• Collected from approved sources
• Converted into machine-readable formats
• Cleaned and normalized for consistent processing
• Organized with metadata such as authorship, date, department, and access permissions

Embeddings convert text into mathematical representations that capture the meaning of words and sentences.

Instead of matching documents based only on keywords, embeddings allow the system to understand semantic relationships. For example, a question about “contract termination conditions” can retrieve documents discussing “ending agreements” even if the exact wording differs.

By representing documents and queries as vectors in a high-dimensional space, embeddings allow the system to identify conceptually related information, significantly improving search accuracy.

• Fast semantic search across large document collections
• Scalable retrieval from millions of knowledge entries
• Filtering based on metadata such as department or document type

The language model is responsible for generating human-readable responses.

Depending on security and deployment requirements, models can run:

• Within a private cloud environment
• On-premises inside the organization’s infrastructure
• Within secure enterprise AI platforms

This ensures sensitive information remains within the organization’s governance boundaries.