A chat with data platform allows users to interact with their proprietary data through natural language conversations. An industry-specific version tailors the underlying language models to the terminology, regulations, and best practices of a particular sector like healthcare, finance, manufacturing etc. This enables the AI to provide nuanced insights contextualized to that domain.

The language model is first trained on a broad corpus of data to build general language understanding. It is then further trained on proprietary industry data like documentation, codebases, data models etc. This allows it to comprehend the unique language constructs, jargon, and concepts of that specific sector.

It leverages services like Azure Language Services for natural language processing, Azure Machine Learning for custom model training, Azure Cognitive Search for intelligent data retrieval, and Azure Cognitive Services like text analytics. These are orchestrated on Azure App Service or Azure Kubernetes Service.

Data never leaves the customer’s environment. The platform can be deployed on the customer’s Azure private cloud, Azure Arc-enabled data centers or other hosting options with advanced data encryption, anonymization, and access controls.

Yes, it can connect to a wide range of on-premises and cloud data sources like databases, data lakes, APIs, SaaS applications and more using Azure Data Factory pipelines and pre-built connectors. Federated querying enables unified data access.

In addition to general industry fine-tuning, the language model can be further customized by training it on an enterprise’s proprietary data assets like documentation, product data, codebases and more using Azure Machine Learning and modeling frameworks. 

Definitely, the language models can distill deep veteran expertise by being exposed to data sources encapsulating that knowledge like documented best practices, design documents, conversation transcripts and more during custom training.

It supports a wide variety of data-centric interactions like querying, exploration, root cause analysis, forecasting, anomaly detection, recommendation and more through natural conversations in the user’s own domain language constructs.

By incorporating compliance rules, industry standards and governance policies during the model training process, the AI’s responses can be gated to align with relevant regulations like HIPAA, GDPR, FINRA and more.

Yes, it can interface with popular BI, data visualization, and analytics tools through APIs and direct data hooks, enabling capabilities like automated insight narration, data storytelling, what-if analysis and more through natural language interactions.