RAG system development
Retrieval-Augmented Generation connects a language model to your own knowledge sources. Relevant content is retrieved at runtime and supplied as context, rather than retraining a model on every company document.
Typical starting points
Scope
Use cases
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.
Decision criteria
We first assess whether AI genuinely adds value compared with conventional software, search or workflow automation for the specific task.
Quality and control
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.
How we work
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.
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.
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.
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
Direct answers to points that should be clarified before making a technical decision.
RAG first retrieves relevant information from defined sources and then provides that material to a language model as context. This lets answers draw on current, company-specific knowledge.
No. Vector search is often useful, but it is not the only retrieval method. Depending on the data, full-text search, filters, hybrid retrieval or structured queries may be more important.
No. RAG can improve grounding, but it does not guarantee correct answers. Retrieval, prompting, source grounding and answer quality must be evaluated separately.
Yes. PDFs, manuals, wikis, databases and other knowledge sources can be connected when ingestion, updates, metadata and permissions are defined properly.
Yes. Retrieved passages and metadata can be retained and shown with answers. Access controls should be enforced before retrieval so users only receive content they are authorised to see.
Related services
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.
