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 real value emerges 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 production-grade AI integrations that respect data residency, deliver measurable automation, and operate reliably over time.

What I Deliver

Agentic Workflow Automation

LLM-driven automations that replace brittle scripts and manual review steps. Agent designs that know when to act, when to ask, and when to escalate. Integration with existing ticketing, approval, and orchestration systems.

RAG & Knowledge-Base Systems

Retrieval-augmented generation pipelines grounded in your internal documentation, runbooks, and ticket history. Answers that cite sources. Ingestion pipelines that keep the knowledge base fresh.

Self-Hosted & EU-Region LLM Deployment

LLM infrastructure on your own hardware (Ollama, vLLM) or in EU-compliant cloud regions. No data leaves your boundary. Fit for banking, healthcare, and regulated sectors where sending prompts to US-based APIs is not an option.

Prompt Engineering & Agent Design

Prompts treated as versioned artifacts, not strings buried in code. Systematic evaluation frameworks so model upgrades don’t silently regress behavior. Agent architectures using the Model Context Protocol (MCP) for clean tool boundaries.

Integration With Existing DevOps Pipelines

AI-assisted code review, automated incident summarization, runbook generation from logs. LLMs invoked from CI/CD, Ansible playbooks, and existing Python tooling — not as a separate stack, but as one more capability alongside the automation you already have.

Typical Use Cases

• 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

I design AI integrations with GDPR and enterprise data-handling requirements as a starting point, not an afterthought. 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.”