The 13 Key Criteria for Selecting Enterprise AI

To ensure a reliable comparison, we look beyond the usual marketing claims. These 13 standardized criteria reveal the true competitive edge:

1. Experience in productive enterprise deployment: How long has the provider demonstrably been operating language processing solutions in customer environments, and how long has the specific product been available?
2. Corporate Headquarters & Digital Sovereignty (U.S. Cloud Act): Can foreign authorities compel access to sensitive company data?
3. Data Security & Compliance (GDPR & EU Hosting): Is data processed in compliance with the GDPR and guaranteed to be free of model training?
4. Target Audience Focus: Was the platform designed for small and medium-sized businesses, large corporations, or the public sector?
5. On-premises operation: Is it possible to install the system in your own data center or in a private cloud?
6. Model Variety & Flexibility (Model Agnosticism): Can language models (GPT-6, Claude, Mistral, etc.) be switched flexibly?
7. AI Workflows & Automation: Are workflow automations included, or are there hidden additional costs?
8. External customer service solutions: Can the technology be expanded to include highly sophisticated chatbots and phonebots for end customers?
9. Integration of specialized and industry-specific software: Are systems specifically designed for German small and medium-sized businesses supported?
10. Custom Integration as a Service: Does the vendor offer the development of missing interfaces as a service?
11. Partner Network for Rollout & Change Management: Are there partners who can support the organizational rollout?
12. Service Model & Onboarding: Is there personalized support, or is it just self-service without assistance?
13. Pricing Transparency & Cost Control: Is billing straightforward, or does it involve confusing add-on packages?

Market Overview: All 4 Enterprise AI Platforms at a Glance

The following matrix summarizes the 13 criteria for all four platforms, plus the Cowork feature. We explain the details and limitations of each provider below.

The 4 Leading AI Platforms in Detail

Microsoft 365 Copilot: Seamless Office Integration

Microsoft (founded in 1975) has natively integrated Copilot (since 2023) into Word, Excel, Outlook, and Teams. The system directly accesses shared M365 data within the existing permission context.

Limitations: The pricing structure is complex: The basic license is required in addition to the monthly Copilot add-on. Furthermore, the free version of Copilot is not integrated into Word, Excel, and other applications. Customizations via Copilot Studio cost an additional €173.30 per month or more. Furthermore, there is no on-premises option, a dependency on the Microsoft ecosystem, and the scope of the U.S. Cloud Act, which remains controversial under data protection laws. We’ve outlined the available Copilot versions in “Which Microsoft Copilot Do You Actually Mean?” .

ChatGPT Business & Enterprise: The Powerful Pioneer

OpenAI (founded in 2015) remains the technology leader in model intelligence with ChatGPT (since 2022). The platform offers a code sandbox, interactive artifacts, and easy agent creation. The business version is available starting at two licenses (€21 per user).

Limitations: This is a single-vendor solution with no on-premises option. Personal support is only available at the higher enterprise tiers; the entry-level plan is self-service. Additionally, because the company is headquartered in the U.S., the full risk under the Cloud Act applies.

Langdock: The Modern, Model-Agnostic Challenger

The Berlin-based startup Langdock (founded in 2023) offers a privacy-compliant, model-agnostic platform with EU hosting and ISO 27001 certification. The Business license starts at €25.

Limitations: Langdock replaced its U.S. parent company with a German holding company (SE) in September 2026. However, the platform continues to run on Azure and AWS—that is, on infrastructure provided by U.S. corporations; a dedicated data center has been announced for late 2026. On-premises deployment is only available for approximately 5,000 users or more, and AI usage costs in workflows are not included in the license price.

Kauz aiWorkplace: The Sovereign Alternative for SMEs

Kauz.ai GmbH (founded in Düsseldorf in 2016) has been developing language processing solutions for enterprise use since 2016. Launched in 2025, aiWorkplace brings together the leading LLMs under a single interface. The intelligent Kauz Selection automatically selects the appropriate model for each query. Internal tests showed that, while maintaining the same result quality, this approach reduced AI usage costs by up to 90 percent compared to the consistent use of top-tier models.

As a purely German company, Kauz.ai is not subject to the U.S. Cloud Act. Kauz.ai integrates specialized German software (e.g., Kencube, myODAV) and develops custom interfaces as a service. Billing is handled simply through AI points: whether it’s chat, analysis, or workflows, everything is drawn from the existing pool with automatic “overage” protection. This prevents unexpectedly high additional costs from arising due to the automated use of AI services.

