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The capability to handle large-scale graphs without needing GPUs is a big plus. It has saved us costs on hardware.
I found that the inductive capabilities sometimes produce inconsistent results, which can be frustrating during analysis.
It simplifies the embedding process; however, the inconsistency in results means I still need to validate them through other means.
I like the cross-dataset compositionality; it allows me to combine embeddings in meaningful ways that enhance my models.
There are occasional bugs that require workarounds, which can be a hindrance during critical times.
It has vastly improved my ability to perform client segmentation, leading to better-targeted marketing campaigns.
I like that it simplifies the embedding process for non-technical users, making it accessible for my team.
The embedding quality varies, and I sometimes find it lacking in certain complex datasets.
It helps create embeddings for product recommendations, but I still need to manually refine the results for accuracy.
I’m impressed by the speed; it processes large graphs in minutes, which is a game-changer for my analytics work.
It sometimes struggles with the inductive capabilities, providing less accurate vectors for newly added entities.
It has made the embedding process much more efficient, but I still need to cross-check results with other systems.
The automatic scaling feature is fantastic, allowing me to focus on my data rather than hardware concerns.
The lack of extensive tutorials or user guides makes it challenging to fully leverage its capabilities.
It helps in creating embeddings for churn prediction, but I sometimes struggle with the accuracy of those predictions.
I love how easy it is to get started with Cleora.AI. Just three columns from my database, and I can generate embeddings quickly.
Sometimes, the automatic graph detection can be a bit finicky, requiring manual adjustments to get the embeddings right.
It has streamlined my workflow for creating embeddings for client segmentation, which has directly improved my marketing strategies.
The speed and efficiency in generating embeddings are impressive, especially for a tool that doesn’t require a high-end setup.
I noticed that the embedding stability can vary depending on the dataset, which can affect downstream applications.
It has helped in creating effective recommendation systems, but I hope for improvements in embedding stability for diverse datasets.
The initial setup is straightforward, and I appreciate that I can work with simple data inputs.
The tool frequently crashes when handling larger datasets, which has led to data loss and frustration.
While it aims to assist with embedding generation, the crashes make it unreliable for critical projects.
I appreciate the concept of Cleora.AI and its potential to create embeddings quickly without requiring expensive hardware.
The software lacks comprehensive documentation, making it challenging to maximize its capabilities effectively.
While it aims to assist in generating embeddings for data analysis, I found it less reliable for larger datasets, which limits its utility in my projects.
Its speed is commendable, especially when working with smaller datasets. I noticed it outperforms some other tools in generating embeddings.
The user interface feels somewhat outdated and could benefit from a more modern design to improve user experience.
It simplifies the embedding process, but for complex datasets, I still find myself needing additional tools to achieve the desired results.