AI Process Automation for SMEs
Large language models, vision AI and workflow automation integrated into real business processes — not as a sandbox, but as a productive solution. We connect GPT, Claude and custom models with your CRM, ERP and DMS. First productive process in 4–8 weeks, GDPR-compliant, on-premise optional.
Why AI Gets Stuck in Mid-Sized Companies
Everyone tried ChatGPT. But integration into real processes is where it stops — and that's why a large share of AI initiatives in DACH SMEs never reach production.
AI Stays an Isolated Sandbox
Employees use ChatGPT in a browser tab, but the output never lands automatically in CRM, ERP or DMS. Everyone copies manually.
Data Protection Blocks Adoption
Personal data cannot move to US cloud AI. Without a secure GDPR-compliant solution, AI use moves into shadow IT.
Endless Pilot Phases
Three months of workshops, five-figure consulting, ending with a PDF — but no running process. Many AI projects stop after the pilot.
No System Integration
Even great AI models deliver nothing if they're not connected to SAP, DATEV, HubSpot or your line-of-business software. APIs are missing, nobody builds the bridge.
Hallucinations & Compliance Risk
Without guardrails, validation and audit trail, you risk wrong data in accounting, contracts or customer communication. That gets expensive in audits.
AI Capabilities We Embed Into Processes
Not AI for its own sake — AI where it delivers ROI. From document extraction to autonomous agents, embedded in your running systems.
GPT & Claude Integration
LLMs like GPT-4, Claude or local models (Llama, Mistral) wired into your processes via API — with prompt templating, versioning and audit log.
Vision AI for Documents & Products
Computer vision reads invoices, delivery notes, contracts, product defects or damage photos. Structured data lands automatically in your ERP.
Workflow Automation with AI Steps
n8n, Make or custom orchestration — AI is a step in the workflow, not the whole process. Human-in-the-loop where needed, autonomous where possible.
Custom Enterprise AI / RAG Bot
A chatbot that knows your contracts, wikis, tickets and product data — RAG architecture, source citations, no blind hallucinations.
AI Agents for Complex Tasks
Agents handle multi-step tasks: lead research, proposal drafts, reporting aggregation — with clear limits and escalation rules.
GDPR-Compliant AI Operations
EU hosting, on-premise or private-cloud options. Data processing agreements, audit trail, data minimization — AI deployment EU-AI-Act-ready.
The ROI of AI Process Automation
Typical levers from productive AI projects with DACH mid-sized companies — no demo slides. We determine the concrete figures for your processes in the AI audit.
How Our Clients Put AI Into Processes
AI Proposal Drafts — Many Hours Saved Per Quote
Problem
Illustrative scenario: A wholesaler creates complex quotes manually. Each quote takes several hours for research, calculation and writing — at a high quote volume, quickly a full-time equivalent.
Solution
A custom AI solution accesses the product catalog, prices, contract history and customer communication. Generates a complete quote draft in minutes, the inside sales team reviews and refines.
AI Contract Analysis — Risk Check in Seconds Instead of Hours
Problem
Illustrative scenario: For every new client, several existing insurance contracts have to be reviewed manually — coverage scope, gaps, optimization potential. Each review takes one to two hours, often incomplete under time pressure.
Solution
Vision AI extracts structured data from the PDF contracts. An LLM agent compares against a benchmark database and creates a risk briefing for the broker — including concrete optimization suggestions.
Custom Service Bot — Ticket Volume Noticeably Reduced
Problem
Illustrative scenario: Service inside sales handles a high daily ticket volume on spare parts, maintenance intervals and technical specs — usually from old manuals, internal wikis and outdated Excel sheets.
Solution
A RAG bot knows every manual, spare-parts catalog and service protocol. Answers a large share of standard inquiries directly with source citation, escalates complex cases.
Frequently Asked Questions About AI Process Automation
Answers to the most common questions about deploying AI productively in business processes.
What is AI process automation — and how does it differ from classic RPA?
Classic Robotic Process Automation (RPA) works rule-based: it follows predefined steps and fails the moment a form or layout changes. AI process automation adds language and vision models that understand unstructured content (emails, PDFs, images, voice) and make decisions. They complement each other: RPA for stable click paths, AI for anything requiring understanding — classification, extraction, drafting, summarization.
Which AI models do you use — and how is that GDPR-compliant?
We pick the right model per use case: GPT-4, Claude and Gemini via EU endpoints or Azure-OpenAI for compute-heavy tasks; local models like Llama 3 or Mistral when data must not leave your premises. For GDPR compliance we have data processing agreements with all vendors, document data flows and can run fully on-premise or in a German private cloud for sensitive data. EU AI Act classification happens before project start.
How do you prevent the AI from hallucinating or writing wrong data into systems?
Three mechanisms: (1) RAG architecture — the AI only answers based on your documented sources, not from training gut feeling. (2) Validation layer: for critical data (invoice amounts, contract text) a second rule- or model-based check verifies plausibility. (3) Human-in-the-loop for anything customer-facing — the AI drafts, a human approves. Every AI action is logged with prompt, output and source (audit trail).
How fast does a first process go live?
A first productive use case (e.g. AI classification of incoming emails, automated proposal drafts, contract extraction) is typically live in 4–8 weeks. We start with discovery and selection of the highest ROI lever (1–2 weeks), build proof of value (2–3 weeks) and iterate into production. Unlike classic mega-projects: you see working software after 4 weeks, not concept papers.
What does AI process automation cost?
The scope ranges from entry projects (a single defined process such as invoice extraction or proposal drafting) to more comprehensive solutions with a custom enterprise AI, multiple connected systems and continuous improvement — plus ongoing operations (LLM tokens, hosting). You always receive an individual quote from us. In a free AI audit we show concrete savings potential in your company — with ROI calculation based on your real volumes.
Ready to Put AI Productively Into Your Processes?
In a free AI audit we identify the process with the highest ROI leverage and show how to put it into production in 4–8 weeks.
Book Free AI Audit