Great to meet you at the data summit 2030

Since you may have chatted with us already, we want to give you the opportunity to dive deeper into our expertise. You might have caught a glimpse of a few success stories at our booth, but we know how fast-paced events can be!

Feel free to take your time and explore them at your own pace. We really want you to have a chance to let the details sink in. If you need any more assistance you can contact us here.

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Our success stories

1
The migration from on-premise to a cloud environment reduced maintenance costs and enabled the addition of new features.
2
The deployment times for a single build went from weeks to hours.
3
The automation reduced the risk of failure for the new deployment process.

Moving top-tier investment bank infrastructure to the next level with cloud deployment

1
The migration from on-premise to a cloud environment reduced maintenance costs and enabled the addition of new features.
2
The deployment times for a single build went from weeks to hours.
3
The automation reduced the risk of failure for the new deployment process.

1
The migration from on-premise to a cloud environment reduced maintenance costs and enabled the addition of new features.
2
The deployment times for a single build went from weeks to hours.
3
The automation reduced the risk of failure for the new deployment process.

Moving top-tier investment bank infrastructure_cover

1
The migration from on-premise to a cloud environment reduced maintenance costs and enabled the addition of new features.
2
The deployment times for a single build went from weeks to hours.
3
The automation reduced the risk of failure for the new deployment process.

1
Operational costs reduction by 77%
2
Storage reduction by 63%
3
Organized modification and update of the data structure

77% storage cost reduction without vendor lock

1
Operational costs reduction by 77%
2
Storage reduction by 63%
3
Organized modification and update of the data structure

1
Operational costs reduction by 77%
2
Storage reduction by 63%
3
Organized modification and update of the data structure

storage_cost_reduction_without_vendor_lock-in_cover

1
Operational costs reduction by 77%
2
Storage reduction by 63%
3
Organized modification and update of the data structure

1
Unified, scalable data platform processing thousands of tables daily.
2
Major time and cost savings via automation and self-service analytics.
3
Future-proof, vendor-independent system built with Scala’s type safety.

Optimizing business analyses with an automated single source of truth

1
Unified, scalable data platform processing thousands of tables daily.
2
Major time and cost savings via automation and self-service analytics.
3
Future-proof, vendor-independent system built with Scala’s type safety.

1
Unified, scalable data platform processing thousands of tables daily.
2
Major time and cost savings via automation and self-service analytics.
3
Future-proof, vendor-independent system built with Scala’s type safety.

Streamlining_business_analyses_with_an_automated_single_source_of_truth_cover

1
Unified, scalable data platform processing thousands of tables daily.
2
Major time and cost savings via automation and self-service analytics.
3
Future-proof, vendor-independent system built with Scala’s type safety.

1
User-friendly interface with extended features.
2
Compatibility with a range of LLMs including Claude, GPT, Mixtral, and Gemini.
3
Support for multiple JetBrains IDEs.

Extending AI-code assistance to IntelliJ

1
User-friendly interface with extended features.
2
Compatibility with a range of LLMs including Claude, GPT, Mixtral, and Gemini.
3
Support for multiple JetBrains IDEs.

1
User-friendly interface with extended features.
2
Compatibility with a range of LLMs including Claude, GPT, Mixtral, and Gemini.
3
Support for multiple JetBrains IDEs.

Extending AI-code assistance to IntelliJ_3

1
User-friendly interface with extended features.
2
Compatibility with a range of LLMs including Claude, GPT, Mixtral, and Gemini.
3
Support for multiple JetBrains IDEs.

1
Reduced data collection time from 20 minutes to 1 minute per case.
2
Freed underwriters to focus on complex analyses.
3
Improved accuracy with automated data synthesis.

Enhancing underwriting efficiency with LLM integration

1
Reduced data collection time from 20 minutes to 1 minute per case.
2
Freed underwriters to focus on complex analyses.
3
Improved accuracy with automated data synthesis.

1
Reduced data collection time from 20 minutes to 1 minute per case.
2
Freed underwriters to focus on complex analyses.
3
Improved accuracy with automated data synthesis.

Enhancing_underwriting_efficiency_with_LLM_integration_image-min.jpg

1
Reduced data collection time from 20 minutes to 1 minute per case.
2
Freed underwriters to focus on complex analyses.
3
Improved accuracy with automated data synthesis.

1
Successfully deployed and used by subsidiaries within 6 months
2
Five new distinct projects were launched immediately using the updated framework in the new Azure Databricks environment.
3
Adopted engineering best practices — CI/CD, testing, code reviews, DEV/PROD environments.

Migrating and generalizing a forecasting framework from Hadoop to Databricks

1
Successfully deployed and used by subsidiaries within 6 months
2
Five new distinct projects were launched immediately using the updated framework in the new Azure Databricks environment.
3
Adopted engineering best practices — CI/CD, testing, code reviews, DEV/PROD environments.

1
Successfully deployed and used by subsidiaries within 6 months
2
Five new distinct projects were launched immediately using the updated framework in the new Azure Databricks environment.
3
Adopted engineering best practices — CI/CD, testing, code reviews, DEV/PROD environments.

Migrating_and_generalising_a_forecasting_framework_from_Hadoop_to_Azure_Databricks_cover

1
Successfully deployed and used by subsidiaries within 6 months
2
Five new distinct projects were launched immediately using the updated framework in the new Azure Databricks environment.
3
Adopted engineering best practices — CI/CD, testing, code reviews, DEV/PROD environments.

1
The model reduces analysis time by suggesting data for review, avoiding exhaustive scrutiny of device type/characteristic combinations.
2
Data review and comparison are streamlined in a single place, with convenient filtering and display options.
3
Machine learning operates autonomously, freeing data analysts from constant monitoring. Automated feedback enhances their focus on critical tasks.

