Contact now
Contact now
Bright, transparent data cube in neon colors on a dark background.

AI platform for companies

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

BMW Group Logo
DeutscheBahn_logo-2
Creditreform Logo
DERTOUR
jochen-schweizer
Dräger Logo
kuka
BMW Group Logo
DeutscheBahn_logo-2
Creditreform Logo
DERTOUR
jochen-schweizer
Dräger Logo
kuka
ProSieben_Logo_2015-2
Mercedes
Volkswagen Logo
DEKRA
stihl
Sonax_logo
Weidmüller logo
Das Logo der Bundesagentur für Arbeit
ProSieben_Logo_2015-2
Mercedes
Volkswagen Logo
DEKRA
stihl
Sonax_logo
Weidmüller logo
Das Logo der Bundesagentur für Arbeit

The most important points at a glance

An AI platform brings together all the features a company needs to efficiently develop, deploy, and sustainably operate AI applications.

  • An AI platform integrates data processing, model training, MLOps, and monitoring into a centralized, modular infrastructure.

  • MaibornWolff provides support for building custom AI platforms—from architecture to production.

  • There are three types of platforms to choose from: preconfigured cloud solutions, custom platforms, and open-source approaches.

  • Data protection (GDPR), governance, and scalability are integral components from the very start—not just add-ons.

  • Proof-of-concepts and initial production-ready MVPs can be implemented in four to eight weeks.

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

MaibornWolff develops AI platforms that integrate seamlessly with your existing processes and IT infrastructure—secure, scalable, and with a clear focus on measurable benefits.

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.

An abstract illustration shows a human figure made of geometric shapes in a scheme of pink and purple.
The most important success criterion for this machine learning project is a standardized and scalable solution that effectively integrates the diversity of our products as well as manually planned plants. This synergy, customized by MaibornWolff, shows the true value of the project.
Dr. Daniel Patrick Kilian, Senior Key Expert Data Science & AI, Digital Industries IT at Siemens

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
  • Close-up of colorful puzzle pieces floating in the air, each piece engraved with a different insurance symbol.
    STARTRAIFF: Business Intelligence for the sales force
    To the STARTRAIFF reference
    CloudData/Data PlatformsApps

    Aggregation of internal customer data & external data in a single web application

    To the STARTRAIFF reference

    Data bundling & analysis with Amazon Bedrock

    To the STARTRAIFF reference

    Intuitive user interface for sales, 88% reduced preparation time before customer visits

    To the STARTRAIFF reference
  • Two orthopaedic surgeons view a transparent 3D hologram of the skeleton and musculature on an elegant tablet interface, surrounded by floating UI panels.
    Health.exe: AI-supported platform creates training plans for patients
    To the Health.exe reference
    CloudData/Data PlatformsApps

    AI-supported service for orthopedic & sports medicine practices

    To the Health.exe reference

    Cloud-based web application for doctors for the automated, evidence-based creation of individually tailored patient training plans

    To the Health.exe reference

    New revenue source without fixed costs, higher patient retention, AI-supported & guideline-based

    To the Health.exe reference
  • A robotic arm places precise digital data in a futuristic, dark room.
    Research: AI-supported robotics for employees with physical limitations
    See robotics reference
    Embedded Systems & RoboticsIndustry 4.0Manufacturing

    Customized assistance robots for people with physical disabilities in production

    See robotics reference

    Integration of AI for automated adaptation of robots to people's capabilities

    See robotics reference

    Effective empowerment of people with physical disabilities

    See robotics reference
  • A technician in a green Siemens jacket sits in front of a computer on a factory floor with industrial equipment in the background.
    Siemens: AI demand prediction platform for industrial production planning
    See Siemens reference
    CloudData/Data PlatformsIndustry 4.0

    Machine learning for time series forecasting

    See Siemens reference

    AutoML for automated adaptation of models to different data

    See Siemens reference

    Unified, scalable solution, optimized inventory costs, efficiency gains

    See Siemens reference
  • Two women are standing in a workshop. One woman is holding a tablet in her hands.
    TÜV NORD GPT: Development of AI assistance
    See TÜV NORD reference
    AppsWeb & Portal PlatformsPublic/Administration

    Secure operation of AI in the European MS Azure cloud environment

    See TÜV NORD reference

    Frontend & backend via MS Azure App, "Chat with your PDF" for TÜV employees

    See TÜV NORD reference

    Quick implementation of new technologies (AI), strengthening knowledge management

