Solutions for high-stakes LLM use

Persistent memory + factual control

Remanentia

Evidence memory for local AI systems that need to remember operational context, retrieve sources, and verify answers before they leave the system.

100%current operational recall
75.8%factuality benchmark BA
72.2%LongMemEval R11
2,062current repo test count

What it does

Turns scattered project knowledge into controlled evidence.

Remanentia is built for operators who need AI systems to use memory without turning every internal record into public text.

Memory

Durable recall

Indexes files, docs, handovers, session logs, project facts, and selected corpora into a refreshable memory layer.

Retrieval

Evidence search

Combines keyword search, embeddings, reranking, compiled facts, and direct recall paths for source-grounded answers.

Control

Factual guard

Director Class AI verifies generated answers against retrieved evidence and blocks unsupported public output.

Boundary

Public safety

Public APIs expose allowlisted corpora and redacted snippets while private operational memory remains internal by default.

Operating loop

Retrieve first. Verify next. Answer last.

Remanentia separates memory from output. Retrieval produces evidence; the control layer checks that evidence against the intended answer and deployment policy.

1Request enters from an agent, product, or API path.
2Policy selects private recall, public search, or no-generation mode.
3Memory returns sources, snippets, and compiled facts.
4Factual checks and public-output controls accept or reject the answer.

Measured state

Proof points from the current local operator build.

Operational recall100%

Recall@1 / @5 / @10 / @20 on the current operational suite.

Vector state236 MB

60,890 chunks, 768 dimensions, full rebuild measured at 391.06s.

Public vector search95.3ms

p50 through the local API path, with p95 measured at 121.4ms.

Factual control75.8%

Balanced accuracy on the factual-consistency benchmark.

Temporal retrieval65.4%

LongMemEval R11 temporal score, with 72.2% overall.

Regression surface2,062

Current Remanentia repository test count tracked in shared project state.

Deployment fit

For teams building memory-backed AI products on private knowledge.

Self-hosted memory service

Run Remanentia beside existing agents and applications, with local-first inference and explicit hosted fallback modes.

Controlled public API

Expose only approved corpora, verified snippets, and factual-control decisions to customer-facing surfaces.

Commercial factual guard

Use Director Class AI licensing when the verification layer ships inside closed-source products or SaaS systems.