The most beneficial things in terms of AuraDB are its speed, its good pricing, the multi-cloud availability, and its availability across GCP, Azure, and Amazon. It's great for use cases where you want to do relationship-centric modeling. So, those are the most valuable things in AuraDB.
I also work with real-time data in the AuraDB solution. A lot of this, especially the scalability and how efficient these conversations are, depends on what model or writing strategy you go for. But you can definitely work with real-time data.
For my personal projects, I use AI. What we're seeing right now can work very well with RAGs in AuraDB or any graph database. So we take extra data, put it in a graph database—AuraDB in this case—and feed it to an existing large language or a small language model. With that, an AI model can gain some extra understanding of your data, which is stored in a graph database.
It can give out very contextual and specific answers based on the extra data users provide in the form of a graph database, which is stored in AuraDB. So the use cases are, from what I mean, the terminology is graph RAG, but that's where I see a lot of potential use cases for a lot of data.
The outcome accuracy with the AI-enhanced graph is good for my use cases. However, it may be difficult to assign a numerical accuracy metric to Neo4j. But for example, with text summarization, you cannot put a number to the accuracy. However, just seeing the answers and the improvements in the model, it's definitely helpful in improving the results. It's essentially giving an extra context to your model. So, I definitely see the advantages of using AuraDB.