AI capabilities that companies can use immediately
Azure AI Services are Microsoft's portfolio of pre-built, ready-to-use AI building blocks on the Azure platform. They provide companies and developers with capabilities such as speech recognition, text analysis, document processing, computer vision, machine translation and generative AI as APIs and SDKs - without having to train their own AI models or build their own AI infrastructure. Companies integrate these AI functions directly into existing applications, processes and systems.
Azure AI Services are now united under the umbrella of Azure AI Foundry - Microsoft's central development platform for AI applications. Azure AI Foundry bundles access to over 1,800 AI models, the development tools for prompts, RAG architectures and AI agents as well as operational functions for governance and monitoring. The Azure OpenAI Service - the centrepiece for generative AI at enterprise level - is an integral part of this platform.
The decisive advantage of Azure AI Services over building AI models in-house is the speed of entry. Companies do not have to employ their own data scientists, build up training data or operate a modelling infrastructure. They call up AI functions via API and integrate them into their applications. What used to be an AI project lasting several months becomes development work that takes weeks with the right services.
Azure AI Services operate AI models in the Microsoft cloud - on request in German or European data centres, fully GDPR-compliant and isolated from the public internet. The Azure OpenAI Service provides GPT models and other large language models without using input data to train Microsoft models. For companies that want to use AI on sensitive business data, this is the structural difference to public AI services.
Azure AI Services are modular: Companies start with individual ready-made APIs for specific tasks and build more complex AI applications on top of them. With Azure AI Foundry, AI agents are created that combine multiple services, access company data, control processes independently and operate in controlled, monitored environments. collana develops AI applications on Azure AI Services - from simple automation solutions to complete agent architectures for enterprise use.
Azure OpenAI Service provides GPT-4, GPT-4o and other OpenAI models in a secure, enterprise-grade Azure environment. Companies use it to build chatbots and knowledge agents based on their own documents, manuals and databases, automated text generation for reports and communication, and RAG architectures that combine LLM responses with up-to-date company data. collana has developed an AI chat app on Azure OpenAI for Bucher Municipal that makes maintenance data available in seconds.
Relevant for: Wholesale & Foreign Trade, Production & Manufacturing, Healthcare, Logistics & Transport, All knowledge-intensive industries
Azure Document Intelligence (formerly Form Recognizer) automatically extracts structured information from documents: invoices, delivery notes, contracts, forms, ID cards and individual document types are analysed, relevant fields are recognised and output as structured data. Manual data entry, error-prone document processing and time-consuming archiving work are replaced by AI-supported automation.
Azure AI Language provides ready-made NLP functions: sentiment analysis, entity recognition, keyword extraction, text summarisation, question-answer systems and conversational language understanding. Companies can use it to automatically analyse customer feedback, classify service requests, extract relevant information from texts and build conversational interfaces for their applications.
Relevant for: Wholesale & Foreign Trade, E-Commerce, Healthcare, Telecommunications & IT, all companies with high communication volumes
Azure AI Vision automatically analyses images and videos: object recognition, text recognition (OCR), quality control in production, face recognition, scene analysis and user-defined image classification. In manufacturing, computer vision recognises defects on production lines. In retail, it analyses shelf stocks. In logistics, it automatically reads labels and licence plates.
Azure Machine Learning is the platform for companies that want to train, test and operate their own AI models. AutoML automates model selection, MLOps pipelines control training, versioning and deployment. Data scientists train predictive analytics models for demand forecasting, predictive maintenance and anomaly detection - directly on the company's own data in the Azure environment.
Azure AI Foundry creates AI agents that perform complex tasks independently: Controlling processes, calling up tools, accessing data and coordinating with other agents. collana develops customised AI agents for corporate use: knowledge agents that search internal documentation, process agents that automate approval workflows and service agents that answer customer enquiries independently. In addition, ILAI, collana's own AI platform for chatbots and voice assistants, is available as a ready-to-use solution.
Relevant for: All sectors, in particular Production & Manufacturing, Healthcare, Wholesale & Foreign Trade, Telecommunications & IT
Not because they have larger IT teams. But because they use AI as a tool, not as an experiment. Those who introduce Azure AI Services do not gain a new technology platform, but concrete automation: Documents that read themselves. Service requests that classify themselves. Knowledge agents that find answers in seconds. The following companies have taken this step with collana and show what is possible.
Many companies know that AI could improve their processes - but the path from idea to productive application seems unclear. Which services fit which use case? How are AI outputs integrated into existing systems? How do you ensure that AI responses are reliable and GDPR-compliant? And how does an initial pilot solution scale to a company-wide application?
collana answers these questions not in theory, but in practice. The AI Discovery Workshop is the structured introduction: specific use cases are identified, evaluated and prioritised in a short space of time. Which process has the greatest potential for automation? Which Azure AI services are suitable? And what does a realistic implementation plan look like that quickly leads to measurable results?
