Durable recall
Indexes files, docs, handovers, session logs, project facts, and selected corpora into a refreshable memory layer.
Persistent memory + factual control
Evidence memory for local AI systems that need to remember operational context, retrieve sources, and verify answers before they leave the system.
What it does
Remanentia is built for operators who need AI systems to use memory without turning every internal record into public text.
Indexes files, docs, handovers, session logs, project facts, and selected corpora into a refreshable memory layer.
Combines keyword search, embeddings, reranking, compiled facts, and direct recall paths for source-grounded answers.
Director Class AI verifies generated answers against retrieved evidence and blocks unsupported public output.
Public APIs expose allowlisted corpora and redacted snippets while private operational memory remains internal by default.
Operating loop
Remanentia separates memory from output. Retrieval produces evidence; the control layer checks that evidence against the intended answer and deployment policy.
Measured state
Recall@1 / @5 / @10 / @20 on the current operational suite.
60,890 chunks, 768 dimensions, full rebuild measured at 391.06s.
p50 through the local API path, with p95 measured at 121.4ms.
Balanced accuracy on the factual-consistency benchmark.
LongMemEval R11 temporal score, with 72.2% overall.
Current Remanentia repository test count tracked in shared project state.
Deployment fit
Run Remanentia beside existing agents and applications, with local-first inference and explicit hosted fallback modes.
Expose only approved corpora, verified snippets, and factual-control decisions to customer-facing surfaces.
Use Director Class AI licensing when the verification layer ships inside closed-source products or SaaS systems.