Thanks for the feedback regarding the Readme. We have some additional detail in the docs here https://dataherald.readthedocs.io/en/latest/index.html https://dat

Thanks for the feedback regarding the Readme. We have some additional detail in the docs here https://dataherald.readthedocs.io/en/latest/index.html https://dataherald.readthedocs.io/en/latest/index.html (including an architecture doc), but will definitely be incorporating some of the feedback as we iterate on the documentation which. You can connect the hosted version to your data warehouse using the SQLAlchemy connectors which are in the open source engine, and the connection info will be stored after being encrypted. An on-prem version of the enterprise product is also on the roadmap. For protecting against errors the approach is to allow for a human to be in the loop as you alluded to. One of the core pieces of the engine is the Evaluation module which combines white box and black box techniques. You can block the response from going back to the question asker if the confidence level is below a certain threshold until a human verifies the response manually (or always put the human from the data team in the loop).