The Azure OpenAI Service is Microsoft’s service that enables organisations to run large language models within their own Azure environment – utilising the Azure platform’s security, identity and compliance mechanisms rather than via a public interface. Microsoft now bundles the available models under Microsoft Foundry, the platform for building and running AI applications in Azure.
However, the service on its own is not yet a solution. Between a functioning prototype and an AI service that can withstand the demands of day-to-day operations lie governance, a concept for roles and permissions, integration with ERP, DMS and CRM systems, and an operational model. This is precisely the gap we bridge: we enhance Microsoft Azure with security, governance and practical deployment mechanisms that are not pre-configured as standard.
We want to use ChatGPT technology - but not in the cloud and certainly not without data control.
We deploy generative AI securely in your Azure environment. Fully under your control and GDPR-compliant.
Our data is stored in different systems and nobody knows how we can even use it with AI.
We connect your data sources in Azure and make them accessible to generative AI models: securely, in a structured format and ready for use.
We’re testing AI, but it lacks scalability. The models work well in the demo, but not in real-world operations.
We bring your AI projects into productive operation: stable, high-performance and Azure-optimised.
Why you can trust us
Data sovereignty in your own Azure environment
Availability for productive AI applications
Model lock-in due to open architecture
Platform for secure AI solutions
Target group
Large companies and groups
Markets
Germany, Austria, Switzerland
Technological basis
Microsoft Azure, Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Services
Complex data environments, stringent security requirements, a lack of infrastructure? That is exactly what our Azure-based AI platform has been designed for. Our generative AI solution brings artificial intelligence to your organisation securely and scalably. It is based on Microsoft Azure, integrates with existing systems and meets stringent data protection and compliance requirements.
The difference becomes apparent in practice. A prototype using the Azure OpenAI Service can be built in a matter of days – but then the questions arise: Who is authorised to query which documents via the AI? How can we prevent a model from accessing data to which the user making the request does not have authorisation? How can costs be made transparent for each department? Who will operate the service once the project team has moved on? These questions determine whether a pilot project will develop into a productive service.
If you want to make productive use of generative AI whilst retaining full control over your data: Get in touch Please tell us about your deployment using the Azure OpenAI Service.
The key difference between a standalone Azure environment and collana's solution lies in the enterprise maturity and feasibility of generative AI. We enhance Microsoft Azure with security, governance and practical deployment mechanisms that are not preconfigured in the standard system.
Azure with collana
Azure Standard
Data protection, role and permission management, and GDPR compliance are integrated from the outset, without the need for additional set-up work or external tools. In a standard Azure environment, configuration and governance must be set up manually.
Existing systems such as ERP, DMS or CRM are connected directly and made usable for AI models - without lengthy interface development.
The solution is optimised for high-load scenarios and scales to meet your needs: stable, high-performance and Azure-native.
From the architecture right through to go-live, we handle maintenance, updates and technical support for your production AI services.
Fast and secure deployment of large language models and AI agents in Azure, pre-configured for production use within the organisation.
The Azure OpenAI Service does not provide a single AI model, but rather several families of models that differ in terms of task, speed and cost. When designing a solution, the choice of models is often more important than the choice of platform, as it directly determines the quality of the responses and the operating costs.
The largest group consists of language models for dialogue, summarisation, classification and text generation. In other words, the same family of models that underpins ChatGPT. They form the basis for chatbots and knowledge agents. Within this group, there are faster, more cost-effective models for simple tasks and more powerful models for complex queries. In practice, we combine the two: the smaller model handles standard queries, whilst the larger one deals with the more difficult cases.
For tasks that require several steps of reasoning – such as checking sets of rules or evaluating conflicting information – the Azure OpenAI Service provides models with greater reasoning capabilities. Whilst they are slower and more expensive per query, they deliver significantly more reliable results when dealing with complex queries.
In addition to text-only models, there are multimodal models that process images, models for speech recognition and speech synthesis, and image generation via the DALL-E models. For businesses, image and document processing are particularly relevant, for example when extracting data from forms or technical drawings; DALL-E tends to play a greater role in marketing.
Embedding models convert text into vectors and are therefore essential for semantic search within your own documents. When combined with Azure AI Search, this forms the framework that enables a knowledge agent to respond to queries based on corporate data without the need to retrain the models.
We will determine which models are suitable for your specific situation during the architectural design phase. As Microsoft regularly updates the range of options available in Microsoft Foundry, we deliberately keep the selection interchangeable: your application should be able to switch between models without having to be rebuilt.
