Find the idea, even when the words change.
You may remember the question without remembering the phrase someone used. Memrain combines semantic search with keyword matching to retrieve relevant passages from the sources you can access.
Illustrative example
Why did we postpone the release?
Search can retrieve a decision note about a delayed launch, along with the source passage that explains the reason.
Illustrative scenario, not a live product result.Use both meaning and exact language.
Semantic retrieval looks for related meaning; keyword retrieval helps when names, identifiers and specific phrases matter. Memrain combines the results into one ranking and removes near-duplicates. The default path uses a query embedding without a chat-model call. Optional query expansion and reranking can add model-assisted retrieval when the operator enables them and accepts the additional usage cost.
Narrow the question when context matters.
Search and query can filter by page type and slug prefix, helping an assistant focus on a project or kind of record. Dates and source information help explain what a result represents. Literal text search is also available when you need to find an exact phrase in stored pages. Different retrieval methods answer different questions; use the one that fits the evidence you need.
Inspect the evidence before relying on it.
Returned passages include source pointers so an assistant can read beyond the snippet. An incomplete retrieval reports degradation where available, rather than turning every failure into an empty answer. Search quality still depends on what was imported, indexed and permitted. A small set of questions with known answers is a practical way to evaluate your own corpus.
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.