AI Automation Consultant Frankfurt · LLM & Agent Integration Services

Why AI Automation Now?

Large Language Models have moved from novelty to production. Most organizations have experimented with ChatGPT or Copilot, but the payoff comes when LLMs are integrated into actual business workflows: behind firewalls, on your own data, with audit trails that compliance teams can live with.

I help teams move past the chatbot-demo stage toward AI integrations that respect data residency, deliver measurable automation, and keep working over time.

What Production Integration Involves

Agentic workflow automation replaces brittle scripts and manual review steps with LLM-driven automations, designed so the agent knows when to act, when to ask, and when to escalate, and wired into the ticketing, approval, and orchestration systems already in use.

Retrieval-augmented generation grounds all of this in your internal documentation, runbooks, and ticket history. Answers cite their sources, and ingestion pipelines keep the knowledge base fresh.

Where the data demands it, the models run on your own hardware through Ollama or vLLM, or in EU-compliant cloud regions. No data leaves your boundary, which is what makes this workable for banking, healthcare, and other regulated sectors where sending prompts to US-based APIs isn’t an option.

Prompts are treated as versioned artifacts, not strings buried in code, with systematic evaluation frameworks so a model upgrade doesn’t silently regress behavior, and agent architectures built on the Model Context Protocol for clean tool boundaries.

All of it sits alongside the automation you already have: AI-assisted code review, automated incident summarization, runbook generation from logs, with LLMs invoked from CI/CD, from Ansible playbooks, and from existing Python tooling rather than living in a stack of their own.

Common Starting Points

• Internal knowledge assistants grounded in company documentation
• Automated ticket triage and routing
• Incident summarization and post-mortem drafting
• Code review assistants on GitLab or GitHub
• Document extraction and structured-data generation from unstructured sources
• Runbook execution copilots with human-in-the-loop approval

Compliance & Data Residency

GDPR and enterprise data-handling requirements shape these designs from the beginning. That means self-hosted deployments when the data demands it, clear audit logs for every model invocation, and explicit opt-outs for any telemetry. For regulated sectors, this is often the difference between “we evaluated LLMs” and “we actually shipped them.”