Platform Service Excellence
savvytec develops scalable software platforms for data-intensive industries. We leverage years of platform expertise from automotive, healthcare, the public sector, and energy to address challenges in research and business—enhanced by proven, practical use of AI.
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We are experts with 14+ years of platform experience
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Platforms for Mercedes-Benz, healthcare, energy, and more
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Proven use of AI in commercial projects
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We serve both research and industry equally
No thanks to isolated solutions—digital systems only unlock their value when they work together. When domain systems remain isolated across departments, locations, or assets, complexity and costs increase. But how can innovation emerge if information doesn’t flow?
AI is in constant interaction with a changing reality. Without a platform, there is no way to detect model drift, retrain models, or seamlessly deploy new versions into operations. High-quality, stable AI is therefore less a question of models and more a matter of platform decisions.
Why companies need a platform
The value of data does not come from volume, but from structure and meaning. Consistent data models and semantic harmonization are essential to break down silos and enable data-driven decisions as well as reliable AI applications.
Open, interoperable architectures are the structural prerequisite for sustainable platform economies. They enable continuous quality improvement and flexible adaptation of smart services—because strategic capability always starts with architecture.
We address challenges from both research and industry.
Proven Platform Expertise
For over 14 years, we have built software platforms across a wide range of industries and use cases. From automotive and healthcare to public-sector applications and energy—leveraging proven patterns, robust architectures, and reliable processes.
Mercedes-Benz:
• Enterprise platforms in the automotive sector
Korian / Healthcare:
• Platform solutions for the healthcare sector
Corona Visitor Documentation:
• Platforms for visitor registration and contact tracing in the public sector
Enercept / Energy Sector:
• Energy platform for district networks and renewable energy systems
Architecture that connects
We don’t think in isolated systems, but in architectures. Scalable cloud infrastructures, semantic data models, and interoperable interfaces—we connect heterogeneous system landscapes into a cohesive, functioning whole.
✓ Scalable reference architectures for complex system landscapes
✓ Unified data models for heterogeneous sources
✓ Open standards and interoperable interfaces
Proven AI in Practice
We have successfully applied AI in real-world commercial projects. This experience flows into new platform initiatives—from conception and integration to the reliable operation of AI components.
✓ Proven use of AI in real-world business projects
✓ Automated drift detection and model lifecycle management
✓ AI governance for transparent, trustworthy results
What you gain with savvytec
Our reference architectures are designed not for a single project, but for an ecosystem. Open interfaces and scalable infrastructure ensure your system grows with you.
We consistently rely on open data models, standardized APIs, and proven industry standards. Your data and systems remain under your control.
AI that has already been proven in commercial projects—featuring drift detection, controlled model updates, and governance that works in production.
Proven architecture patterns, release processes, and integration concepts from industry significantly accelerate the path from prototype to production.
We apply our platform expertise to serve both research and commercial use cases—GDPR-compliant and built on European infrastructure.
Four pillars of our platform expertise
“The foundation everything is built on”
We design scalable, interoperable target architectures for complex system landscapes—whether cloud-native, edge–cloud continuums, or classic enterprise platforms.
What we deliver:
• Architecture design for scalable platforms
• Interface specifications (data, services, AI)
• Open standards and interoperable architectures
• Scalable patterns for heterogeneous system landscapes
Methodology:
Service-oriented architectures (SOA), domain-driven design, arc42 architecture documentation, API-first design
“Turning data silos into data knowledge”
We create unified, machine-readable data models for heterogeneous systems and sources. Different data sources, protocols, and formats are harmonized into a consistent data space.
What we deliver:
• Standardized data models for industry-specific requirements
• Semantic harmonization of disparate data sources
• Integration based on open data space principles
• Documented data and semantic standards
Typical data sources:
IoT sensors, ERP systems, domain applications, external APIs, real-time data streams
“AI that works in production”
We integrate AI components into existing platforms and ensure their safe, transparent operation. Proven in commercial projects—not just theory, but real-world practice.
What we deliver:
• Drift detection: automatic identification of when an AI model loses validity
• Parallel operation: new AI versions run alongside proven ones—controlled transition
• Model lifecycle management: from training and deployment to decommissioning
• Governance documentation: full traceability of every AI decision
Governance principles:
• Human-in-the-loop: AI makes recommendations, humans decide in critical cases
• Traceability: every AI decision is documented and auditable
• Honesty: AI outputs are assessed realistically—no overconfidence
• Economic value: AI must deliver real, measurable benefits
“From prototype to production—securely”
The transition from development to operations is the most critical phase of any software project. We bring proven industrial release and rollout processes—combined with feedback loops from operational data for continuous improvement.
What we deliver:
• Rollout concepts for cloud and edge deployments
• Release processes with defined quality gates
• Feedback of operational data into development
• Continuous deployment in industrial environments
• Operations and release process models
Our process:
Development → Staging → Canary release → Monitoring → Full rollout
(with operational data fed back into development)
Industries for the configurator
Over many years, we have developed enterprise platforms for Mercedes-Benz—ranging from connected vehicle systems and cloud-based services to data-driven processes. The automotive industry’s high demands for scalability, security, and quality have shaped the way we work.