Use cases
In projects using the Azure OpenAI Service, we frequently encounter four particular patterns. All four are based on the same technical foundation, but differ significantly in terms of the effort required for data integration and governance.
An internal assistant answers questions based on manuals, guidelines or technical documentation. This is the use case with the quickest visible benefit. And it is the one where access rights are most critical, because otherwise the model would bring together information that a user would not be permitted to view individually.
Automated responses to recurring enquiries, with complex cases being handed over to staff. The key here is integration with core systems, so that the chatbot can not only provide information but also complete transactions.
Extracting, classifying and summarising large volumes of documents: for example, in procurement, contract review or technical support. This is where the integration with other Azure services comes into its own, as storage, search and processing all take place within the same environment.
Instead of having its own interface, the AI is integrated into the systems where work is already being carried out: in the ERP, CRM or a specialist application. Acceptance is highest in these cases because nobody has to open an additional tool.
Case Studies
At Bucher Municipal, much of the technical documentation – particularly for older vehicle models – was only available in decentralised locations or in paper form. For the maintenance teams, this meant a great deal of time spent searching for information, long periods of downtime and inefficient processes. The company was looking for a solution that would enable it to access reliable information more quickly on a global scale – in digital form, in multiple languages and on a scalable basis.
In collaboration with Option 4.0, a member of the collana Group, we developed a chatbot app that uses Azure OpenAI and Azure AI Search to access technical documents, process them and provide clear answers to maintenance-related queries. The backend is based on Python, the frontend on ReactJS, and the solution is fully integrated into the existing infrastructure. It automatically searches through documentation and provides context-relevant answers, whether in German, Spanish or Chinese.
Maintenance operations are carried out more quickly because the necessary knowledge is readily available. Diagnostic and repair times are reduced, thereby minimising downtime and operating costs. Thanks to Azure’s scalable architecture, the solution is in use worldwide – from Scandinavia to Australia.
„Our collaboration with Option 4.0 is going very smoothly. Our contact is far more than just a technical service provider – he acts as if he were part of our in-house IT team.“ Christian Johansson, CIO, Bucher Municipal.
Implementation
We analyse your current Azure network architecture, including firewalls, VNetworks and landing zones, and identify the best approaches for integrating an LLM.
We design an AI architecture with a range of options: a standard LLM via the Azure OpenAI Service or an open-source model – whichever best suits your requirements.
In an isolated, non-production environment, we demonstrate the technical feasibility of the chosen AI use case for your organisation.
Next comes system integration: the AI solution is connected to your existing systems, such as ERP and databases, and made operational.
We provide you with long-term support: monitoring, updates, optimisations and extensions to your Azure AI infrastructure are all part of our service.
Technology used
Azure OpenAI Service
Azure Machine Learning
Azure Cognitive Services
Azure Active Directory / Entra ID
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FAQs
The Azure OpenAI Service is a Microsoft service that enables organisations to use large language models within their own Azure environment. Unlike with a public API, this utilises the Azure platform’s identity, networking and compliance mechanisms – such as encryption, role-based access control and key management via Azure Key Vault.
ChatGPT is a consumer-facing product, whilst the Azure OpenAI Service is a platform for building your own applications. The key difference for businesses lies in where the data is processed and the level of control you have over access, logging and retention.
A generative AI solution in Microsoft Azure enables companies to operate generative AI models securely and scalably in the Azure cloud. It combines the Azure OpenAI Service with defined architecture, governance and security standards and supports the productive use of AI - from technical integration to operation in the corporate environment.
The solution is aimed at large companies and corporations with high data protection, compliance and system integration requirements. It is particularly suitable for organisations that want to integrate generative AI into existing IT landscapes such as ERP, CRM or DMS systems.
In contrast to a pure Azure deployment, the solution provides a preconfigured architecture for the secure operation of large language models. This includes governance mechanisms, monitoring, cost control and the structured integration of AI into existing business processes.
Yes, data processing takes place within the Azure environment of the respective company, without access from external providers. The architecture fulfils European data protection requirements such as the GDPR and uses security mechanisms such as encryption, role-based access controls and the use of Azure Key Vault.
In that case, the Azure approach is not the right one. For data categories that must not leave the corporate network, we run language models entirely on-premises as offline AI and local LLMs. In many projects, the end result is a combination of both, depending on the data classification.
Talk to us about your AI deployment in Azure. Get a no-obligation consultation now.