Limitations: As a new Workplace product, it offers fewer ready-made standard interfaces than Langdock. There is no direct visual inline integration with Word or Excel.

Deep Dive: The 3 Critical Decisions When Choosing a Provider

Why the Cloud Act Risk Is Crucial to Digital Sovereignty

The U.S. CLOUD Act requires companies subject to U.S. jurisdiction to disclose data, even if it is stored on servers outside the United States. What matters, therefore, is not the server’s location, but rather who controls the provider. This affects U.S.-based providers such as Microsoft and OpenAI.

What if I use a U.S. model through a European platform?

The legitimate question is: Doesn’t anyone who uses GPT or Claude still send their data to a U.S. provider? The difference lies in the conditions under which this happens. In aiWorkplace, even external models are accessed exclusively in European data centers, including models in sovereign data centers operated by European providers. Those who need to prevent any external access can run only self-hosted open-source models in their own data center within aiWorkplace.

Kauz.ai, as a German GmbH with no foreign shareholders, is not itself subject to the provisions of the U.S. CLOUD Act.

On-Premises for SMEs: Theory vs. Practice

On-premises operation is mandatory for sensitive industries. Microsoft and OpenAI do not offer this option for their Workplace products. Langdock only provides this option starting at 5,000 licenses. Kauz.ai, on the other hand, enables on-premises operation—ideal for small and medium-sized businesses—starting with just one user, either on your own hardware or in a private cloud.

The Partner Network: Rollout Security for German Companies

While global competitors rely on unassisted self-service, Kauz.ai offers a tightly knit partner network for the rollout. AI projects always combine the technology with introducing AI to the people who use it.

Best Practice: The IT Services Provider vE&K as a Dual Partner

The strength of this practical approach is evident at the system integrator vE&K: As a dual partner, vE&K uses aiWorkplace itself in its day-to-day operations while also supporting its own clients—as a certified implementation partner—with the technical rollout, training, and change management. This combination of user experience and IT expertise ensures a smooth implementation.

Conclusion: Which AI platform is right for your business?

• Choose Microsoft 365 Copilot if you work entirely within the Microsoft ecosystem, need inline integration with Office, and the U.S. Cloud Act is not critical to your compliance.
• Choose ChatGPT Business if you want to take advantage of the latest model intelligence in a fully self-service environment.
• Choose Langdock if a model-agnostic approach is sufficient, you rely on standard SaaS platforms such as Slack and Salesforce, and on-premises operation is not required.
• Choose Kauz aiWorkplace if you are a medium-sized business or public-sector organization looking for a GDPR-compliant, digitally sovereign, and on-premises-capable all-in-one solution that includes personalized support during implementation and transparent cost management.

Smart AI Solutions for Small and Medium-Sized Businesses

Microsoft Copilot in the Enterprise: Which Product Do You Mean?

When you talk about “Copilot” – which one do you actually mean? This very first question shows why the comparison is harder than it sounds.

Microsoft Copilot is not a single product. The name covers several offerings with different features, data protection terms, licenses and costs. The free consumer Copilot is something different from Microsoft 365 Copilot Chat. And anyone who needs agents, automations or external channels quickly ends up with additional components such as Copilot Studio.

Microsoft Copilot Free or Paid: What Is the Difference?

Four variants are particularly relevant for companies:

• The free consumer Microsoft Copilot for personal Microsoft accounts.
• Microsoft 365 Copilot Chat as a free add-on for eligible Microsoft 365 users.
• Microsoft 365 Copilot Business or Enterprise as a paid solution with deep Office integration.
• Agent Builder and Copilot Studio for custom agents, actions and automations.

A freely available AI chat is therefore not automatically a comprehensive enterprise solution.

Microsoft Copilot vs. Kauz aiWorkplace: Is a Cross-System AI Workplace Right for You?

Microsoft 365 Copilot is primarily the AI assistant for the Microsoft 365 world. Kauz aiWorkplace is designed as a system- and model-independent AI workplace for research, analysis, document work, company knowledge and controlled workflows.

1. Technological Independence Through Multiple Model Providers

Kauz aiWorkplace brings models from different providers together in one shared working environment, including OpenAI, Anthropic, Google and Mistral as well as open-source model families such as DeepSeek, Qwen, GLM and MiniMax. For image generation, Google Gemini “Nano Banana” and the German model FLUX by Black Forest Labs are among the integrated models. The selection is continuously being expanded.

