Does WZ-IT build AI applications or only infrastructure?

Both. We add AI features to portals, dashboards, internal tools and existing software - and can also operate the matching infrastructure, from LiteLLM and Langfuse to local GPU servers.

When does RAG make sense?

RAG makes sense when answers should be grounded in your own documents, databases or APIs. Source attribution, permission checks, data updates and answer evaluation are essential.

Do you build AI agents?

Yes, but with control. An agent only receives the tools and permissions it actually needs. Critical actions can include review steps, approvals or audit logs.

Does AI have to run locally?

Not always. Local AI makes sense for sensitive data, stable workloads, compliance requirements or cost control. For other use cases, a hybrid setup with local infrastructure and selected APIs may be better.

How do you prevent AI features from becoming unmaintainable?

We treat AI as production software: versioning, tests, observability, rollback, access control and an operations concept are included from the start.

Integrate AI features, agents and RAG into production software

We do not build AI demos. We build operable software: with data access, permissions, guardrails, monitoring and the right local or European infrastructure.

RAG · Answers from own data
Agents · Tool use with boundaries
LLMOps · Tracing, monitoring, operations

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Positioning

Why WZ-IT for AI software?

Many AI projects fail not because of the model, but because of integration, data access, permissions, quality assurance and operations. That is where our software and infrastructure experience meet.

Internal assistant for support, sales or operations
Document and knowledge search with sources
AI feature inside an existing customer portal
Workflow automation via APIs and n8n
Legacy modernization with AI-assisted analysis

Services

What we build

AI becomes valuable when it is embedded into existing processes, portals and data flows. We build the application layer and operate the stack behind it.

Assist

Internal AI assistants
Assistants for support, sales, operations or engineering - connected to your documents, APIs and permission models.
LLM integration into existing software
Add AI features directly into portals, dashboards, admin UIs and business apps instead of adding another separate tool.

Knowledge

RAG & Knowledge Systems
Documents, databases, wikis and business systems become searchable LLM context - with sources, access control and update processes.

Act

AI agents & workflows
Agents that can use tools: create tickets, check data, prepare drafts or trigger processes via APIs - with clear boundaries.

Secure

Evaluation & quality assurance
Prompt versions, test sets, tracing and review flows so answers improve measurably and errors stay visible.
Guardrails & security
Tenant isolation, roles, PII reduction, rate limits and safe tool use - especially important for agents and internal data.

AI accelerates development and processes. Our job is to turn that into maintainable, secure and operable software - not just prompts that look good in a demo.

Process

From use case to operations

We deliberately separate experiment, productization and operations. This keeps AI fast enough for prototypes, but stable enough for real users.

01
Clarify use case & risk
Which task should AI really take over, which data does it need, and what must it never do?
02
Prototype with real data
No slideware demo stack: we test early with realistic documents, APIs and user roles.
03
Productization
Auth, permissions, logging, tests, tracing, UX and deployment become the actual software.
04
Operations & evolution
Monitoring, updates, cost control and model changes become part of ongoing operations.

Build + Operate

AI software does not end at the first prompt

Production AI needs evaluation, logging, cost control, updates, permissions and incident response. That is why we design AI applications with operations, monitoring and security from the start.

RAG sprint

Make one priority knowledge source securely usable

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Langfuse

Tracing and evaluation

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LiteLLM

LLM gateway and cost control

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Qdrant

Vector DB for RAG

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FAQ about AI software development

Answers about RAG, agents, local models and production operations.

Knowledge

Software projects with reliable outcomes

Proof for modernization, API extension, architecture, deployment and ongoing operations.

WZ-IT moved our studio infrastructure from decentralised individual devices to a central platform: every site is securely connected via VPN, new devices are onboarded automatically and an entire site is provisioned from a template, without manual steps on location. What impressed me most is the breadth and depth of their knowledge: Timo and Robin are not a typical IT provider who sets up a server and leaves. The two of them think their way into highly complex infrastructure and software topics, work through every requirement we put in front of them, and build networking, provisioning and operations so that everything fits together in the end. WZ-IT is an excellent partner for complex software, network and architecture projects.

Steve KirchnerManaging Director, nextGYM GmbH

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International

Built in Germany's Ruhr Valley. Running worldwide.

WZ-IT designs, develops and operates infrastructure and software for clients in Germany and internationally. We deliver projects remotely and continue supporting them in ongoing operations after go-live.