Semantica: AI memory with provenance
I keep coming back to the same question when I look at a new “AI memory” tool: is this storing meaning or just embeddings with good marketing?
Semantica is one of the few that actually answers it right. A decision isn’t a log line, it’s a graph node, with inputs, a confidence score, a causal chain, exportable as W3C PROV-O. That’s the piece I rarely see teams bother with because retrieval quality is the fun problem and provenance is the boring one nobody gets promoted for building.
Underneath Semantica: a context graph, five reasoning engines (forward chaining, Rete, deductive, abductive, SPARQL), polyglot storage across Neo4j/FalkorDB/RDF. This is knowledge graph and expert-systems know-how finally hitching a ride on the agentic AI wave, in new clothing. Not a criticism of the build, just a reminder that we’ve solved this before. What I’ll actually be watching is whether teams adopting it populate the ontology with any discipline or let entity resolution drift the way it always does when nobody’s minding the graph.
If you’re building exposure-provenance knowledge into a regulated AI stack, is PROV-O what your customers/auditors actually want or are you exporting something else?
⦿ Semantica AI: https://getsemantica.ai/ ⦿ Prov-o: https://www.w3.org/TR/prov-o/