Kauz Selection automatically assigns requests to a suitable model based on defined criteria such as task type, expected quality of results and cost-efficiency.

The decisive point is long-term technological independence: companies do not permanently commit to a single model provider or a single platform logic.

2. Freedom of Choice in Data Storage and Operation

Kauz aiWorkplace can be operated as a managed service in EU data centers of European providers or, if required, on-premise. Company data is not used to train external models. Role-based access and logging support controlled use within the company.

As a German company, Kauz.ai is committed to European data security and digital sovereignty. This includes European infrastructure, defined data space boundaries and the option to use suitable European and open models.

3. Company Knowledge Across System Boundaries – But Not Unchecked

Microsoft Copilot is closely connected to Microsoft Graph, SharePoint, OneDrive, Teams and Office. In practice, however, relevant expert knowledge is often spread across different systems.

In addition to Microsoft sources, Kauz aiWorkplace can integrate Confluence, Notion, Google services and specialized business applications such as Kencube and myODAV via interfaces. The goal is central access to the sources that are actually relevant for the respective business process.

4. The Business Process in One Platform Instead of Additional Components

With Power Automate, Copilot Studio, agent flows and connectors, Microsoft offers various tools for automation. A complete business process may therefore involve several products, licensing models and administration areas.

In Kauz aiWorkplace, custom agents and no-code workflows are included directly in the same working environment in which employees research, analyze, create documents and use company knowledge.

Conclusion: The Product Name Is Not What Matters

With Copilot Chat, Microsoft 365 Copilot, Agent Builder and Copilot Studio, Microsoft Copilot covers different tasks. Companies that already use Microsoft 365 therefore do not automatically have all the AI functions, knowledge sources and processes they need.

So the key question should not be: “Do we have Copilot?” It should be:

“Can we use the chosen solution to run a specific business process reliably, in a controlled manner and at predictable total costs?”

This involves more than writing a text or summarizing a meeting. What matters is whether the relevant sources are accessible and properly maintained, whether permissions are correct, whether results can be verified and whether approvals, filing or follow-up actions are possible afterwards. Equally important is how dependent the company wants to be on a single provider, model and platform architecture in the long term.

That is why you should compare not just assistants, but a complete real-world process. Only a practical test shows which solution delivers better results, requires less rework, integrates the necessary systems and offers more control in the long term.

Would you like to check this for your use case? Choose a real process together with us. We will then compare quality, sources, rework, integration, operation and costs under real-world conditions.

Sources and Further Information

• ARD Sounds: “Was stimmt nicht mit dem Microsoft Copilot?” (German)
• Microsoft FY26 Q4: Earnings Release and Webcast
• Microsoft Community Hub: more than 450 million commercial Microsoft 365 seats in FY26 Q2
• Microsoft 365 Copilot: Plans and Pricing in Germany
• Microsoft Copilot Studio: Pricing and Credits
• Microsoft Learn: Enterprise Data Protection in Microsoft Copilot and Copilot Chat
• Microsoft Learn: Data, Privacy and Security for Microsoft Copilot
• Microsoft Learn: Privacy and Protections in Copilot Chat
• Microsoft Learn: Get SharePoint Ready for Copilot and Agents
• Microsoft Learn: Optimize SharePoint Content for Employee Self-Service Agents
• Microsoft Learn: Agent Flows in Copilot Studio
• Microsoft Learn: Power Automate License Types
• Kauz.ai: Kauz Selection – the right AI model for every task

Pricing and product information as of August 27, 2026. Microsoft pages, promotional prices and feature sets are subject to change.

Smart AI Solutions for Small and Medium-Sized Businesses

The market is becoming polarized and creating a gap

The budget standard segment

At one end of the spectrum are streamlined, cost-effective solutions designed for a quick, largely self-sufficient start. They answer calls, provide business hours, answer simple questions, schedule appointments, or take messages. They are practical and efficient for clearly standardized use cases and small organizations. Their limitations become apparent when issues become more complex, require deeper knowledge, involve more sophisticated integrations, or entail heightened data protection and operational requirements.

The Enterprise Segment

At the other end of the spectrum are comprehensive contact center platforms designed for large, integrated service environments with high call volumes, sophisticatedrouting and agent processes, and dedicated automation teams. For large corporations, these are often the right choice. For many medium-sized and institutional organizations, however, they are too comprehensive, too process-heavy, and require too much integration.

