Typical starting points
When this service is a good fit
- Analyse and structure text or documents
- Use natural language as an interface
- Add AI capabilities to existing software
- Keep multiple model providers available where justified
LLM integration services
Integrate language models into existing applications cleanly and reliably.
We integrate large language models where they create concrete product or process value. Model choice, privacy, cost, answer quality, resilience and future provider flexibility are considered together.
Strategic guidance before implementation
Custom solution instead of a standard tool
Secure integration into your system landscape
Operation and continuous development considered from the start
Scope
What we deliver
- Provider and model evaluation with real test cases
- API, fallback and abstraction architecture
- Prompt, tool and output design
- Monitoring, cost controls and quality checks
Use cases
Where LLM Integration can create concrete value.
The right architecture depends on the process, data, existing systems and the cost of a wrong decision. These examples are common use cases, not blanket recommendations.
01
Text classification and extraction
02
Summaries and structured outputs
03
Assistant features in business software
04
Tool calling and API orchestration
05
Document processing
06
Multi-provider and fallback architectures
Decision criteria
When the approach fits – and when it does not.
We first assess whether AI genuinely adds value compared with conventional software, search or workflow automation for the specific task.
Questions we clarify early
- How well does the model perform on the actual test set?
- Which data may be sent to which provider?
- What latency and cost levels are acceptable?
- How important is provider portability?
Quality and control
AI capabilities need measurable quality criteria.
Non-deterministic outputs should not be judged from a handful of impressive demos. We define representative test cases, observe failure patterns and design fallbacks or human approvals where the business context requires them.
- Evaluate on your own data rather than marketing benchmarks
- Schema-validated structured outputs
- Timeouts, retries and fallbacks
- Cost and quality monitoring per use case
How we work
From business problem to robust solution.
We do not start with a model name. We start with the process, users, available data and technical constraints. From there, we design an architecture that balances business value, privacy, cost, maintainability and operations.
Analysis and target architecture
At the start, we clarify the current situation, users, data sources, integrations and quality requirements. If core assumptions are still open, we shape an initial phase specifically to test those assumptions.
Development and integration
The software is developed iteratively against real data and interfaces. Critical integrations and AI capabilities are tested early so technical risks become visible before the end of the project.
Operation and continuous improvement
For production systems, monitoring, cost control, logging, failure paths and model or provider changes are part of the architecture. An AI project does not end with an impressive demo.
Frequently asked questions
Key questions about LLM Integration.
Direct answers to points that should be clarified before making a technical decision.
Which LLM is best?
There is no credible answer without a specific use case. What matters is performance on your own test cases, privacy, latency, tool use, cost and operating requirements.
Can we switch providers later?
Yes, if the integration is decoupled appropriately. Models are rarely fully interchangeable, however, because prompting, tool calling and output behaviour differ.
Are open-source models automatically more privacy-friendly?
No. Self-hosting can provide greater control over data flows, but it also creates responsibilities for infrastructure, access controls, updates and monitoring.
Can OpenAI, Claude or Gemini be integrated into existing software via API?
Yes. Language models can be connected server-side to business applications, portals or web apps. Authentication, structured outputs, error handling, cost controls and privacy need to be designed deliberately.
When does a multi-LLM or fallback architecture make sense?
When availability, cost or different model strengths materially matter. The abstraction should only be as complex as the use case requires.
Related services
Relevant building blocks for your project.
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.