AI Integration & Automation

Secure AI
inside the business process

Local LLMs, retrieval-augmented generation and model governance – embedded in existing business processes and controllable architectures.

Approach

AI as part of your architecture,
not a black box

We integrate AI where it delivers measurable business value: in business applications, back-office processes and decision support. Models run locally or via controlled APIs – never as an unobserved black box.

RAG architectures connect your internal knowledge sources with language models without data ever leaving your controlled environment.

Discuss a use case

Scope of delivery

  • Use-case analysis with business-value evaluation
  • Local LLMs on your infrastructure or controlled API connection
  • RAG pipelines with vector index, retrieval and evaluation
  • Integration into business and process applications via API and workflow
  • Model governance: registry, evaluation, monitoring, audit trails
Building blocks

Four building blocks for productive AI

Each building block is independently production-ready and auditable.

Local LLMs

Open-source models on your infrastructure. No external data flows, full control over model and processing.

RAG

Retrieval-augmented generation on internal knowledge sources – traceable, citable and without data leakage.

Workflow Automation

AI-assisted process automation with clear hand-overs between human, system and model.

Model Governance

Model registry, versioning, evaluation and monitoring – with full audit trail down to the response.

Approach

From use case to production

01 — Use Case

Business case, data availability and risk assessed in a structured way.

02 — Pilot

Lean pilot in a controlled environment with clear acceptance criteria.

03 — Production

Hand-over to operations with model governance, monitoring and SLA.

Define your first productive AI workload

We typically start with a concrete use case and a clearly scoped pilot that goes live within a few weeks.

Discuss your use case