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
Writer
Score 8.6 out of 10
N/A
Writer is a full-stack generative AI platform. It consists of Writer-built LLMs, a graph-based RAG, AI guardrails, and a flexible application layer. Writer boasts users at enterprises like L’Oreal, Vanguard, and Accenture.
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
I think Writer is perfect for marketing professionals who need to draft copy about dense topics and subject matter, such as within tech and product companies. It bridges the gap of knowledge between marketer and engineer and saves the former countless minutes to bring what the latter develops to the marketplace
Regarding what Writer does, I can say as a first instance it makes my work easier when making my forms, sealing and inserting what I want to implement.
As a second place when it comes to directing me through a business document, you have the option of the synonym search engine and automatic correction.
As a third example, I would say that Writer makes my work easier, is easy to use[,] and saves versions of the same document.
i do find the organisation a bit confusing, like the functions available in 'ask Writer' being separate to the main window. there were lots of functions i wasn't able to find intuitively that we needed a support call for
asking for a summary of key points doesn't always pick up the main points
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
Writer's "recipes" and workflow approach allow me to think through the process of generation better than an open-chat format like ChatGPT or Gemini. It also is more flexible than Jasper (at least when I tested both).
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
