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AI platform for companies

Experience how a modern AI platform connects data, models and workflows. For productive AI in production, development and operations.

What is an AI platform? The backbone of productive artificial intelligence

An AI platform is the technical basis for the productive use of artificial intelligence in a company. It bundles all the central functions required to efficiently develop, provide and operate AI applications in the long term. This includes processing large volumes of data, training and versioning models and connecting them via APIs.

The platform combines methods from data engineering, ML ops, model management and monitoring - in an infrastructure that can be expanded modularly and adapted to the specific needs of the company. The aim is to shorten development times, automate processes and securely integrate AI into existing systems.

MaibornWolff supports companies in setting up such platforms - from the technological architecture to the productive application.

Arrange a free consultation appointment

Building an AI platform: Your advantages with MaibornWolff

The right architecture for your AI goals

We develop platforms along your existing data science and MLOps processes.

Expertise from several disciplines

Our teams combine data engineering, software architecture and AI expertise.

Flexible in the cloud and on-premise

Whether AWS, Azure, GCP or data center - we build scalable solutions for every environment.

Focus: safety and quick benefits

GDPR compliance, governance and short time-to-value are part of our standard approach.

Our references and projects

With an AI platform for companies, you have the opportunity to revolutionize working methods and processes. Would you like to know what we have achieved for other companies beforehand? No problem - take a look at our references!

To all references

Research: AI-supported robotics for employees with physical limitations

With demographic change, employees with physical limitations need better support to remain productive. Assistance robots can help, but are not flexible enough. The KiRo4LeMi research project aims to use AI to dynamically adapt robots to individual changes in performance. Using digital models and "living personas", the AI optimizes robot operation in real time.
See robotics reference

Research: AI-supported robotics for employees with physical limitations

3 years

research project, funded by the Bavarian Ministry of Economic Affairs

Design of the robot

more individuality thanks to AI

Digital twin

helps to be able to react quickly during operation

See robotics reference
Siemens: AI demand prediction platform for industrial production planning

Siemens is looking to the future with the AI Demand Prediction Platform. Thanks to machine learning and AutoML, precise demand forecasts can be created for over 100 products and production can be better planned. Launched as a proof of concept, the platform quickly developed into a system that can be used productively. The self-service web application will soon be used in other plants.
See Siemens reference

Siemens: AI demand prediction platform for industrial production planning

Project duration

Since February 2022

Proof of concept

in a few weeks

Time series prediction

for 100 different products

See Siemens reference
TÜV NORD GPT: Development of AI assistance

Find relevant information faster by chatting with documents? It's possible! The TÜV NORD GROUP uses GPT technology in the secure Microsoft Azure Cloud. With the aim of optimizing knowledge management and efficiency. The system enables new usage options within the testing group and is operated securely. Find out more about the innovative AI assistance system now.
See TÜV NORD reference

TÜV NORD GPT: Development of AI assistance

Project duration

since September 2023

33.000

GPT applications in the first month

ChatGPT Model 4

in the European Microsoft Azure Cloud

See TÜV NORD reference
digikoo: A data platform for the Azure Cloud

Structured geodata, automated quality assurance, seamless provision - we developed a powerful Snowflake data platform on Azure for Digikoo. It makes analysis easier for data scientists and lays the foundation for precise forecasts and new use cases.
See digikoo reference

digikoo: A data platform for the Azure Cloud

5 months

Project duration

Climate change

Plan digitally and implement efficiently

Foundation data platform

Microsoft Azure Cloud

See digikoo reference
Bayernwerk: Knowledge management via teams

Experience must not be lost - Bayernwerk digitizes the knowledge of long-standing employees. MaibornWolff designed an intuitive MS Teams app with a clear UX/UI. Close collaboration, lived Scrum values and user-centered development make the app a success. Promoting exchange, optimizing processes - this is how knowledge transfer works today.
Read more

Bayernwerk: Knowledge management via teams

6 months

Project duration

Target:

Identify implicit knowledge

Requirements:

a user-centered, intuitive and clear UX/UI design

Read more

1. Define goals and requirements

Not every AI solution is suitable for every company. This is precisely why it is worth taking a closer look: You gain clarity about whether, how and where the use of AI actually makes sense in your company. Together, we analyze your goals, the technological requirements and the specific application scenarios. This will provide you with a sound basis for deciding on an AI platform that brings measurable benefits and can really be integrated into your day-to-day operations.

2. Technology selection and architecture design

3. Data integration and preparation

4. Model development and MLOps setup

5. Security, monitoring and operation

6. Scaling and business enablement

What types of AI platforms are there?