The gap between them

Between these two extremes lies a large portion of the SME sector: organizations with many recurring but substantively complex phone inquiries that want to professionally design a genuine service channel — without resorting to a purely off-the-shelf solution or a large-scale contact center project. For this group, the usual decision-making frameworks are only of limited use.

Why the price per minute is the least reliable criterion

The price per minute of conversation is the most easily comparable metric on the market, which is why it dominates the discussion. That is precisely where the problem lies: comparability is not the same as informative value. The price per minute obscures the factors that determine actual service quality: the design of the conversations, the quality and verifiability of the stored knowledge, integration with specialized systems, the data protection architecture, and day-to-day operations.

In addition, the per-minute price listed is rarely the total price in practice. Depending on the offering, costs for telephony, language models, data storage, support, compliance options, and development services may be added. A sound decision is therefore not made by comparing individual per-minute rates, but by considering the overall performance over the system’s lifecycle. The relevant question, then, is not “How cheap is a minute of talk time?” but rather “How do we design a phone service that fits our organization and can be operated responsibly?”

How an AI phone assistant is technically structured

To evaluate this, it helps to take a look at the architecture underlying most systems. At its core, it consists of two levels and a tension between them.

The Telephone and Voice Layer

It converts spoken language into text in real time (speech-to-text), generates speech from the responses (text-to-speech), and controls the flow of the conversation, including events and interruptions. Good voice quality and low latency — that is , the time between the end of a statement and the start of the response — are crucial for a natural listening experience.

The AI and Knowledge Level

It determines what the assistant does in terms of content: how it categorizes a request, what knowledge base it uses to respond, when it asks for clarification, and when it hands the request off to a human. This level is configured, tested, and refined over time.

The Conflict of Objectives Between Latency and Depth

These two levels are linked in a practical way that is often underestimated. A very large language model with a very broad dataset can handle more in terms of content, but tends to respond more slowly—which, unlike in chat, is immediately perceived as an uncomfortable pause on the phone. In many telephone use cases, therefore, a deliberately smaller model with a focused, well-maintained dataset is the better choice. Call quality is not achieved through maximum model size, but through the proper balance of model, knowledge, and response time.

A natural voice is a prerequisite

Synthetic voices have now advanced to the point where callers often can hardly tell they’re machine-generated. This is a significant step forward, but it’s a basic requirement—not a quality feature in and of itself. An assistant that sounds natural but miscategorizes inquiries raises expectations it cannot meet. The fact that technical naturalness alone does not build trust is also evident in the numbers: In the 2024 Customer Service Barometer, only 43 percent rate the responses from chatbots and digital assistants as trustworthy. True performance, therefore, is demonstrated through behavior: whether the assistant correctly understands the inquiry, provides reliable rather than merely plausible-sounding responses, asks meaningful follow-up questions when something is unclear, and recognizes when it’s necessary to hand the matter over to a human.

What You Need to Know for Chat and Phone Support

In many organizations, website FAQs, chatbots, and phone hotlines are managed separately — by different teams with varying levels of expertise. This creates extra work and, as callers can tell, inconsistent information across channels. The real added value of a platform-based solution therefore lies less in the fact that a system can handle phone calls, and more in the fact that phone and chat draw from the same knowledge base: knowledge is maintained once rather than multiple times, insights from one channel improve the other, and answers remain consistent. A shared knowledge base does not, however, mean identical conversation styles — phone calls follow their own rules for greetings, follow-up questions, confirmations, interruptions, and a style suited to the spoken word, and are specifically designed for the spoken medium.

Integration and Handover to People

Service calls rarely end with just providing information. It is often necessary to retrieve data, create a ticket, schedule an appointment, or continue a process in a specialized application. It is therefore crucial how flexibly a system can be integrated with existing systems such as CRM, ticketing, or specialized applications — including more specialized systems that fall outside the standard framework of common contact center integrations. Equally important is the structured handoff to staff members: A good telephone assistant recognizes their limitations and transfers the call rather than continuing a conversation that goes beyond their expertise.

Privacy by Design

Information requiring special protection is regularly discussed over the phone. For regulated service providers, public organizations, and applications in the healthcare sector, data protection is therefore not just a general selling point but a specific selection criterion — one that cannot be added after the fact. It must be clarified early on where calls and data are processed, what data flows arise, and whether the AI and knowledge infrastructure can be operated in an EU-hosted, private, or on-premises environment when requirements are more stringent. These questions belong at the beginning of a project, not in the final phase.

