Schauersberger DE

Your own AI for your business.

A central AI platform with your documents, your access permissions, and controllable data flows. No tool chaos, no licence confusion.

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In short

A private AI platform makes sense for your business when multiple employees need to work with internal documents, clearly defined access permissions, and controllable data flows. Individual licences for ChatGPT or Copilot add up quickly, yet they know nothing about your company's knowledge or role logic. At the same time, employees are already using private AI tools — often without the IT department's knowledge. An internal AI platform closes this gap: centralised control, your own document integration, flexible hosting models, and a plannable data-protection framework.

Last updated: March 2026

Why do businesses need a private AI platform?

A private AI for businesses solves three problems simultaneously: escalating licence costs, uncontrolled data leakage, and the absence of any connection to internal knowledge. Providing ten employees with ChatGPT Plus today costs around $230 per month according to the current OpenAI pricing, without the AI knowing a single internal document. With 50 employees, that amounts to over $13,000 per year — for a tool that neither draws on company knowledge nor respects access permissions.

In parallel, an invisible problem is growing: shadow AI. According to a 2025 Bitkom survey, 8 per cent of German companies report widespread use of private AI tools for professional tasks, with a further 17 per cent reporting isolated cases. Another 17 per cent assume it is happening, but cannot say for certain. Sensitive customer data, contract details, and strategic documents end up on servers over which the organisation has no control.

An internal AI platform puts an end to this chaos. It gives all employees access to powerful language models, connects them to the company’s knowledge base, and uses roles and permissions to control who may access which information.

What exactly is an internal AI platform?

An internal AI platform is a self-operated or dedicated-hosted application through which employees interact with language models. It can access internal data sources, and its usage is centrally managed. At its core it consists of four layers: language models (operated either via API or locally), document integration (the AI responds on the basis of company knowledge, not just general training data), user management with roles and permissions, and configurable knowledge spaces for different departments.

The decisive difference from SaaS tools such as ChatGPT or Microsoft Copilot: the organisation decides which models are used, where data is processed, and which employees may access which content. Depending on the deployment model, the platform can run on a European cloud server, on the company’s own hardware on-premises, or combine both as a hybrid solution.

For companies in the DACH region working with sensitive industry or customer data, this level of control is not a luxury — it is an operational necessity. The EU AI Act (in force since August 2024, with phased applicability through to August 2026) raises the requirements for transparency and documentation when deploying AI systems. A private platform makes it easier to meet these requirements in a traceable manner than a patchwork of individual tools.

When does a private AI make more sense than ChatGPT Enterprise or Copilot?

The decision between an in-house AI platform and standard SaaS solutions such as ChatGPT Business, Microsoft Copilot, or Google Gemini is not a question of “better or worse”, but of your starting position and requirements.

Cost logic

SaaS providers charge per user per month, regardless of actual usage. ChatGPT Business is priced at $25 per user/month according to OpenAI, and Microsoft 365 Copilot at €18.20 to €21.84 per user/month according to Microsoft. With a private platform there is no per-user licence. Whether this works out cheaper depends on the number of users and intensity of use.

Data control

SaaS solutions process data in the provider’s cloud. A private platform offers a choice: European cloud, your own servers, or a hybrid model. This does not replace sound data-protection governance, but it does give you more room to manoeuvre.

Model flexibility

SaaS solutions lock you in to a single provider. A private platform can run different models in parallel, select the most appropriate one for each task, and switch when needed.

Your own knowledge

Standard SaaS tools have no knowledge of your internal documents, or only very limited access. A private platform can connect to any data source: manuals, contracts, knowledge bases, CRM exports, ticketing systems.

Rule of thumb: If your team mainly handles general writing tasks and is already deeply embedded in Microsoft 365 or Google Workspace, a SaaS tool may be the faster route to getting started. Once multiple employees regularly work with the same internal knowledge sources, access permissions become relevant, and the cost logic of individual licences no longer adds up, a private platform becomes a serious alternative.

What are the building blocks of an in-house AI?

An internal AI platform for employees consists of six core components, combined according to the company’s needs:

Language models

Commercial models via API (from OpenAI, Anthropic, or Google, for example) or open-source models run locally. The choice depends on data-protection requirements, performance needs, and budget.

Document integration

The AI is connected to internal documents. With each query, relevant passages are automatically retrieved and passed to the language model as context.

Knowledge spaces

Document collections organised by topic: HR knowledge, technical documentation, sales materials. Each space has its own access rules.

Roles and permissions

Integration with existing user directories (Microsoft Entra ID, Google Workspace, or other directory services). No separate account management required.

Web search

For queries that go beyond internal knowledge: privacy-friendly web search, either self-hosted or via commercial search services.

Workflow automation

Embed AI capabilities into existing processes: e-mail summarisation, ticket classification, quote drafting, or report generation.

Practical examples: How businesses use their own AI

The following projects were delivered by Schauersberger Software.

