OpenAI Embeddings integration

The OpenAI Embeddings integration adds semantic search and semantic analysis capabilities to Catalyst workflows.

Embeddings convert text into numerical vector representations, allowing systems to analyse meaning and contextual similarity rather than relying only on keywords.

Typical use cases

OpenAI Embeddings can be used for:

• semantic document search
• similarity matching
• intelligent document retrieval
• data classification
• clustering and grouping
• AI-enhanced analysis

Example business scenarios

Examples include:

• semantic clause searching in iManage
• identifying risky contract language
• fraud investigation support
• AI-assisted document review
• financial report analysis
• contextual knowledge retrieval

Configurable settings

The Embeddings integration supports configuration of:

• models
• input formats
• encoding methods
• vector dimensions

Integration with workflows

Embeddings can be combined with:

• iManage
• Salesforce
• Microsoft Graph
• DocuSign
• workflow automation
• Sequencer tasks and records

Things to remember

• Embeddings analyse meaning and similarity rather than generating conversational text.
• Search quality depends on the quality and consistency of the source content.
• Some use cases may require integration with external document systems.

Autologyx Classification: Unrestricted, Public