PLATFORM SERVICE EXCELLENCE

For over 14 years, across all industries

savvytec develops scalable software platforms for data-intensive industries.  We leverage our many years of platform expertise in the automotive, healthcare, public sector, and energy industries  to solve problems in research and business—supplemented by tried-and-tested AI applications.

14+ years of platform expertise

Platforms for automotive, healthcare, energy, and much more

AI use tested in economic projects

Research & Economy

We have been building software platforms for a wide range of industries and use cases for over 14 years.

Mercedes-Benz:

Enterprise platforms in the automotive segment

Korian:

Platform solutions for the healthcare sector

Korian:

Platforms for visitor registration and tracking

Enercept:

Energy platform for area networks and renewable energy systems

Architecture that connects

We don't think in terms of individual systems, but rather in terms of architectures. Scalable cloud infrastructures, semantic data models, interoperable interfaces – we connect heterogeneous system landscapes to form a functioning unit.

Scalable architectures for complex system landscapes

Uniform data models for heterogeneous sources

Open standards and interoperable interfaces

Practiceproven AI application

We have already successfully used AI in commercial projects. We bring this expertise to new platform projects—from conception and integration to the reliable operation of AI components.

AI use tested in real-world economic projects

Drift detection and model lifecycle management

AI governance for trustworthy results

Why companies need a platform

Whether in the automotive, healthcare, or energy industries, the challenges are similar: isolated applications, lack of integration, no consistent architecture. We have solved these problems across industries.

AI without a platform

Open, interoperable architectures are the structural prerequisite for sustainable platform economies. They enable smart services to be continuously developed in terms of quality and adapted to meet specific needs—because strategic capacity for action always begins with architecture.

Unstructured data

The value of data does not come from volume, but from structure and meaning. Uniform data models and semantic harmonization are prerequisites for breaking down silos and enabling data-driven decisions and reliable AI applications.

Isolated solutions

No to isolated solutions – digital systems reveal their full potential when they work together. If specialist systems remain isolated across departments, locations, or facilities, complexity and costs increase – but how can innovation flourish if information does not flow?

Lack of architecture

AI is in constant interaction with a changing reality. Without a platform, there is no ability to recognize model aging, retrain, and seamlessly transfer versions into operation. High-quality and stable AI deployment is therefore less a question of model and more a question of platform.

AI without a platform
Open, interoperable architectures are the structural prerequisite for sustainable platform economies. They enable smart services to be continuously developed in terms of quality and adapted to meet specific needs—because strategic capacity for action always begins with architecture.
Unstructured data
The value of data does not come from volume, but from structure and meaning. Uniform data models and semantic harmonization are prerequisites for breaking down silos and enabling data-driven decisions and reliable AI applications.
Isolated solutions
No to isolated solutions – digital systems reveal their full potential when they work together. If specialist systems remain isolated across departments, locations, or facilities, complexity and costs increase – but how can innovation flourish if information does not flow?
Lack of architecture
AI is in constant interaction with a changing reality. Without a platform, there is no ability to recognize model aging, retrain, and seamlessly transfer versions into operation. High-quality and stable AI deployment is therefore less a question of model and more a question of platform.

What you gain with savvytec

DEFINITION

Four pillars of our platform expertise

CORE AREAS

Our platform references

ADVANTAGES

What you gain with savvytec

OUR PROCESS

How we build platforms

PROCEDURE

Platform expertise for research and industry

PLATFORM EXPERTISE

The four pillars

Our service areas build on each other – from architecture to operations. Each area has been tested in real projects in various industries.

Technical Reference Architecture

We design scalable, interoperable target architectures for complex system landscapes—whether cloud-native, edge-cloud continuum, or classic enterprise platforms.

What we deliver:

Architectural 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

Integration & Semantic Harmonization

We create uniform, 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:

Data models for industry-specific requirements

Semantic harmonization of different sources

Integration based on open data room principles

Documented data and semantic standards

Typical data sources:

IoT sensors, ERP systems, specialized applications, external APIs, real-time data streams

AI integration & governance

We integrate AI components into existing platforms and ensure their secure, traceable operation. Already tested in commercial projects – not just theory, but proven in practice.

What we deliver:

Drift detection: Automatic identification of when an AI model loses its validity

Parallel operation: New AI version runs alongside the tried-and-tested version – controlled transition

Model lifecycle management: From training to deployment to decommissioning

Governance documentation: Traceability of every AI decision

Governance principles:

Humans as final decision-makers:

AI makes suggestions, humans decide

Traceability:

Every AI decision is documented and auditable

Honesty:

Outputs are assessed realistically, no overconfidence

Economic efficiency:

AI must deliver real, measurable added value

Operating and handover concepts

The transition from development to operation is the most critical phase of any software project. We bring established release and rollout processes from industry—with feedback from operating data for improvement.

What we deliver:

Rollout concepts for cloud and edge deployments

Release processes with defined quality gates

Feedback of operating data into development

Continuous deployment in industrial environments

Operational and release process models

Process:

Development → Staging → Canary release → Monitoring → Full rollout (with feedback of operating data to development)

REFERENCES

Our platform references

For over 14 years, we have been building platforms for a wide variety of industries and use cases. This cross-industry experience is our greatest asset—and the foundation for every new project.

