Is Cassandra tied to one LLM?
Cassandra applies large language models in precise, controlled doses to amplify an operator’s ability to extract insight from complex data.
The system is LLM-agnostic by design. Value resides in the reasoning layer—constraints, domain adaptation, provenance, and verification—not in any single underlying model.
Bottom line
Cassandra is LLM agnostic- the value is the reasoning layer, not the model.
Does Cassandra hallucinate?
LLM’s are probabilistic and may produce errors. Cassandra is architected for full transparency and lowest friction error detection. The system surfaces confidence, provides direct links to evidence, and enables one-click verification to underlying source passages.
Bottom line
LLMs can err; Cassandra makes every conclusion inspectable, traceable, and verifiable back to source.
How fast can Cassandra adapt to a new domain?
Cassandra’s Aperture Engine enables domain adaptation by allowing teams to define acronyms, entities, and domain rules once, then reuse and extend them across workspaces as needs evolve. This produces an enterprise-ready system that conforms precisely to the operational fingerprint of each mission.
Bottom line
Teach once. Reuse everywhere. Adapt Cassandra to your mission’s fingerprint.
How is this different from a basic RAG chatbot?
Traditional RAG systems retrieve relevant passages. 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—and shows its work.
Can this support audit-heavy environments?
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
How confident can I be in Cassandra’s outputs?
Cassandra operates as a reasoning engine whose analysis is dependent on input quality. Digitally native documents preserve structure, metadata, and semantic integrity, enabling the highestconfidence extraction, linking, and auditability. Where available, digitally native sources are strongly recommended to ensure optimal system performance and defensible outputs.
Bottom line
Cassandra’s outputs are only as strong as its inputs- digitally native documents deliver the highest fidelity and defensibility.