What does an AI integration actually do for your company?
An AI integration pays off when a language model takes over one concrete, recurring task in your business — not when “something with AI” sits on a strategy slide. Bauer IT Solutions builds exactly these integrations: software that extracts data from incoming invoices, answers questions from your internal documentation, or triages support requests before a human opens them.
I am Jan Bauer, a software engineer in Rodgau near Frankfurt, Germany — and deliberately not an AI consultant. You talk directly to the engineer who writes your code: no project-management layer, no agency margin, no anonymous offshore team. The outcome is not a presentation but a running system on staging, tested with your real data before production.
The models themselves — GPT, Claude, Mistral or open models on your own hardware — are interchangeable building blocks. The value lies in the integration: the connection to your data sources, the fit with your workflows, legally sound operation. That combination — custom software development from Germany, GDPR-native architecture — is the gap this service fills.
Which AI use cases actually work for mid-sized companies in 2026?
Four categories of AI use cases work reliably for mid-sized companies in 2026: document extraction, knowledge-base questions via RAG, email and ticket triage, and text drafting with company context. All four are narrowly scoped, measurable, and require no rebuild of your IT. The examples below are typical scenarios from project inquiries — not client references.
How does automated document extraction and classification work?
In document extraction, a language model reads unstructured documents — invoices, delivery notes, contracts, applications — and returns structured data for your systems. A typical scenario: supplier invoices arrive as PDFs, the integration extracts invoice number, amounts and payment terms and creates the accounting record — minutes of retyping become a quick check. Fields the model is unsure about are flagged for manual review instead of silently accepted.
How does a RAG system answer questions from your own knowledge base?
A RAG system answers questions from your employees or customers based on your own documents rather than the model’s generic training knowledge. Your team asks in plain language — “What is the notice period in the maintenance contract with supplier X?” — and gets the answer with a reference to the source document. It works for contract archives, technical manuals and internal policies. The source citation is decisive: it makes every answer verifiable.
How does email and ticket triage work?
Email and ticket triage means the model classifies incoming messages by topic, urgency and responsibility before a human opens them. In a support inbox where orders, complaints, invoice questions and spam arrive mixed together, the integration routes each message to the right team, extracts key data such as customer numbers, and drafts replies for standard cases — a human still decides, but no longer from zero. I connect this to your existing ticket system or mailbox; no migration required.
When are AI text drafts with company context worth it?
AI text drafts with company context are worth it wherever your team repeatedly writes similar texts drawing on company data — quote cover letters, product descriptions, customer-service replies. The difference from ChatGPT in a browser tab is context: the integration knows your product data, pricing logic and tone of voice. It also ends the uncontrolled pasting of internal data into personal AI accounts — everyday reality in many companies — by bringing that work into a controlled, GDPR-compliant setup.
Many of these use cases are, at their core, internal business tools with a language model as one component.
How is AI implementation different from AI consulting?
AI consulting delivers recommendations; AI implementation delivers working software — that is the whole difference. If your use case is already clear, implementation can start directly.
| Criterion | AI consulting | AI implementation at Bauer IT Solutions |
|---|---|---|
| Deliverable | Slide deck, strategy, roadmap | Working software on staging and in production |
| Time horizon | Weeks to months until a recommendation — the build starts afterwards | First testable version in weeks, weekly builds on staging |
| Success measurement | Hard to measure; often ends with the final presentation | Measured on the process: documents processed, correct routings, hours saved |
| Follow-up costs | Implementation commissioned separately, often from a third party | API and hosting costs, itemized in the quote; the code is yours |
| Point of contact | A consultant who hands the build on | The engineer himself, from first call to handover |
Concept work does not disappear — it lives in the written specification you receive before development starts, the binding basis for the fixed-price quote.
How does your AI integration stay GDPR-compliant?
GDPR-compliant AI is an architecture decision, not a checklist applied afterwards. Bauer IT Solutions builds on four principles from day one.
First, EU data processing. The major model providers now offer EU endpoints, and the rest of the stack — database, backend, document storage — is hosted in Frankfurt. Your documents stay in the EU end to end; nothing has to flow to US infrastructure.
Second, clean data processing agreements. Every service touching personal data needs one; the specification lists every provider involved, so your data protection officer can audit the chain before any code is written.
