AI Automation for Business
We bring AI productively into companies — not as an isolated demo, but as integrated solutions in real business and IT processes. We automate recurring tasks, standardize workflows, and create room for the work that humans do best.
For whom, what problem, what outcome
Companies whose teams lose time daily on manual routines, inconsistent workflows, and media breaks. Information is searched, emails answered repeatedly, tickets sorted manually, documents read, data transferred, and standard tasks done over and over. After our work: measurably less routine work, consistent standards, documented processes, and teams who spend their time on advice, strategy, and customer relationships instead of repetitive small tasks.
Typical use cases
- Analyze emails, classify them, generate prepared response drafts
- Understand, prioritize, and route tickets to the right place
- Process documents — contract clauses, invoices, delivery notes, protocols
- Prepare reports — from heterogeneous data sources, with source links
- Transfer data between systems — CRM ↔ ERP ↔ knowledge base
- Trigger standard processes — from request to approval with sign-off logic
- Prepare marketing content — briefings, drafts, variants, adaptations
- Support IT administration tasks — from monitoring triage to documentation care
How we work
- Process analysis — we sit with your team on-site, identify recurring tasks, measure effort and frequency. Gut feeling becomes an automation inventory.
- Potential assessment — per task we check feasibility, data availability, risk, ROI. We prioritize by benefit-to-effort, not by technological fascination.
- Architecture & integration — we design how AI agents, RAG systems, and workflows interact with your existing systems (email, CRM, ERP, ticketing, knowledge base). Standardization becomes part of the architecture.
- Iterative delivery — every 2–3 weeks a production-ready module, tested, documented, in operation. No waterfall, no big-bang risk.
- Operations & improvement — quality and drift monitoring, audit trail of all decisions, regular reviews with the business team. What does not carry is removed.
Tech stack
Deliverables
- Automation inventory of your processes with effort, frequency, prioritization
- Architecture sketch with integration points into your system landscape
- Implemented AI agents and workflows, modular, with eval suite and audit log
- Operations runbook (what to do on drift, how to scale, how to switch modules off)
- Training of your team in maintenance, extension, and quality control
Customer benefit
- Less manual work — and therefore fewer errors and less delay
- Faster handling of recurring requests, tickets, documents
- Better, more consistent quality, because standards are part of the workflow
- Clear standards and traceability — less dependence on individuals
- Relieved teams that have time for advice, relationships, and growth
Compliance & standards
- EU AI Act: risk-tier assessment per use case, transparency and documentation duties
- GDPR: data classification, locality choices, processor contracts, deletion concepts
- ISO/IEC 42001 as management system for AI governance, where relevant
- Audit trail of all agent decisions incl. model version and prompt hash
- Industry standards (BaFin, BSI C5, KRITIS) where your sector requires them
FAQ
Where should we start if we have no AI in production yet?
With the three most frequent routine tasks that do not interact with customers directly. Classification, triage, data enrichment — these are the areas where AI delivers reliably today and employees feel relief quickly. We start with a 2–3 week discovery that identifies and prioritizes these tasks.
How is this different from a chatbot?
A chatbot reacts to single requests. An AI agent does a concrete task in a workflow — reads, classifies, decides, calls tools, writes data back, escalates to humans when needed. Chatbots live at the user front-end, AI agents live in your business process.
Do we have to put our data in the cloud?
No. For regulated sectors or sensitive data we use on-premise language models (Gemma, Llama, Mistral via vLLM/llama.cpp/Ollama) on your infrastructure or in our GDPR-compliant DACH cloud. Cloud APIs only where data class and DPA permit.
How quickly do we see the first productive result?
Four to six weeks for the first productive use case. We work in iterative sprints and prioritize so that one workflow runs in your environment before we start the next.
What if an AI agent makes a wrong decision?
Three safety nets: (1) human-in-the-loop on every externally-facing decision; (2) eval suite with golden test set as regression gate; (3) audit log and monitoring that make drift visible. A wrong decision gets caught, documented, corrected — and the agent learns.
Check your automation potential
Seven short questions on industry, size, and biggest routines — we respond with a first assessment of feasibility and prioritized entry use cases.
> Start AI Readiness Check