Keep saved knowledge ready to use.
As your knowledge grows, its index needs care. Memrain runs a background maintenance cycle to keep stored pages and retrieval data connected.
Illustrative example
Why can I open this new page but not find it in search yet?
Check indexing and job status: some writes become readable before the maintenance cycle has added their content to search.
Illustrative scenario, not a live product result.Maintain the index as content changes.
The cycle performs routine indexing and housekeeping over the stored corpus. When page content changes, unchanged chunks can reuse their existing embeddings instead of paying to embed the whole document again. Different ingestion paths can leave work for later reconciliation. That makes it important to distinguish a successful write, a completed indexing pass and a successful retrieval test.
Choose whether to add synthesis.
Optional model-assisted phases can distill concepts, takes, reflections and recurring patterns from saved material. They are controlled by configuration and can incur model usage. Generated synthesis lives separately from original notes, and the system avoids simply feeding its own derived material back as raw evidence. These features can help organize a large corpus, but they should be evaluated rather than assumed accurate.
Observe the result instead of assuming health.
Diagnostics expose storage, indexing, maintenance and job information. Failed collectors and incomplete results should remain visible. An opt-in evaluation probe can run a fixed question set under a spend ceiling, once the operator installs and enables it. It helps track retrieval quality on that corpus; it is not a public accuracy benchmark or a guarantee that all questions will be answered correctly.
Keep the context. Move the work forward.
Connect your knowledge to the AI you already use. Find your starting point and make the next conversation count.