AI Integration
Integrating language models into your existing applications – not for their own sake, but for clearly defined tasks: making your own documents searchable, pre-sorting requests, generating text. For multi-step processes, several AI instances can be combined into a multi-agent system – with clearly defined approval points.
Get in touchScope of services
- Connecting language models to existing applications (REST, function calling)
- Retrieval-augmented generation: making your own documents and data usable
- Agentic integration: the model acts on its own (emails, code execution, bookings)
- Capturing recurring procedures as skills: approach, scripts and templates in one place
- Multi-agent systems in which several AI instances work together
- Vector databases, embeddings and reusable prompt templates
- Embedding AI into existing workflows (customer support, recruiting, accounting)
- Defining human-AI collaboration: who does what, where the approval points are
Our approach
Four steps, one point of contact – from analysis to ongoing operation.
Use case
Define a concrete use case and verifiable success criteria – before talking about models.
Feasibility
Use a prototype to check whether a language model solves the task reliably.
Integration
Connect to the existing application – including data access, prompt templates and behaviour on failure.
Operations
Monitor answer quality, cost and latency in live operation and adjust as needed.
References
AI integration into an existing business application
Added LLM capabilities to a production-grade Java/React application: REST integration with a language model, reusable prompt templates and retrieval-augmented generation with vector search.
Six building blocks of LLM integration in Spring Boot
A publicly available demo repo with six typical building blocks, each as its own channel: REST-based chat, prompt templates, retrieval-augmented generation, integration with a vector database (Qdrant), LangChain4j with session memory, and AI-assisted ticket classification.