Third, no training on your data. Business API terms of the relevant providers rule out that your inputs train their models — unlike some free consumer services — and I only use access tiers with this contractual assurance.
Fourth, local models as an option. For particularly sensitive data — health or HR records — open models can run entirely on your own infrastructure or a dedicated Frankfurt server, so no document ever leaves your environment. They are often somewhat weaker than the large API models — a trade-off I assess honestly in the first call.
Add data minimization: prompts contain only the fields the task requires, and personal identifiers are pseudonymized before the model call where possible.
What does the EU AI Act mean for your project?
For most business integrations, the EU AI Act means transparency and documentation duties — not a ban, not an elaborate certification. Fully applicable from 2 August 2026, it classifies AI systems by risk: prohibited practices (social scoring) and high-risk systems (AI in hiring or credit decisions) face strict requirements, while the integrations on this page count as minimal-risk.
What still applies: people interacting with a chatbot must be able to tell they are talking to an AI, and externally published AI-generated content may carry labeling duties. Obligations for the foundation models themselves sit with the providers, not with you.
The risk classification of your project is part of the written specification, including the duties that follow — and if a project genuinely heads toward high-risk territory, automated applicant pre-screening, say, I tell you before it starts. Panic is the wrong advisor on the AI Act; so is ignoring it.
How do I integrate an LLM into your existing software?
An LLM integration into existing software works as a separate service next to your systems, not inside them — that is the most important architectural principle. Bauer IT Solutions builds an API layer: a thin service between your systems and the model. Your ERP, CRM or ticket system calls a defined interface (“extract this document”, “answer this question”) and gets structured data back. The model behind it stays invisible to your systems — so when a better or cheaper model ships next year, it is swapped in the API layer without touching your software.
For knowledge-base applications, RAG system development adds four steps: your documents are split into meaningful sections; each section gets an embedding — a mathematical representation of its meaning — stored in PostgreSQL with the pgvector extension, proven standard technology; on each question the system retrieves the most relevant sections; and the model formulates the answer from them with a source citation. New documents are queryable immediately, no training takes place, and access rights are enforced per user.
My stack is deliberately unspectacular: TypeScript and Node.js or Python, React, PostgreSQL. Everything runs on staging first, where you test with real documents and real questions — whether an AI integration is good is decided on your data, not on my demo.
How does the collaboration work?
The collaboration runs in five fixed steps, from a free initial consultation to a documented handover — fully remote if you are not in Germany, in English or German as you prefer. I reply to inquiries within 24 hours.
- Free initial consultation. We clarify the task, the data, and whether a language model is even the right tool — if a simple script is cheaper, I will tell you.
- Written specification with a fixed price. You receive a document covering use case, data sources, model choice, GDPR setup, AI Act classification and measurable acceptance criteria — for AI projects typically a test set of real examples — plus a no-obligation fixed-price quote.
- Development with weekly builds on staging. You test early with real documents and real questions — with AI, quality only shows on real material, so you get a clickable build every week, not a surprise at the end.
- Acceptance. The system is checked against the criteria from the specification — for the test set: how many documents were extracted correctly, how many correctly flagged for review?
- Documented handover. Source code, prompts, architecture and operations documentation belong to you. You know where which data flows, what operation costs, and how a model swap works.
Why the specification comes before the first line of code is described under How I work.
What does an AI integration cost — and what are the ongoing costs?
The cost of an AI integration has two parts: one-off development and ongoing model and hosting costs. Development depends on scope — a document extractor is a small project, a RAG system spanning several knowledge sources a mid-sized one. After the free initial consultation you receive a fixed-price quote, so the budget is settled before the project starts; running costs for typical volumes are modest and estimated in the quote. What each project size means, and which factors drive the price, is broken down on the pricing page.
How do you start your AI project?
The best start for an AI project is one concrete, small use case — not a grand AI strategy. Look for the single process that costs your team time every day, where people currently retype, sort or hunt for information locked in text. That is where an integration pays for itself first.
If you have such a process in mind, describe it via the contact page. You will get a reply within 24 hours from the engineer who will build the system, and after a free initial consultation a no-obligation fixed-price quote. If you want to hire a German software developer who ships working AI instead of slide decks, that is exactly what I do — no roadmap workshops, a system that runs.