Scigility Services

You will receive: AI and ML use case roadmap, data strategy, definition of data products, and technical enterprise data architecture, taking into consideration data protection, IT security, and governance specifications. The formulation of the business case for the implementation of a data-driven strategy also includes prioritization and planning for MVPs, Proof of Concept (POCs), and the productive deployment of AI and ML use case and a data platform.

Our broad technology expertise lets us provide advice on the appropriate AI tools for a successful implementation of ML projects. Thanks to our broad experience and great affinity in the field, we are a leader in new technologies. We maintain close ties with pioneering universities, spin-offs, and start-ups.

We advise and ensure compliance with regulatory and customer-internal data protection specifications (e.g. GDPR, FINMA, BAG, etc.). During audits of existing data platforms, we can deploy our many years of experience for your benefit.

Many years of experience in AI use case implementation in financial services, insurance, pharmaceuticals, health, manufacturing, utilities, and many other industries. For the implementation of use cases, we deploy the MLOps & AI Industrialization Frameworks and best practices developed by Scigility, and we use innovative and optimally tailored MLOps and data engineering tools and frameworks.

Implementation of enterprise data platforms in public cloud, hybrid, or on-premise. Application of MLOps and DevOps best practices, automation of deployments according to infrastructure as code principles, and use of cloud-native technologies such as Kubernetes and Serverless.

Support and enhancement of use case applications and data platforms, second/third-level support, firefighting, incident and problem-solving, monitoring, periodic reporting, cost and capacity management of the applications and platforms.

Expertise transfer on the project, training about technologies and programming languages, agile frameworks for the successful implementation of AI and data projects, including establishment of expertise and personnel resource support.