When introducing AI platforms, companies are faced with a fundamental decision: standard solution, individual development or open source approach? Each of these approaches offers its own opportunities, but also certain limitations.

Preconfigured platforms

Many cloud providers offer AI platforms "out of the box". These solutions, including Google Vertex AI, Azure and Amazon SageMaker, offer a quick way to get started, contain common tools for data processing, model training and deployment and can be activated with just a few clicks. The advantage lies in the rapid availability and proven infrastructure. However, these platforms are often not very flexible and can only be adapted to company-specific processes or data protection requirements to a limited extent.

Customized platforms

Individual platforms are tailor-made for the respective company. They are precisely tailored to the existing data sources, business logic and IT systems. This is a decisive advantage, especially for organizations with high requirements in terms of security, depth of integration or scalability. The setup requires more coordination and initial effort, but pays off in the long term through efficiency and strategic independence. Examples include tailor-made AI stacks or custom GPTs.

Open source platforms

Frameworks such as Kubeflow, MLflow or Ray offer a modular open source approach for building your own platforms. They enable maximum control and freedom of customization with high cost transparency. However, companies need the appropriate know-how internally to ensure operation, maintenance and further development. In addition, integration into existing systems is more complex than with commercial solutions.

Criterion	Preconfigured platform	Customized platform	Open source platform
Implementation effort	Low - quickly ready to go	Medium to high - depending on requirements	High - technical know-how required
Flexibility	Restricted - predefined functions	High - fully adaptable to business processes	High - complete control
Adaptation to IT landscape	Partially possible	Customized integration	Possible, but more complex
Operation & maintenance	Taken over by the provider	By internal or external team - possibly higher expenses for personnel & updates	Independently or under supervision
Data protection & compliance	Limited configurability - much predefined by provider	Configurable - requires clear responsibilities	Configurable - requires clear responsibilities
Cost structure	Usage-based license and cloud costs	Project-dependent - possibly cheaper in the long term	No license costs - but effort for operation & maintenance
Scalability	Good - within the provider infrastructure	Very good - scalable to measure	Possible - but with technical effort
Typical use	Fast MVP, proof of concept	Strategic AI development, complex corporate structures	Research, individual projects, high flexibility

Futuristic, glowing robot with blue eyes stretches a hand forward - in front of an abstract, dark background with light particles.

Is your company AI-ready? Here is the answer

AI is the buzzword of our time. Many companies are jumping on the AI bandwagon without knowing what they are getting into or what is actually behind it. The results are usually very predictable: a lack of clarity about responsibilities and benefits, a lack of acceptance and an actually powerful tool that is either used incorrectly or not used properly at all.

We will show you whether your company is already set up in such a way that you can benefit from using an AI platform - in MaibornWolff's AI readiness check.

To the AI readiness check

AI as a catalyst for data-driven business processes

By using an AI platform, you create the conditions for using data strategically. Be it to automate decisions, for personalized services or to increase the efficiency of operational processes.

Scalable basis for technological maturity

Focus on security, governance and control: whether on-premise, cloud or hybrid

Strengthening collaboration, breaking down silos

Bringing innovation to application faster

Strategic technology consulting

We help you find the right approach for your AI platform - with sound advice on make-or-buy, architecture models, technology stacks and operating models.
Custom GPTs & agent workflows

We develop individual GPT instances and AI agents that access your internal data, automate processes and provide targeted support for your teams, from supplier evaluation to knowledge management.
Tool integration & process automation

Existing tools (e.g. ERP, CRM, DMS) can be seamlessly integrated into the platform. This creates an end-to-end, AI-supported workflow with real automation potential.
RAG, vector databases & document knowledge

We link unstructured company data with generative AI, for example through retrieval augmented generation, semantic search and high-performance vector databases.
Adoption, training & enablement

To ensure that your platform not only works, but is also used, we support your teams through shadowing, internal training formats and role-based enablement - from proof of concept to scaling.
Security, Audit & Governance

We think about data protection, role allocation and IT security from the outset, in line with regulatory requirements such as GDPR or ISO standards. Platform security is not an add-on for us, but standard.

FAQs: Frequently asked questions about AI platforms

Whether platform strategy, technology selection or implementation steps: In our projects, we repeatedly encounter exciting questions about the development and introduction of AI platforms. Here you will find answers to the most frequently asked questions about architecture, project progression and collaboration with MaibornWolff.

