Introduction – Flood of Data vs. Knowledge Gaps
Did you know that medium-sized companies generate immense amounts of unstructured text data per year? This hides crucial facts – but classic business intelligence approaches often only exploit a fraction of the potential of this data.
In this article, we want to provide decision-makers with a clear roadmap for combining Retrieval-Augmented Generation (RAG) and our DeepResearch loop to enable in-depth analysis based solely on 100% internal knowledge – without web search and without data drain.
1 | What does RAG do?
RAG extends large language models with a targeted search step. Before the model responds, it gets text passages from your own sources – project reports, mails or tickets – and embeds them as context. The result is fact-based answers, whose precision can be significantly increased compared to “pure” LLM answers according to studies.
Note: RAG brings the facts into the model, not the model to the facts.
2 | What complements DeepResearch?
RAG answers a question solidly – but remains with a one-time research. However, complex business questions require iterative deepening.
RAG therefore extends our DeepResearch loop with these steps and saved 70% analysis time per use case in the field test Ø:
- Defining the question – e.g. “Which cost and risk drivers can we find in all projects over the last five years?”
- First RAG search – Automatic extraction of relevant passages.
- Synthesis & Reasoning – Pattern recognition (e.g. Ø cost deviation +10%).
- Deepening issues – focused follow-up questions (“Which projects were +20%? Why?”).
- Targeted Search (RAG) – only where context is missing, is searched again.
- Sufficiency check Ends only when ≥ 90% source coverage is reached.
- Final Report Structured analysis with metrics, causes, recommendations for action
3 | The integrated analysis process at a glance
Question: “Which types of projects cause the highest additional costs?”
| Phase | Purpose | Results |
|---|---|---|
| 1 Question | Sharpening business objectives | Clear hypothesis |
| 2 Retrieve | Finding relevant internal documents | Context snippets |
| 3 Generates | Derive trends and patterns | Preliminary insights |
| 4 loop | Closing knowledge gaps | Extended data set |
| 5 Check | Sufficiency and plausibility testing | Validated basis |
| 6 Report | Structuring results | Decision-Mature output |
4 | Practical example: 100 internal customer projects (2019-2024)
Question: “Which types of projects cause the highest additional costs?”
| Project type | Ø Actual/target deviation | Main drivers |
|---|---|---|
| Hardware | +12 % | Late delivery (37 %), change (*) |
| Software | +7 % | Scope Creep (42%), bug fixes |
| Consulting services | +3 % | Additional workshops, travel expenses |
*Change = short-term change of specifications.
Recommendation for action:
- Early risk monitoring in hardware projects (supply chain KPIs, buffer times).
- Change Control Board for software projects to limit scope-creep.
5 | Business Benefits at a Glance
| Benefit factor | Measurement variable | Result (pilot) |
|---|---|---|
| Depth instead of surface | Identified root cases | 5x more than manual |
| Transparent sources | Audit Trail Completeness | 100 % |
| Data protection and compliance | External data flows | 0 bytes |
| Efficiency | Analysis throughput time | -70 % |
6 | Conclusion
- RAG provides reliable facts from your data base.
- DeepResearch transforms these facts through iterative deepening into action-relevant insights.
- 100 % internal sources ensure data sovereignty, compliance and trust.
Find out more: How we implement RAG systems from idea to operation can be read on our services page AI solutions: RAG systems & AI agents.
Would you like to implement this in your company? We support you pragmatically – from the idea to the operation.