Marketing agency

Dental expertise as an AI knowledge base

A dental practice marketing agency works with two knowledge bases: its own agency knowledge (processes, templates, client history) and a dental specialist archive. Employees use this to create more technically grounded blog posts and practice websites, without having to consult a specialist for every technical term.

Recruitment firm

Automated CV handling in the application process

Incoming CVs are automatically parsed, structured, and matched against open positions. Recruiters receive a summary for each application including a matching score and relevant qualifications. The manual pre-screening that previously consumed the majority of processing time is largely eliminated.

Same platform, completely different knowledge spaces, workflows, and roles. That is precisely the advantage of a bespoke solution over rigid SaaS tools.

What does a private AI platform for businesses cost?

The cost structure of an internal AI platform differs fundamentally from SaaS subscriptions. There is no per-user licence charged monthly. Instead, the investment breaks down into three blocks:

1. One-time setup project

Configuring the platform, connecting it to existing IT infrastructure, building knowledge spaces, and setting up document integration. This includes architecture consultancy, technical implementation, role configuration, and an introductory training session for the team.

2. Optional ongoing support

Updates, monitoring, troubleshooting, and platform extensions. For companies without internal IT capacity for ongoing operations.

3. Ongoing third-party costs (external)

Hosting (cloud servers from approx. €50–200/month), API token costs for commercial models (typically €50–500/month depending on usage), optionally search API, OCR services, or GPU hardware for local model operation.

Whether a private platform is more cost-effective than distributed individual licences depends primarily on the number of users, intensity of use, document requirements, and the level of data control desired. Particularly when multiple employees are involved and recurring internal knowledge processes are a key part of the workflow, it can be significantly more attractive. For low-volume use or purely general writing tasks, a standard SaaS tool may be the simpler path. An architecture review will clarify upfront which model suits your situation.

How the rollout of an internal AI platform works

Introducing a private AI for your business is not a months-long IT project — it is a structured process in four phases. At Schauersberger Software, it looks like this:

Phase 1

Architecture and viability review (1–2 hours)

A joint assessment: which processes should be supported? Which data sources exist? What IT infrastructure is already in place? The outcome is a clear recommendation on whether and how a private AI platform makes sense.

Phase 2

Setup and configuration (1–3 weeks)

Deploy the platform, connect models, configure user management, populate initial knowledge spaces with documents, integrate web search.

Phase 3

Pilot operation with the core team (2–4 weeks)

A small team works productively with the platform. Feedback is gathered, configurations are fine-tuned, and additional documents are integrated.

Phase 4

Rollout and training

The platform is made available to all intended employees. A hands-on training session covers not only how to use the platform, but also the most effective usage patterns.

The entire process from the initial assessment to productive deployment typically takes 4 to 8 weeks.

Frequently asked questions

Do I need my own servers to run an internal AI platform?
No. The platform can run on European cloud servers (Hetzner, IONOS, Azure with EU data residency). Your own hardware is only necessary if you want to run local language models with no external connections whatsoever.
Can my employees use the AI platform without technical knowledge?
Yes. The interface works like a familiar chat application. Employees ask questions, upload documents, or select a knowledge space. An introductory training session of around two hours is sufficient for a productive start.
What types of documents can the AI process?
PDFs, Word files, Excel spreadsheets, Markdown, HTML, and plain text files are supported natively. Scanned documents and images containing text can also be integrated via OCR. Databases and APIs can likewise be connected as knowledge sources.
Can a private AI platform be operated in a GDPR-compliant way?
A private platform gives you more flexibility than standard SaaS tools: you choose the hosting location, control the data flows, and determine which information goes to which models. Whether this is GDPR-compliant in a specific case depends on the overall architecture — data processing agreements, technical and organisational measures, purpose limitation, and the permissions concept all need to be in order.
How quickly can the platform be up and running?
From the initial assessment to productive use, the typical timeline is 4 to 8 weeks. A first functional prototype with basic integration can often be ready within a week.
What happens when AI models continue to evolve?
The platform is model-agnostic. When a new, more capable model becomes available — whether from OpenAI, Anthropic, Google, Meta, or a European provider such as Mistral — it can be integrated without switching platforms.
How does this differ from a pure AI consultancy?
At Schauersberger Software, there are no PowerPoint presentations without implementation. The consultation leads directly to a working system: architecture, setup, configuration, and training are all delivered from a single source, by someone with over 15 years of software development experience.
What company sizes are suited to a private AI platform?
The topic becomes particularly relevant as soon as multiple employees regularly work with the same internal knowledge sources and individual licences, data silos, or uncontrolled tool usage become a problem. In practice, this most commonly affects SMEs with 10 to 200 employees.

Next step: Architecture and viability review

In 15 minutes we will clarify together whether a private AI platform makes sense for your business, which architecture suits your requirements, and what costs you can expect. No obligation, no sales pitch.

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