Cassandra applies large language models in specific program pathways to amplify an operator’s ability to extract key details from complex data.
The system is LLM-agnostic by design. Value resides in the application layer—viewpoint adaptation, corpus enrichment, constraints, and auditability—not in a model.
Bottom line
Cassandra is LLM-agnostic—the value is the application layer, not the model.
Cassandra employs a battery of unique safeguards, such as the Viewpoint creation system, that compound the rejection of false outputs by up to 98% for industry-leading extraction.
Additionally, all reasoning is user-inspectable, ensuring 100% auditability.
Bottom line
Cassandra rejects false outputs using user-defined key extraction parameters.
Cassandra’s Viewpoint system enables rapid scientific domain adaptation via explicit user-defined inputs—write in natural language the scientific domain and what specific data you want pulled from your documents, and the Viewpoint system will do the rest.
Bottom line
Define the scientific domain. Specify what details are most meaningful to you. Press enter.
RAG systems retrieve relevant passages and get lost in short order (Laban et al., 2025). Cassandra represents the next evolution, constructing structured relationships across documents and exposing the exact subgraph used to produce an answer, along with traceable reasoning paths for inspection and validation.
Bottom line
RAG retrieves text. Cassandra reasons over structure at scale for dense and contextually nuanced work.
Cassandra ensures compliance through evidence-linked outputs, inspectable reasoning paths, and layer-to-layer fail-safe controls. The system supports flexible deployment across cloud images, on-premises infrastructure, and SCIF-ready desktop environments.
Bottom line
Inspectable reasoning. Evidence-linked outputs. Deployable anywhere—cloud, on-prem, or SCIF.