Technical depth
Architecture, data flows and quality criteria are clarified before implementation begins.
Strategy · Development · Integration
Custom AI software development for businesses.
We design and build custom AI solutions, integrate them into existing software, develop AI agents and RAG systems, and automate business processes – with pragmatic engineering, clear controls and measurable objectives.
Custom software instead of a generic off-the-shelf tool
Integration with existing systems
Privacy and security considered from the start
From discovery to production deployment and operations
Typical project starting points
Where do you want to start?
The right entry point depends on your current situation. The most common routes are improving an existing process, building a new digital product or adding AI to existing software.
Automate processes
Handle recurring work more efficiently
Combine deterministic workflows with AI where text, documents or variable decisions need to be interpreted.
Learn moreBuild a custom AI solution
Develop an AI application around a clear hypothesis
Build new software using real data and explicit quality criteria rather than a superficial proof-of-concept demo.
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Integrate AI into existing systems
Add the right AI capabilities to existing software
Extend applications, portals and business software without replacing systems that already work well.
Learn moreWhat we build
AI software for real business use cases.
We do not build AI demos for their own sake. We build systems with a clearly defined role in a real product or business process.
AI software development overviewAI Agents
Agentic workflows and intelligent assistants for clearly defined tasks and business processes.
Learn moreRAG Systems
Make company knowledge in documents and data sources usable with language models in a controlled, traceable way.
Learn moreLLM Integration
Integrate GPT, Claude, Gemini or open-source models into software and business processes with the right architecture.
Learn moreAI Process Automation
Automate recurring work intelligently without losing control of the wider business process.
Learn moreAI Web Applications
Custom portals, SaaS products and business applications with AI capabilities that support a real product workflow.
Learn moreAI MVP Development
Validate new ideas quickly and turn the result into a sound technical foundation for the next stage.
Learn moreOur process
Four steps from problem to production.
1
Discovery
We clarify objectives, users, business processes, data sources and technical constraints.
2
Concept & architecture
We define integrations, data flows, security boundaries, quality criteria and sensible limits for AI use.
3
Development & evaluation
We build iteratively, integrate real systems and test AI capabilities with representative cases.
4
Production deployment & operations
We plan deployment, monitoring, cost controls, failure paths and further product development.
Two perspectives – one goal
Software with AI. And software developed with AI.
We build software in which AI can be a core product capability, and we also use AI selectively as an engineering tool.
Architecture, quality control, privacy, security decisions and professional responsibility remain with the development team. AI supports the work; it does not replace accountability for the outcome.
How we use AI in software engineering
Engineering over hype
Not every software problem needs AI.
If a deterministic rule, full-text search or traditional workflow is more reliable, that is often the right solution. AI is useful where variable language, unstructured information or flexible decision-making creates genuine value.
Questions we clarify before implementation
- What concrete problem should be solved?
- Which data and interfaces are available?
- How will quality be measured by the business?
- Which failures need technical or human safeguards?
- How will the solution be operated and evolved?
Technologies
Technology selected for the project.
We choose models, frameworks and infrastructure based on requirements, not trend cycles or provider branding.
OpenAIClaudeGeminiRAGAPIsAngularAstroDjangoPythonPostgreSQLVector SearchDockerKubernetesCloud & On-Premise
Insights for better decisions
Understand the technology before fixing the architecture.
Our technical articles cover recurring questions around RAG, agents, model selection and project cost. They complement the service pages with decision criteria and practical context.
View all insights
Insights
What is RAG?
How Retrieval-Augmented Generation connects company knowledge to language models, and when the approach is useful.
Read article
Insights
AI agents or traditional workflows?
Where agents are useful, where deterministic automation is the better fit and why hybrid systems are often the strongest choice.
Read article
Insights
OpenAI, Claude or open source?
Which criteria matter more than generic model rankings when selecting an LLM for a real software product.
Read articleWhy KI-Softwareentwicklung.de
Technical substance instead of an isolated AI feature.
We treat AI capabilities as part of a complete software system, including data, interfaces, security, quality and operations.
More about our engineering approachIntegration by design
New capabilities are planned around existing systems, APIs and responsibilities wherever that is the better solution.
Controlled quality
Testing, permissions, validation and human approval are matched to the risk of the use case.
Operations from day one
Monitoring, cost controls, failure paths and future model changes are part of the system design.
Frequently asked questions
Short, practical answers.
The key question is not which model currently receives the most attention, but which approach is robust for the actual use case.
What does custom AI software development cost?
Cost depends mainly on product scope, integrations, data quality, quality requirements and the operating model. A short discovery phase is often the most sensible way to define scope and produce a more reliable estimate for the next stage.
When does a RAG system make sense?
RAG is particularly useful when answers need to be based on your own documents or knowledge sources and those sources need to remain current, traceable or permission-aware.
Can AI be integrated into existing software?
Yes. Extending an existing system is often more sensible than rebuilding it. The key factors are available APIs, data access, security requirements and the existing architecture.
How do you handle company data and security?
Privacy and information security are assessed for each project. This includes data flows, providers, storage locations, processing agreements, permissions, logging and the question of which data a model should be allowed to access at all.
How long does implementation take?
A focused prototype can be delivered much faster than a production business application. Timelines are driven mainly by integrations, data preparation, quality assurance and business approvals.
Let’s turn your AI idea into a concrete software project.
In an initial conversation, we clarify the objective, current situation and sensible next steps.
To get started, your objective, existing systems and the main open question are enough.