A phone bot is a project, not a switch

These criteria lead to a practical conclusion: A reliable phone assistant capable of handling complex inquiries cannot be created simply by having a well-worded prompt and an active phone number. Among other things, call objectives, typical pitfalls, escalation and transfer procedures, data access, and tone of voice must be designed — and all of this must be tested before regular operation begins and continuously refined thereafter. This calls for an approach that combines the platform with ongoing support and views day-to-day operations as a distinct quality service. Availability, reducing the workload on employees, consistent service across all channels, and controlled refinement are the key metrics by which value is measured.

How to make this happen: the Phonebot from Kauz.ai

This is exactly where the Kauz.ai Phonebot comes in. It is designed for discerning small and medium-sized businesses and institutional organizations — that is, for the space between affordable standard phone assistants and enterprise call center suites, where the criteria described above take center stage.

The starting point is deliberately not an isolated telephony product, but rather the shared AI assistant platform: Phone bots are created, configured, and further developed in aiStudio and share the same knowledge base as chatbots. This allows the phone bot to answer specific queries just like the website chat, while its conversational style is specifically tailored for the voice channel. For quality control, the platform incorporates tools proven in chatbot operations — knowledge management, transparency regarding the source of responses, tests and test reports prior to changes, insight into real conversations, and, for particularly sensitive topics, expert-reviewed answers drawn from topic catalogs.

At the telephony level, Kauz.ai relies on an EU-hosted voice and telephony infrastructure; the AI and knowledge layer can be integrated with EU hosting or, for more demanding requirements, with private cloud or on-premises options . Existing systems—such as those for data retrieval, ticketing, CRM, or specialized applications—can be integrated via flexible programming interfaces, as can the handoff to employees. The platform is complemented by support for design, setup, testing, and further development—depending on the project, either as managed operations or under shared responsibility with internal teams and partners.

Phonebots for Your Customer Experience

For small and medium-sized businesses, AI-powered phone assistance is no longer a question of “if,” but of “how.” This “how” determines far more than just a single metric. Whether a phone assistant adds value depends on the interplay of several factors: the ability to control call behavior; a shared knowledge base for chat and phone interactions that allows for independent call handling; the ability to integrate into existing systems; a data protection architecture designed from the outset; and operations designed to deliver consistent quality performance.

Anyone who uses these points as a benchmark shifts their perspective: away from a tool that you activate, toward a service channel that you design. For organizations whose issues are complex and whose reputation hinges on every single call, this is the real opportunity. They can tailor the phone — often their most direct and most frequently used line of communication with customers and citizens — so that accessibility, reliability, and consistency are not left to chance. This is precisely the difference between a number answered by an AI voice and a telephone service that fits the organization.

Smart AI Solutions for Small and Medium-Sized Businesses

1. Technology-agnostic AI platform

A technology-agnostic AI platform is characterized by several features. Support for and flexible use of various LLMs form the foundation for a future-proof AI strategy. This is because not only is the performance of these models evolving rapidly—their optimal areas of application are also constantly changing. A model that is the best choice for a specific task today may be replaced tomorrow by a more powerful or cost-effective alternative.

Open APIs and standardized interfaces are equally crucial. They enable seamless integration into existing enterprise systems and lay the foundation for an interoperable AI landscape. New technologies and standards—such as the Model Context Protocol (MCP) for the standardized connection of AI agents to enterprise systems—can thus be easily integrated. This keeps the platform open to future innovations, reduces proprietary dependencies, and protects existing investments.

2. Data Sovereignty Through Flexible Operating Models

Companies vary significantly in terms of their data protection, compliance, and IT infrastructure requirements. While a traditional cloud environment may be sufficient for some use cases, the location and operator of the cloud infrastructure also play a decisive role in choosing the operating model. Cloud offerings from major international providers such as Microsoft Azure, AWS, or Google Cloud may be suitable for many scenarios, while regulated industries in particular are increasingly turning to European cloud providers such as Open Telekom Cloud, Hetzner, or OVHcloud to meet stricter requirements for data sovereignty and compliance. For particularly sensitive use cases—such as in the financial or healthcare sectors—a private cloud or on-premises solution may be required, in which confidential information is processed within the organization’s own control boundaries. A sovereign AI platform should therefore support various hosting options and give companies the freedom to choose the operating model that best suits their needs—and to flexibly adapt or switch it as needed.

