The Data Fabric eliminates the need to duplicate data storage - you don't pay twice for storing the same data in both operational systems and a separate data lake. Instead of physically copying data, the Knowledge Fabric manages metadata, lineage, and governance while data remains in its source systems, significantly reducing storage and operational costs. The fabric provides unified access, discovery, and observability across all sources by maintaining up-to-date metadata catalogs rather than replicating the actual data. This approach gives you data lake-like capabilities (governance, lineage tracking, unified querying) without the expense and complexity of maintaining a separate physical repository. You only need to manage and keep the metadata layer current, while the data itself remains in operational systems, which already maintain and update it. Any existing or future data lakes or data warehouses can be seamlessly added as additional sources to ensure the best utilization of their respective efforts.

Furthermore, the knowledge fabric is not limited to customer own data but can also extend to external data sources.

The platform doesn't replicate a full data warehouse; instead, it handles heavy data modeling by automatically mapping data to business-domain entities through the Knowledge Fabric, eliminating expensive ETL processes and complex dimensional modeling. Rather than attempting to address all analytical needs at once, LegalFab focuses on specific use cases, allowing you to start small and add more as your needs evolve. This approach dramatically reduces dependency on IT teams: business users interact directly with the Knowledge Fabric by reviewing and adjusting the glossary schema, validating entity mappings, and configuring data governance policies, without requiring technical data modeling expertise. Human oversight shifts from managing ETL pipelines and data warehouse maintenance to business-focused tasks, such as confirming that legal concepts are correctly mapped and entities are properly classified.

Minimal specialist knowledge is required because the system automatically discovers relationships from legal documents and case precedents using the domain ontology. The ontology is knowledge graph-based and materializes inference-oriented data management. The system is flexible enough to accommodate any form of relationship and adapt on the fly. Legal professionals review and validate the auto-generated ontology through the system, which requires legal domain expertise but not technical data modeling skills. The system flags low-confidence relationships and contradictory information for human review through dedicated queues, allowing your team to validate uncertain extractions without needing to understand the underlying technical mechanisms. Adding relationships that the system cannot infer is managed through the system’s configuration interface, where you can define additional question mappings or fact dependencies using business terminology instead of technical schemas.