Do you have specific questions or would you like to discuss an idea? Then make an appointment directly or write to us - we will advise you personally and without obligation.

What type of AI platform is suitable for my company?

This depends on your goals, your existing or planned IT landscape and the desired degree of customization. Do you want to make knowledge available, automate processes or develop your own models? Then you need a platform that is designed precisely for this - preconfigured or customized, depending on your capacities and requirements.

Other questions that should be answered in advance: Will the platform run via cloud, on-premise or hybrid? The deployment should also match your data protection requirements.

It is important that the platform can be seamlessly integrated, scaled and operated - ideally with monitoring, governance and MLOps right from the start.
How long does it take to implement a production-ready AI platform?

Proof-of-concepts (PoCs) and initial productive MVPs (minimum viable products) can be implemented within four to eight weeks, depending on complexity, and in more complex cases within two to three months.
What technologies does MaibornWolff work with in the field of AI and MLOps?

MaibornWolff relies on a technology-open, future-proof stack that can be flexibly adapted to customer requirements and IT landscapes. It uses proven OpenAI technology for individual GPTs and modern Open Web components for the RAG user interface to connect document knowledge. Numerous other tools for automating internal processes can also be connected.

We combine these technologies to create customizable, agent-based AI platforms that fit seamlessly into your system landscape.
Can existing data sources and IT systems be integrated?
Yes, existing data sources can be integrated into your AI platform for AI-supported, centralized knowledge management. These are data sources such as:
- Word documents
- PDFs
- text files
- emails
Internal tools can also be integrated into workflows. This opens up the platform for existing IT ecosystems.
How does MaibornWolff ensure that data protection and compliance are adhered to?
The AI platform is designed to be GDPR-compliant from the ground up. Three deployment models are available:
- In the customer's cloud (e.g. Azure)
- On-premise in your own data center
- On-device (locally on individual devices)
Particularly important: no data leaves the company boundaries. This means that data protection is not a nice-to-have, but standard.
Does MaibornWolff also offer operation, support and training?
Yes, we attach great importance to adoption and enablement among our customers - this includes
- Involving users at an early stage
- Technical empowerment of customers through shadowing
- Focus on internal training
- Governance, role allocation and security are taken into account
Is it possible to integrate generative AI such as ChatGPT or image models into the platform?
Yes, the platform is based on OpenAI technology and enables the development and use of company-specific GPTs. One specific use case, for example, is an AI assistant that performs supplier evaluations. The integration of generative language models is therefore a central component of the platform strategy.

Generative image models can also be integrated into the platform. Companies use these for example for:
- Marketing and product communication: automated visualization of campaign ideas or product variants
- UX/design teams: initial drafts for interfaces, icons or mood boards from text prompts
- E-commerce & retail: dynamic image generation based on customer input, including for configurators
The integration of such models into existing workflows opens up new creative potential with full control over output, data flows and access rights.

AI platform for companies

What is an AI platform? The backbone of productive artificial intelligence

Building an AI platform: Your advantages with MaibornWolff

The right architecture for your AI goals

Expertise from several disciplines

Flexible in the cloud and on-premise

Focus: safety and quick benefits

Our references and projects

The path to an individual AI platform: our approach

1. Define goals and requirements

2. Technology selection and architecture design

3. Data integration and preparation

4. Model development and MLOps setup

5. Security, monitoring and operation

6. Scaling and business enablement

A small selection of our customers

Do you still have questions about developing an AI platform?

What types of AI platforms are there?

Preconfigured platforms

Customized platforms

Open source platforms

Criteria for the selection of AI platforms

Is your company AI-ready? Here is the answer

Your own AI platform: advantages for your company

AI as a catalyst for data-driven business processes

Scalable basis for technological maturity

Focus on security, governance and control: whether on-premise, cloud or hybrid

Strengthening collaboration, breaking down silos

Bringing innovation to application faster

Arrange your consultation appointment today!

AI platform can be developed: Our range of services

Strategic technology consulting

Custom GPTs & agent workflows

Tool integration & process automation

RAG, vector databases & document knowledge

Adoption, training & enablement

Security, Audit & Governance

FAQs: Frequently asked questions about AI platforms

What type of AI platform is suitable for my company?

How long does it take to implement a production-ready AI platform?

What technologies does MaibornWolff work with in the field of AI and MLOps?

Can existing data sources and IT systems be integrated?

How does MaibornWolff ensure that data protection and compliance are adhered to?

Does MaibornWolff also offer operation, support and training?

Is it possible to integrate generative AI such as ChatGPT or image models into the platform?