3. AI must not be a black box

Who uses AI in the company? What is the knowledge base behind the results? What are the actual costs of using AI? Where can it be improved? And can AI results be reviewed and, if necessary, adjusted manually?

As AI usage increases, it becomes difficult to maintain an overview of content and organizational structure. Different AI tools, decentralized licenses, and unclear responsibilities make it difficult to use AI securely and cost-effectively. At the same time, the risk of “shadow AI” increases when employees resort to unauthorized applications, with all the associated data protection implications. A in-house CompanyGPT as a centralized, approved work environment is therefore one of the most effective measures for curbing unauthorized AI use and preventing data leaks.

A central AI platform also provides transparency and control: It consolidates various AI applications into a unified working environment, manages access through role-based and permission-based models, and simplifies license management. At Kauz.ai, this transparency extends beyond day-to-day operations: AI chatbots can also be specifically controlled in terms of content and continuously optimized. Dialogues are documented and can be reviewed and improved as part of human-in-the-loop and feedback processes. This ensures that not only the usage but also the quality of the AI applications remains traceable at all times.

We combine governance, cost control, and quality management into a single interface, making AI transparent and manageable for IT, business units, and management alike. This transforms AI into a professionally operated enterprise application.

4. Maximum Cost Control

The costs of professional AI deployment are difficult to calculate: token prices fluctuate constantly, and usage varies depending on the task, frequency of use, and language model. Systematic cost monitoring thus becomes a crucial management tool for assessing which use cases are worthwhile and which employees actually need which licenses.

To this end, Kauz.ai offers a cost dashboard that transparently displays the consumption of AI points—a unit that makes the costs of different models comparable—by time period and use case. In addition, KauzSelection optimizes costs by automatically assigning each request to the appropriate LLM: Expensive top-tier models are used only where they are truly needed.

The licensing model is also designed to be flexible: Instead of rigid flat rates per user—which often force unnecessary upgrades—the pricing packages include a shared AI point allowance. If additional points are needed, they can be purchased on an ad hoc basis. This makes AI costs predictable, and companies pay only for what they actually use.

5. Think About Compliance from the Very Beginning

With the EU AI Act, the GDPR, and regulations such as DORA, the regulatory framework for AI use is becoming increasingly concrete. In particular, the traceability of AI decisions is evolving from an option to a requirement: The AI Act mandates comprehensive logging for certain AI systems to ensure traceability and human oversight.

A future-proof AI platform should not address compliance only after the fact, but should incorporate it from the ground up. That’s why our architecture is designed from the outset to meet the regulatory requirements of the EU AI Act and the GDPR—from the hosting infrastructure and data processing to the contractual framework. A finely granular role- and permission-based system ensures that only authorized individuals can review and approve AI decisions: a key component of the required human oversight.

6. Independent of external specialists

Data sovereignty has both a technical and a human dimension. Those who rely on external developers or consultants for every adjustment to their AI applications are simply trading dependence on the technology provider for dependence on the service provider. No-code platforms solve this problem by empowering business departments to independently create, maintain, and scale AI chatbots, AI agents, and AI workflows—without any IT support.

aiWorkplace and aiStudio, designed for developing AI chatbots, are consistently tailored for business users: content management, control of response behavior, and dialogue analysis are handled through a user-friendly content management system. Pre-built templates and over 500 actions in the aiWorkflow Builder also enable a quick start with multi-step AI process chains. This ensures that knowledge about the company’s own AI applications remains within the organization—and with it, control.

Data sovereignty is a matter of choice

Data sovereignty is achieved through specific architectural and selection decisions: a technology-agnostic platform with flexible model selection, open standards such as MCP, selectable operating models—including on-premises options—full transparency regarding usage, quality, and costs, integrated compliance, and the empowerment of internal business units. Companies that consistently apply these criteria when selecting a platform future-proof their AI investments—regardless of how the market and regulations evolve. The best time to set the course is now.

AI Impulses for AI-First Companies

The Right Model for Every Task

The Kauz Selection is an intelligent routing system within aiWorkplace that automatically selects the optimal model for each request. Instead of requiring users to manually choose between GPT, Claude, Gemini, or other models, Kauz-Selection analyzes the requirements of a task and decides within milliseconds which model offers the best combination of quality, speed, and cost.

Users no longer have to worry about selecting a model themselves. They simply enter their query as usual, and the appropriate model is selected automatically in the background.

For example, a translation or a short email can be processed by a cost-effective model, while complex contract analyses or demanding research tasks are automatically routed to more powerful reasoning models.