Subsumio doesn't just store documents — it builds a semantic case knowledge base so AI can understand context, detect risk, and ground results in verifiable sources.
Traditional systems store files and metadata. Subsumio models meaning: statements, roles, events, norms, and evidence chains are linked in a semantic structure — so AI can operate in case context.
Semantic search understands synonyms, legal phrasing, and context — reducing “hit-or-miss” results and saving time in complex matters.
Contradictions, timelines, party roles, and evidence references are cross-checked across the case — lowering the risk of missing critical details.
Outputs are tied to sources. Combined with audit trails and role-based access, this is substantially safer for compliance than ungrounded chat answers.
When matters are processed, the system derives abstracted patterns (e.g., typical argument chains, norm references, deadline and event structures). These patterns can — anonymized and aggregated — improve suggestion quality. Raw documents are not shared across firms.
Recurring patterns speed up orientation — from first upload to actionable guidance.
Suggestions become more context-aware as similar structural and argument patterns are recognized.
Anonymization and aggregation are prerequisites — compliance remains part of the architecture.