Then comes the implementation: collana develops AI applications on Azure AI Services that are integrated into existing system landscapes - ERP, CRM, document management systems, web platforms. For companies looking for a quick introduction to generative AI, ILAI, collana's own Azure-based AI assistant, is available as a ready-to-use solution that can be customised to company data and processes.
AI projects rarely fail because of the technology. They fail because use cases are defined too vaguely, data is not available or not clean enough, or AI outputs are not integrated into operational processes. What is needed is a partner who combines technical AI expertise with an understanding of business processes. This is precisely the approach that collana takes when supporting companies in the introduction and further development of AI applications on Azure AI Services.
collana is a six-time certified Microsoft Solutions Partner and fulfils the requirements in several areas of expertise: Business Applications, Data & AI, Digital & App Innovation, Infrastructure, Security and Modern Work. These certifications are not an end in themselves. They prove that we not only know the Microsoft platform, but that we have mastered it at enterprise level and are continuously developing it further.
What sets collana apart from a pure Azure implementer is its own product experience with AI. ILAI, collana's AI assistant for chatbots and voice assistants, is built on Azure OpenAI and is in productive use with customers. Local LLM solutions for data protection-critical environments, anonymisation solutions for sensitive data and AI developer training for internal teams round off the portfolio. collana knows from its own experience what really works in practice when building AI applications - and what doesn't.
collana accompanies companies not only up to the first AI prototype. The aim is a long-term partnership: from the AI discovery workshop to the development and integration of the first application through to ongoing operation and continuous development. More than 1,000 customers in Germany, Switzerland and other European markets rely on this collaboration.
Not every company knows immediately which AI use case is the right first step. The AI Discovery Workshop provides clarity in a short space of time: concrete use cases, realistic cost estimates and an implementation plan that works with the available budget and existing IT infrastructure.
Azure AI Services are Microsoft's portfolio of ready-made AI APIs and models: language processing, computer vision, document analysis, machine translation and generative AI via the Azure OpenAI service. Azure AI Foundry is the overarching development platform under which these services are bundled. Foundry also provides tools for the development of AI agents, RAG architectures, model fine-tuning and AI governance. Azure AI Services and Foundry are two levels of the same platform: Services are the AI building blocks, Foundry is the development and operating environment.
Both use the same OpenAI models (GPT-4, GPT-4o etc.), but pursue fundamentally different approaches. ChatGPT is a public service: input can be used for training future models, data leaves the corporate environment. The Azure OpenAI service is designed for enterprise use: Customer data is not used for model training, the environment runs in EU data centres, access rights are controlled via Azure Active Directory and the solution can be completely isolated from the public internet. For companies with GDPR requirements or sensitive business data, Azure OpenAI is the only legally compliant way to use GPT models.
Traditional chatbots follow predefined dialogue trees or respond to individual questions. AI agents on Azure AI Foundry are systems that act independently: they analyse tasks, plan steps, call up tools and APIs, access data sources and carry out multi-stage processes autonomously. A knowledge agent doesn't just search through documents - it analyses, synthesises and provides context-related answers. A process agent doesn't just check requests - it initiates approval workflows, escalates exceptions and documents results.
Yes, under the right configuration conditions. Azure AI Services can be configured so that all data is processed in German or European data centres and does not leave the EU. Microsoft is contractually obliged not to use customer data from the Azure OpenAI service for training Microsoft models. Microsoft Purview can also be used to set up classification policies and DLP rules for AI workflows. collana advises companies on the GDPR-compliant architecture of their Azure AI solutions.
The Azure OpenAI service is billed on a transaction basis: according to the number of tokens processed (input and output). The costs depend on the model used, the volume and the selected deployment type. Provisioned Throughput Units (PTU) with fixed monthly costs are available for plannable high-volume workloads. Many Azure AI services offer free entry-level quotas for testing. A reliable cost estimate is created based on the specific use cases and the expected usage volume - collana provides support in calculating and optimising Azure AI costs.
Yes, Azure AI Services can be integrated via standardised REST APIs - in Dynamics 365, SAP, Salesforce, individual web applications and mobile apps. AI functions thus become part of existing processes: Invoice processing in SAP is automated by Azure Document Intelligence, service requests in Dynamics 365 are classified by NLP, maintenance manuals become searchable by an Azure OpenAI-based knowledge agent. collana develops such integrations for companies in the DACH region.
This depends heavily on the use case. A simple chatbot on Azure OpenAI that responds to a defined document base can be productive in just a few weeks. A more complex agent architecture with several integrated systems, custom models and a company-wide rollout requires correspondingly more time. The AI Discovery Workshop provides a realistic assessment in a short space of time: which use case is technically feasible, what effort is realistic and what results can be expected in what time frame.