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AI Software Development

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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Leading companies worldwide trust WZ-IT

  • Rekorder
  • Keymate
  • Führerscheinmacher
  • SolidProof
  • ARGE
  • Boese VA
  • NextGym
  • Maho Management
  • Golem.de
  • Millenium
  • Paritel
  • Yonju
  • EVADXB
  • Mr. Clipart
  • Aphy
  • Negosh
  • ABCO Water

AI projects need software and operations maturity

Proof for production deployments, architecture decisions and ongoing operations around modern software stacks.

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.

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.

Typical starting points

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.

Internal AI assistants

Assistants for support, sales, operations or engineering - connected to your documents, APIs and permission models.

RAG & Knowledge Systems

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

AI agents & workflows

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

LLM integration into existing software

Add AI features directly into portals, dashboards, admin UIs and business apps instead of adding another separate tool.

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.

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.

1

Clarify use case & risk

Which task should AI really take over, which data does it need, and what must it never do?

2

Prototype with real data

No slideware demo stack: we test early with realistic documents, APIs and user roles.

3

Productization

Auth, permissions, logging, tests, tracing, UX and deployment become the actual software.

4

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.

FAQ about AI software development

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

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.

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.

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.

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.

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

AI projects need software and operations maturity

Proof for production deployments, architecture decisions and ongoing operations around modern software stacks.

  • Odiseo Solutions
  • Golem.de
  • ARGE

What do our customers say?

Let's Talk About Your Idea

Whether a specific IT challenge or just an idea - we look forward to the exchange. In a brief conversation, we'll evaluate together if and how your project fits with WZ-IT.

E-Mail
[email protected]

Leading companies trust WZ-IT

  • Rekorder
  • Keymate
  • Führerscheinmacher
  • SolidProof
  • ARGE
  • Boese VA
  • NextGym
  • Maho Management
  • Golem.de
  • Millenium
  • Paritel
  • Yonju
  • EVADXB
  • Mr. Clipart
  • Aphy
  • Negosh
  • ABCO Water
Timo Wevelsiep & Robin Zins - CEOs of WZ-IT

Timo Wevelsiep & Robin Zins

Managing Directors of WZ-IT

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