AI projects rarely fail because of the model. Poor data quality, data silos and outdated platforms with rising licence and operating costs stop companies from making full use of their data. We design and build high-performance data and AI platforms, from strategy through to operations, and turn your data foundation into measurable business value:
- Better decisions: trustworthy, contextualised data where decisions are made, from operational teams to executive management.
- Lower cost, less complexity: simpler platform landscapes, less redundancy and lower ongoing costs for managing and providing data.
- AI that scales: a structured, controlled data foundation on which real-time analytics, ML and AI applications and AI agents work with company data securely and reliably.
From data source to intelligent business application
What we offer
Platform review and target architecture
We analyse your data and system landscape and develop a robust target architecture and a data governance framework. This gives you a sound basis for long-term decisions and helps you avoid bad investments.
Building a new data platform
We build a data platform that makes your data reliably usable for analytics, reporting and AI and grows with you. It can include a data warehouse, lakehouse, data pipelines, real-time streaming and interfaces.
Modernising and migrating data platforms
We modernise or migrate legacy data platforms, break down data silos and resolve performance, maintainability and cost issues. This makes it easier to extend your platform for new use cases.
Making your data platform AI-ready
We make your data securely and contextually usable for AI. With a semantic layer, ontology, vector search, RAG and model serving, you put AI into production faster and stay in control of sensitive data.
Governance and security
We help you use your data securely, traceably and in line with regulations. Clear access rights and transparent data flows reduce risk without unnecessarily slowing down business units.
Operations and continuous development
We ensure reliable operations and keep developing your data platform. This keeps data quality, performance and costs under control over the long term.
“The era of big data treasure hunting is over. Data should be generated, collected and processed with care in order to create a meaningful and reliable foundation for the use of data-centred AI.”
Wilhelm Kleiminger
Head of Data Science, Ergon
How we get started together
Whether it is integrating data sources for better reporting or preparing your data for AI, we work with you to identify a sensible starting point that delivers real value, and grow from there.
Turn your data into a competitive advantage: talk to our experts about which first use case is worth pursuing and what it takes to get started.
Your benefits with data engineering from Ergon
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Technology freedom instead of vendor dependence: we give you complete flexibility in your choice of technology, from open-source to cloud data platforms.
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Tailored delivery: whether it is a classic data warehouse, a modern lakehouse architecture or real-time streaming for IoT, we build the solution around your business goals.
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Built for operations from day one: with automated, scalable solutions and DataOps, we create maintainable platforms that can evolve cost-effectively.
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Secure by design: we place great emphasis on security and data protection compliance in line with the GDPR, the Swiss FADP and industry standards.
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One partner from start to finish: with over 40 years’ experience in software development and data projects, Ergon brings together business analysis, architecture, software engineering and operations.
FAQ
A robust data foundation provides the technical and organisational basis for analytics, AI and data-driven decisions. These FAQs explain how modern data architectures are built, what governance and infrastructure need to deliver, and what matters most in migration and scaling.
What is a data foundation?
A data foundation is the technical and organisational basis for capturing and managing data reliably and making it usable for analytics or AI. Instead of manually maintained spreadsheet silos, it provides automated data pipelines and clear data governance.
Why is a data foundation important for AI projects?
A data foundation is important for AI projects because AI results are only as good as the data behind them. A knowledge chatbot can only answer questions reliably if the documents it draws on are structured and can be found. That is exactly what a data foundation delivers. It also keeps data up to date during operation and provides a scalable infrastructure.
Which data infrastructure is right for my company?
The right data infrastructure and platform depend on data volumes, requirements and the existing IT landscape. A proven approach is an architecture that separates compute and storage and remains cost-effective as data volumes grow. Open standards matter more than the choice of vendor. They preserve freedom of choice, prevent vendor lock-in and make it possible to keep confidential data on-premises where needed.
How does data governance support data protection and compliance?
Data governance supports data protection and compliance by defining rules, roles and processes for handling data and clarifying responsibilities. This can be enforced technically through need-based access rights and end-to-end auditing. Tiered protection classes are also possible: public data in the cloud, confidential data on-premises.
When is a data migration worthwhile as part of a data foundation?
A data migration as part of a data foundation is worthwhile when the existing system reaches its performance limits, drives up operating costs, makes analysis difficult or can no longer support new data and AI use cases. Migration projects with five times faster loading, lower costs and a go-live in less than a year show that the move pays off.
We look forward to hearing from you
We are pleased that you are interested in our services. Do you have any questions or a specific project idea? Tell us about it – with no commitment on your part. Our experts will contact you as soon as possible.
Glossary
A high-performing data architecture brings together data availability, data quality and governance to create a solid basis for analytical and AI-supported applications. This glossary explains the key building blocks that shape the planning, operation and further development of a data foundation.
Data architecture
Data architecture describes how data flows, data models, storage locations and interfaces are designed within an organisation. It defines how data is organised technically so that it remains secure, consistent and usable.
Lakehouse
A lakehouse combines the characteristics of a data lake with those of a data warehouse. It stores large volumes of data while supporting structured analysis on a single, unified data basis.
Vendor lock-in
Vendor lock-in occurs when a solution can only be separated from its vendor with considerable effort, for example because of proprietary formats or interfaces. Open standards, portable data models and clearly documented interfaces make it easier to switch vendors later.
Data products
Data products are well-defined, documented datasets with clear ownership that other teams can use directly. They turn one-off analyses into reusable building blocks.
Semantic layer
A semantic layer adds a layer of business meaning on top of technical data structures and describes metrics, dimensions and relationships consistently. Business units, analytics tools and AI applications all work from the same definitions.
Ontology
An ontology describes an organisation’s core business objects, such as customers, products, contracts or locations, and how they relate to one another. It ensures that business users, analytics and AI share the same model of business logic and relationships.
Real-time streaming
Real-time streaming is the continuous processing of data streams as soon as the data is generated. It is relevant wherever applications need to respond quickly to new events or readings, for example in IoT.