For the healthcare sector, we implemented a platform solution with Korian that processes sensitive data, meets strict compliance requirements, and remains user-friendly at the same time. Data protection and reliability are top priorities here.
In a short period of time, we developed and deployed platforms for visitor registration and contact tracing. High load, rapid availability, and ease of use—proof of our ability to deliver scalable solutions under pressure.
Together with our partner EOW, we are building Enercept, a platform for the energy sector—covering district networks, renewable energy systems, and intelligent control. This is where our full platform expertise comes together, enhanced by the first use of AI in commercial projects.
FAQs
You’ll find many answers in our FAQs.
Our platform architecture follows a proven layered model—each layer addresses a clearly defined area of responsibility.
Layer 4 – Governance & Compliance
Control. AI models are monitored, versioned, and handed over in a controlled manner. Every decision is traceable. Compliance requirements are documented automatically.
AI control, drift detection, audit trail
Model lifecycle, parallel operation, reporting
→ Secure, transparent operations
Layer 3 – Applications & Services
Logic. Open APIs enable the integration of external services. Domain-specific business logic is provided as clearly defined services. Event-driven design ensures real-time responsiveness.
Open APIs, service integration, workflows
Domain services, event-driven architecture
→ Business logic and processes
Layer 2 – Data & Integration
Unification. Heterogeneous data sources are transformed into a consistent, semantic data model—the foundation for all analytics and AI.
Data models, semantics, ETL pipelines
Data mesh, harmonization, data spaces
→ A unified, machine-readable data landscape
Layer 1 – Cloud & Edge Infrastructure
Foundation. Cloud-native platforms ensure system-wide availability. Edge systems enable real-time processing on site when required. Container-based deployments allow flexible scaling.
Cloud platforms, edge systems, networking
Containers, orchestration, monitoring
→ Scalable, distributed infrastructure
We leverage our many years of experience in building scalable platforms to address challenges from both research and industry. We combine proven industrial methodologies with innovative approaches—including AI that has already been validated in commercial projects.
We bring together deep platform expertise and AI proven in practice—addressing both research-driven and commercial challenges.
Platform expertise:
14+ years of experience in building data-intensive platforms—from automotive (Mercedes-Benz) and healthcare (Korian) to energy (Enercept). Proven architectures that stand the test of real-world use.
AI proven in practice:
AI applications that have already been validated in commercial projects. We transfer this know-how into new contexts—supported by governance, drift detection, and transparent, traceable results.
Research challenges:
We apply our platform expertise to technically implement research challenges—scalable, reproducible, and at industrial-grade quality.
Commercial applications:
At the same time, we address commercial challenges—from digitalization and process optimization to data-driven, platform-based business models.
We transfer proven patterns from more than a decade of platform development into new domains. Our approach is built on:
Scalable architectures
• robust enough for both research prototypes and production systems
Unified data models
• bringing together heterogeneous sources and preparing them for AI usage
AI integration
• based on our experience from commercial projects, with clear governance
Robust operational concepts
• from prototype to production, with defined quality gates
savvytec is an established platform software developer with over 14 years of experience. We build scalable platforms for a wide range of industries and transfer proven methods to new markets and challenges.
Our story
The 2010s – The platform years
For more than a decade, we have been developing data- and AI-driven platforms and services—initially for the automotive industry (including Mercedes-Benz), and increasingly across industries. During this time, we learned what it takes to operate complex, distributed systems reliably.
Healthcare & public sector
With Korian in the healthcare sector and platforms for corona visitor documentation, we demonstrated that our platform expertise works across industries—handling sensitive data, high loads, and strict compliance requirements.
2024/2025 – The energy sector
The search for new business fields led us to the energy sector. Together with EOW, we founded Enercept—a startup that combines domain expertise from the energy sector with industrial-grade platform competence. savvytec becomes the software backbone of a new ecosystem.
Today – Platforms for research and industry
We apply our platform expertise to address challenges in both research and industry. Enhanced by AI proven in commercial projects, we deliver solutions that are both innovative and production-ready.
Structure:
Clear architectures, defined processes, documented decisions. We believe: good structure is the foundation for everything else.
Reliability:
We deliver what we commit to. Honest status reporting, realistic estimates, transparent communication. “Status must be honest.”
Trust:
Trust is built through structure and reliability. It is the economic foundation of every collaboration—with customers, partners, and within the team.
Our core team covers all disciplines required to build and operate scalable platforms:
Solution Architect:
Overall architecture, platform design, technical leadership
Data Architect:
Data models, semantics, interoperability
MLOps / AI Engineer:
AI integration, drift detection, model lifecycle
Backend Developer:
Platform development, API integration, services
Any further questions?
Maybe we haven't answered your question yet. But that doesn't matter!
Daniel will be happy to help you.
Daniel Pudelko
CEO