run on Cloud (AWS SageMaker, Google Cloud Machine Learning, Oracle Cloud, Azure) or on Docker container deployed On-Premise. Main focus is towards business automation with machine learning, including automation for business rules and processes. Goal is to reduce repetitive labor time and simplify complex, redundant business rules maintenance
2. API layer built to help to transform business data into the format which can be passed to machine learning model. This part provides API to simplify machine learning model usage in customer business applications
3. Monitoring UI designed to display various statistics related to machine learning model usage by customer business applications. UI which helps to transform business data to machine learning format is also implemented in this part
Katana architecture:
One of the business use cases, where we are using Katana - invoice payment risk calculation. UI which is calling Katana machine learning API to identify if invoice payment is at risk:
Currently we offer these machine learning models:
1. Invoice payment risk calculation
2. Automatic order approval processing
3. Sentiment analysis for user complaints
Get in touch for more information.
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