An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
N/A
Oracle Digital Assistant
Score 7.9 out of 10
N/A
Oracle Digital Assistant delivers an AI platform to create conversational experiences for business applications through text, chat, and voice interfaces.
Use H2O.ai whenever you need easy to use tool, when you must be cost efficient (you can not charge the client extra money for software licenses used), need a tool with lots of algorithms that are normally used in data analytics, or need to work on one machine (it is either not allowed to move data to cloud storage or simply not necessary to connect to Hadoop, etc.). Also, you can call H2O directly from Python which makes analysis more efficient.
As mentioned in pros and cons, it depends on the use cases. Most of the normal chatbot use cases can be handled by ODA. If you want to build a chatbot with menu style or conversation style (it is not straight forward but it can be done), ODA can be perfect. If use cases are to also include AI with emotional intelligence to make conversational more interactive and also be able to detect through AI engine automatically what a person would like to do or perform or ask, then ODA may not fit for it. If you also are looking for virtual agents through tighter IVR integration then ODA may not be right. There are a few limitations around the number of words in the text to voice feature.
This is not really a drawback, but rather a warning - the Drivereless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.
I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than H2O.
I selected Oracle Digital Assistant against all other digital assistant platforms as this platform works like a charm with any Oracle application. It integrates well with Oracle Integration Cloud. The new beta version has an inbuilt conversation builder which can be used to build conversation without the YAML code.
Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