    See TÜV NORD reference
  • Large rollers on conveyor belt in factory.
    Planning systems: Optimizing the capacity utilization of pressing plants
    See reference
    Data/Data PlatformsIndustry 4.0Manufacturing

    Centralized planning of component manufacturing for cost- & resource-optimized production capacity worldwide

    See reference

    Conversion from local processing with fat clients to a client-server application, migration to the cloud

    See reference

    Data-based planning & calculation of different manufacturing scenarios & site-specific production costs

    See reference
  • Header_Global-Requirements-Planning-System-for-Workforce-2-16-9
    Global workforce planning system
    See reference
    CloudData/Data PlatformsPublic/Administration

    Centralized web-based IT system to replace individual isolated solutions

    See reference

    Event sourcing for planning & analytics, domain-driven design, cloud migration

    See reference

    Easy updates, expansion, maintenance, optimized security

    See reference

The path to an individual AI platform: our approach

An AI platform only unfolds its value if it has a stable foundation - technically, strategically and organizationally. Our approach follows a clear plan from the initial clarification of objectives through to secure operation.

abstract-linines 3-1

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

A small selection of our customers

Two people working outdoors on a laptop at a table.

Do you still have questions about developing an AI platform?

Contact us now

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.

Criteria for the selection of AI platforms

The type of AI platform that is right for you depends heavily on your business objectives, the resources available and the security and scalability requirements. MaibornWolff supports you in choosing the right approach and implementing it consistently. Here are the most important criteria that we use to find the best fit for your AI platform together:

CriterionPreconfigured platformCustomized platformOpen source platform
Implementation effortLow - quickly ready to goMedium to high - depending on requirementsHigh - technical know-how required
FlexibilityRestricted - predefined functionsHigh - fully adaptable to business processesHigh - complete control
Adaptation to IT landscapePartially possibleCustomized integrationPossible, but more complex
Operation & maintenanceTaken over by the providerBy internal or external team - possibly higher expenses for personnel & updatesIndependently or under supervision
Data protection & complianceLimited configurability - much predefined by providerConfigurable - requires clear responsibilitiesConfigurable - requires clear responsibilities
Cost structureUsage-based license and cloud costsProject-dependent - possibly cheaper in the long termNo license costs - but effort for operation & maintenance
ScalabilityGood - within the provider infrastructureVery good - scalable to measurePossible - but with technical effort
Typical useFast MVP, proof of conceptStrategic AI development, complex corporate structuresResearch, 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

Many companies invest in AI without first assessing the necessary prerequisites—with predictable consequences: unclear responsibilities, a lack of acceptance, and a tool that fails to reach its full potential.

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

Your own AI platform: advantages for your company

A well-designed AI platform can deliver tangible strategic and operational benefits for your business—provided it is aligned with your business objectives:

abstract-linines 2

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

Arrange your consultation appointment today!
Book an appointment now

AI platform can be developed: Our range of services

  • 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 incorporate data protection, role allocation, and IT security from the very beginning, in line with regulatory requirements such as the GDPR, the EU AI Act, and ISO standards. For us, platform security isn’t an add-on—it’s 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-agnostic, future-proof stack that can be flexibly adapted to customer requirements and IT environments. The platform utilizes leading large language models—including those from OpenAI, Anthropic, and Google—for personalized AI assistants, RAG for integrating document knowledge, and modern components for user interfaces. Additionally, numerous other tools for automating internal processes can be integrated.

    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 from the ground up to be GDPR- and EU AI Act-compliant. Three deployment models are available:

    • In the customer's cloud (e.g., Azure)
    • On-premises in your own data center
    • On-device (locally on individual devices)

    Most importantly: No data leaves the company’s premises. This means that data protection is not just a nice-to-have, but the standard. In addition, we comply with the requirements of the EU AI Act—from risk classification and transparency obligations to governance documentation.

  • 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 built on leading large language models—including models from OpenAI, Anthropic, and Google—and enables the development and use of company-specific GPTs. One specific use case is an AI assistant that handles supplier evaluations. The integration of generative language models is thus a central component of the platform’s strategy.

    Similarly, generative image models can 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 designs for interfaces, icons, or mood boards based on text prompts
    • E-commerce & Retail: dynamic image generation based on customer input, including for configurators

    Integrating such models into existing workflows unlocks new creative possibilities while maintaining full control over output, data flows, and access rights. 

Find what suits you best
Refine your search
clear all filters