For content generation

Content summarization

• Summarizing ESG goals from web pages, annual reports, earning calls, news, etc.
• Summarizing project status, data tables, e-mails, complex documents

Automatic description generation

• Describe a table/function/database/view based on its schema, data samples and context.

For content understanding

Deep meaning search (vector embeddings)

• Find the best sentence or paragraph in policies & procedures, FAQs, documentation, help files, web content, etc.
• Find duplicative content – across web sites, documentation, etc.

Content classification

• Identifying risky clauses in contracts, find improper language, locate terms & conditions, identify root cause statements, connecting regulatory statements to corporate obligations, etc.
• Export controls, secret classification, intellectual property, privacy, Material Non-Public Information, etc.

Meaning Enriched Business Identifiers

• Users can find identifiers with simple names and descriptions
• Business entities enriched with deep meaning vectors

To learn more about these applications and how to configure them go to Generative AI Components

Models supported

• Azure Open AI
  • GPT 3.5/4
  • Text Embedding ADA v1 & v2 (text-embedding-ada-002)
  • Customized chat & embeddings models

• On premise Python models (with Aspire + Python Bridge model)
  • BERT
  • T5
  • GTR-T5
  • MiniLM
  • any other embeddings models