Accelerating manual defect detection through ML-enabled feedback loop

1
The model reduces analysis time by suggesting data for review, avoiding exhaustive scrutiny of device type/characteristic combinations.
2
Data review and comparison are streamlined in a single place, with convenient filtering and display options.
3
Machine learning operates autonomously, freeing data analysts from constant monitoring. Automated feedback enhances their focus on critical tasks.

1
The model reduces analysis time by suggesting data for review, avoiding exhaustive scrutiny of device type/characteristic combinations.
2
Data review and comparison are streamlined in a single place, with convenient filtering and display options.
3
Machine learning operates autonomously, freeing data analysts from constant monitoring. Automated feedback enhances their focus on critical tasks.

Accelerating_manual_defect_detection_through_ML-enabled_feedback_loop_cover

1
The model reduces analysis time by suggesting data for review, avoiding exhaustive scrutiny of device type/characteristic combinations.
2
Data review and comparison are streamlined in a single place, with convenient filtering and display options.
3
Machine learning operates autonomously, freeing data analysts from constant monitoring. Automated feedback enhances their focus on critical tasks.

1
Successfully productionised 25 Machine Learning models and over 200 pipelines.
2
Built as a fully automated end-to-end process covering data ingestion, feature generation, model training, deployment/serving, using decoupled components and configuration-driven architecture.
3
Ensured the system became robust, adaptable and scalable to large-scale data processing and model deployment.

Automatization of end-to-end machine learning pipelines

1
Successfully productionised 25 Machine Learning models and over 200 pipelines.
2
Built as a fully automated end-to-end process covering data ingestion, feature generation, model training, deployment/serving, using decoupled components and configuration-driven architecture.
3
Ensured the system became robust, adaptable and scalable to large-scale data processing and model deployment.

1
Successfully productionised 25 Machine Learning models and over 200 pipelines.
2
Built as a fully automated end-to-end process covering data ingestion, feature generation, model training, deployment/serving, using decoupled components and configuration-driven architecture.
3
Ensured the system became robust, adaptable and scalable to large-scale data processing and model deployment.

Automatization of the end-to-end Machine Learning pipeline_1

1
Successfully productionised 25 Machine Learning models and over 200 pipelines.
2
Built as a fully automated end-to-end process covering data ingestion, feature generation, model training, deployment/serving, using decoupled components and configuration-driven architecture.
3
Ensured the system became robust, adaptable and scalable to large-scale data processing and model deployment.

1
Reduced email composition time from minutes to under 5 seconds, saving significant resources.
2
Increased response rates through improved contextual personalization and streamlined messaging.
3
Enabled the processing of thousands of email requests daily, effectively scaling outreach efforts.

Email crafting with an AI-powered email generator

1
Reduced email composition time from minutes to under 5 seconds, saving significant resources.
2
Increased response rates through improved contextual personalization and streamlined messaging.
3
Enabled the processing of thousands of email requests daily, effectively scaling outreach efforts.

1
Reduced email composition time from minutes to under 5 seconds, saving significant resources.
2
Increased response rates through improved contextual personalization and streamlined messaging.
3
Enabled the processing of thousands of email requests daily, effectively scaling outreach efforts.

Email_crafting_with_an_AI-powered_email_generator_image-min.jpg

1
Reduced email composition time from minutes to under 5 seconds, saving significant resources.
2
Increased response rates through improved contextual personalization and streamlined messaging.
3
Enabled the processing of thousands of email requests daily, effectively scaling outreach efforts.

More to read

1
From building data foundations to leveraging generative AI, efficiency gains and market consolidation. Learn how insurers can navigate the transition with a clear roadmap.

Digitalisation and AI in insurance

1
From building data foundations to leveraging generative AI, efficiency gains and market consolidation. Learn how insurers can navigate the transition with a clear roadmap.

1
From building data foundations to leveraging generative AI, efficiency gains and market consolidation. Learn how insurers can navigate the transition with a clear roadmap.

1
From building data foundations to leveraging generative AI, efficiency gains and market consolidation. Learn how insurers can navigate the transition with a clear roadmap.

1
Discover how agentic AI is transforming underwriting in insurance and why a human-in-the-loop approach is essential—from evolving operating models to practical HITL architectures.

Human-in-the-loop in Agentic AI underwriting

1
Discover how agentic AI is transforming underwriting in insurance and why a human-in-the-loop approach is essential—from evolving operating models to practical HITL architectures.

1
Discover how agentic AI is transforming underwriting in insurance and why a human-in-the-loop approach is essential—from evolving operating models to practical HITL architectures.

1
Discover how agentic AI is transforming underwriting in insurance and why a human-in-the-loop approach is essential—from evolving operating models to practical HITL architectures.

1
From tackling data chaos to automating workflows and empowering underwriters as strategic decision-makers.

The role of AI agents in accelerating the insurance underwriting process

1
From tackling data chaos to automating workflows and empowering underwriters as strategic decision-makers.

1
From tackling data chaos to automating workflows and empowering underwriters as strategic decision-makers.

1
From tackling data chaos to automating workflows and empowering underwriters as strategic decision-makers.

1
From architecture and lifecycle to benefits, challenges and choices, how to build a platform that scales analytics, governance and business impact.

What is a dataplatform?

1
From architecture and lifecycle to benefits, challenges and choices, how to build a platform that scales analytics, governance and business impact.

1
From architecture and lifecycle to benefits, challenges and choices, how to build a platform that scales analytics, governance and business impact.

1
From architecture and lifecycle to benefits, challenges and choices, how to build a platform that scales analytics, governance and business impact.