AI Integration
AI and LLM features reliably integrated into your web applications and business systems. In production, not as a demo.
What it is — and what it includes.
AI integration means embedding existing AI technologies and models into production software — at Batunet, into web applications, portals, platforms and the business systems behind them — from connecting large language models (LLMs) through retrieval-augmented generation to AI-assisted workflows. We do not develop our own models; we integrate existing ones so that they work on real data, remain traceable and have clear boundaries of responsibility.
Scope of services
- LLM and RAG integration into existing systems
- Connection to your data (vector and conventional stores)
- Evaluation, boundaries and fallbacks
- Cost control and operations
- Data protection and traceability
From the first conversation to a running system.
Every project follows the same four steps. You see and review interim results along the way, not only at the end.
- 01
Understand the goals and requirements
The first conversation, held with our management, covers your project, your processes and the systems already in place. We then write down goals and requirements and tell you openly whether custom development makes sense.
- 02
Plan the solution and architecture
We propose a technical solution, define the scope and the order of implementation, and document the key decisions together with their reasoning.
- 03
Build and review together
We develop in stages, with tests and code reviews. We present interim results regularly so you can review them early and adjust priorities.
- 04
Launch and keep supporting
After launch we take on maintenance, updates, monitoring and further development to the agreed extent. The project-specific source code and the documentation are handed over to you.
What you have in hand at the end.
AI features that hold up in production
Traceable results instead of a black box
Predictable operating costs
Technologies we use
Questions about AI Integration
RAG or fine-tuning?
Usually RAG. Retrieval-augmented generation keeps knowledge current and traceable without retraining a model. We use fine-tuning selectively for style and format, not for factual knowledge.
How do we keep costs under control?
Through deliberate model selection, caching, limiting context and measuring per request. Cost is an architectural concern, not a matter of chance.
Related topics.
Guides, decisions, services and case studies connected to this page.
Technologies
Services
Perspectives
Let’s talk about your project.
Tell us briefly about your project. Your first conversation is directly with our management.