The result

“14 years of platform expertise from a wide range of industries—that is the basis on which we solve problems in research and business.”

Mercedes-Benz

For many years, we have been developing enterprise platforms for Mercedes-Benz – from connected vehicle systems and cloud-based services to data-driven processes. The requirements for scalability, security, and quality in the automotive industry have shaped the way we work.

Automotive platforms

Korian

For the healthcare sector, we have implemented a platform solution with Korian that processes sensitive data, meets strict compliance requirements, and is user-friendly at the same time. Data protection and reliability are our top priorities here.

Healthcare sector platform

Korian

In a short period of time, we developed and launched platforms for visitor registration and tracking. High load, fast availability, and ease of use—proof of our ability to deliver scalable solutions under pressure.

Public sector

Enercept

Together with our partner EOW, we are building Enercept, a platform for the energy sector – from area networks and renewable energy systems to intelligent control. This is where all our platform experience comes together, complemented by the first-ever use of AI in commercial projects.

Energy Platform

YOUR ADVANTAGES

What you gain with savvytec

Our reference architectures are not designed for a single project, but for an entire ecosystem. Open interfaces, scalable infrastructure—your system grows with you.

No vendor lock-in

We consistently rely on open data models, standardized APIs, and proven industry standards. Your data and systems remain yours.

Across all industries

Platform experience in automotive, healthcare, public sector, and energy—proven solution patterns that we apply to new challenges.

AI in practice

AI deployment that we have already tested in economic projects—with drift detection, controlled model updates, and governance that works in operation.

Fast time-to-production

Proven architecture patterns, release processes, and integration concepts from the industry significantly accelerate the path from prototype to productive operation.

Research & Economy

We use our platform expertise to address issues in both research and business – in compliance with GDPR and on European infrastructures.

OUR PROCESS

How we build platforms

Our platform architecture follows a proven layered model—each layer addresses a clearly defined area of responsibility.

Step 1

Cloud & Edge Infrastructure

The foundation. Cloud-native platforms for system-wide availability. Edge systems for real-time processing on site, if required. Container-based deployments for flexible scaling.

Cloud platforms, edge systems, network

Containers, orchestration, monitoring

→ Scalable, distributed infrastructure

Step 2

Data & Integration

Standardization. Heterogeneous data sources are converted into a consistent, semantic data model. The basis for any form of analytics and AI.

Data models, semantics, ETL pipelines

Data mesh, harmonization, data rooms

→ Uniform, machine-readable data environment

Step 3

Application & Services

The logic. Open APIs enable the integration of external services. Domain-specific business logic is provided as clearly defined services. Event-driven for real-time responsiveness.

Open APIs, service integration, workflows

Domain services, event-driven architecture

→ Technical logic and business processes

Step 4

Governance & Compliance

Control. AI models are monitored, versioned, and transferred in a controlled manner. Every decision is traceable. Compliance requirements are documented automatically.

AI control, drift detection, audit trail

Model lifecycle, parallel operation, reporting

→ Safe, traceable operation

PROCEDURE

Platform expertise for research and industry

We use our many years of experience in building scalable platforms to address issues in both research and business.

What we contribute

We combine deep platform expertise with proven AI applications, addressing both research and business issues.

Platform expertise

14+ years of experience in building data-intensive platforms – from automotive (Mercedes-Benz) to healthcare (Korian) to energy (Enercept). Proven architectures that stand up in practice.

Field-tested AI

AI application that we have already tested in economic projects. We bring this expertise to new contexts—with governance, drift detection, and traceable results.

Research questions

We use our platform expertise to implement research questions technically—scalable, reproducible, and at an industrial quality level.

Economical application

At the same time, we address economic issues – from digitalization and process optimization to data-driven business models based on platforms.

Our approach

We transfer proven patterns from over a decade of platform development to new domains. In doing so, we rely on:

Scalable architectures

that are viable for both research prototypes and production systems

Uniform data models

Merge heterogeneous sources and prepare them for AI use

AI integration

Building on our experience from economic projects, with clear governance

Robust operating concepts

From prototype to production, with defined quality gate Quality gates

Structure = Reliability = Trust

2010s

The Platform Years

For over a decade, we have been developing data- and AI-driven platforms and services—initially for the automotive industry (including Mercedes-Benz), then increasingly across a range of industries. We are learning what it means to reliably operate complex, distributed systems.

2010s

Healthcare & Public Sector

With Korian in the healthcare sector and platforms for COVID-19 visit documentation, we are proving that our platform expertise works across all industries—sensitive data, high load, strict compliance.

2024/2025

The energy sector

The search for new business areas leads us to the energy sector. Together with EOW, we found Enercept—a startup that combines domain knowledge from the energy sector with platform expertise from industry. savvytec becomes the software backbone of a new ecosystem.

status quo

Platforms for research and business

We use our platform expertise to address issues in research and business. Complemented by tried-and-tested AI applications from commercial projects, we create solutions that are both innovative and ready for production.