Scigility Framework

Scigility Modern Data & AI Architecture

Scigility relies on a holistic approach when defining data strategy & architecture, one that we have developed over the years based on project experience and research. We focus on frameworks that allow our customers to view a data strategy in its entirety and to understand and get a grip on its complexity. Scigility’s Modern Data & AI Architecture best practices ensure:

• Enterprise-wide data strategy and holistic data governance that keeps data products interoperable and guarantees continuity, data protection, and transparency in a distributed and agile approach
• Business and cost case including AI & ML Use Case Roadmap and prioritization and planning of MVPs and productive deployment of use cases on a ML / data platform
• Future-proof data architecture and technologies, taking data protection, IT security, and governance requirements into account
• Successful MVP implementation on a modularized data platform that provides data self-service and continuously adapts and improves underlying technologies

Scigility MLOps & AI Industrialization

Adding value to customer projects is Scigility's ultimate goal. With Scigility MLOps & AI Industrialization, we show our customers how to successfully put their use cases into production. We guide our customers through the rapidly changing AI technologies and guarantee they will not get stuck in the MVP or PoC phase.
We regard MLOps and Next Gen Automation as the ultimate goal for deployments. We implement ML applications in such a way that data pipeline updates and retraining can be automated as much as possible, including monitoring, model drift detection, and deployment of new models.
Scigility’s MLOps & AI Industrialization Framework supports the different project phases of ML implementation.

• Use Case AI Journey - identification of use cases and design for realization
• AI Assessment - AI/ML feasibility and analysis of data protection requirements and ethical principles "fair / ethical AI"
• Data Preparation - data preparation and implementation of automated data pipes
• AI Development - development of AI & ML solutions from MVP and productive implementation after approval
• Case Approval - MVP2Prod, Ethics Board, Monitoring, Support, … and ensuring data protection and guaranteeing that, e.g., new data sources are integrated according to specifications - data product, architecture, self-service
• Deployment – automated deployment of the AI/ML solution, taking governance into account
• NextGen Automation – ensures monitoring, model drift detection, and performance and accuracy of the ML application with minimal manual effort

Scigility Data Driven Enablement

Digital evolution is transforming our society and economy and steering both into a new disruptive dimension. As pioneers of this digital evolution, our goal is to take our customers to the new peak of this dimension and unleash their data potential. For the implementation of AI / ML use cases and an enterprise data platform, you need more than just the technology.

From almost 100 projects in recent years, we have developed the necessary methodology and tools to successfully implement data projects. This includes the use of agile frameworks and project methods (Scrum, SAFe...) adapted to data projects, definition of the roles and tools required for successful project implementation (Jira, Wiki, Git Workflow...), and definition of the necessary processes (industrialization from lab to app, automation...).

The customer projects for which we implement solutions are usually set up as mixed teams, which ensure the transfer of know-how during the project. For our customers, we offer training and courses from our Scigility Academy. The trainings in the ML and data sector range from programming languages to enablement of data-driven strategies for decision-makers and management. Our courses are used in many programs, including executive master’s programs at universities and Udemy™.

Scigility Use Case Accelerator

We have many years’ experience in AI use case implementation in financial services, insurance, pharma, health, manufacturing, utilities, and many other industries. Our broad technological know-how helps our customers find the right AI tools for a successful implementation of ML platforms and projects.

Thanks to our varied experience and affinity with the field, we are leaders in new technologies. We maintain close contact with pioneering universities, spin-offs, and start-ups.

With AI & ML development, we apply our Use Case Accelerator framework and best practices because today many people try to implement use cases without standard implementation frameworks. The consequences of this are excessively long development cycles and high to go-to-market costs, especially for productive implementations.

Our framework supports numerous AI / ML cases, including anomaly detection; asset behavior prediction; image, text, and voice classification; predictive analytics, e.g., for crime, sales, traffic prediction; next best-offer; customer DNA; parsing; alert scoring; and many others. Link to Use Cases.

Scigility Site Reliability Engineering

Our Site Reliability Engineering (SRE) includes skilled support & maintenance services for further development 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.

Today, our engineers support some of the largest data platform, cluster, and ML application infrastructures for our most important customers. We as Scigility build your data infrastructures, secure them, integrate them into your environment, and help you to operate them.