When systems retrieve and surface information — RAG, recommendations, search results, alerts — semantic similarity alone isn't sufficient. Memrail governs what is appropriate to surface based on user context, source eligibility, fatigue, and policy — turning retrieval from a search problem into a decision problem.
This pattern applies when
The Problem
Retrieval returns content that matches the query's embedding but is completely wrong for the user's actual situation. A junior employee gets expert-level documentation. A European customer gets US-specific policies. Similarity is not relevance.
The retrieval system has no concept of who is asking, what they've already seen, or what their current state is. Every query is treated as if it exists in a vacuum, ignoring the rich context that should govern what gets surfaced.
All sources are treated equally regardless of appropriateness, recency, or authority. An outdated blog post ranks alongside official documentation. A deprecated API reference appears next to the current one. No source hierarchy exists.
The same content or sources are surfaced repeatedly because there is no concept of fatigue management. Users see the same recommendations, the same documents, and the same answers — creating noise instead of value.
The Solution
Memrail transforms retrieval from a similarity search into a governed decision. Before content reaches the user, it passes through a decision plane that evaluates eligibility, appropriateness, and fatigue — not just embedding distance.
Pre-retrieval routing directs different query types to different retrieval strategies. Post-retrieval filtering applies eligibility rules based on structured metadata — user role, content recency, source authority, and compliance requirements. Fatigue management tracks what has been recently surfaced and suppresses repetition. And every surfacing decision is explainable: you can answer exactly why a piece of content was shown or not shown.
Example: Governed knowledge advisor
Consider a knowledge advisor that surfaces insights for founders from a curated content library. Without governance, the system returns whatever is semantically closest to the query — regardless of whether the founder has already seen it, whether the source is appropriate for their stage, or whether they've been overwhelmed with similar content recently. With Memrail, source eligibility rules ensure only stage-appropriate content is surfaced. Category fatigue management prevents the same topics from dominating. Persona-governed retrieval adapts what gets shown based on the founder's profile and history.
Platform
Pre-retrieval routing and post-retrieval filtering as governed decision points. Rules evaluate content eligibility based on structured metadata, not just similarity scores.
Learn moreExplainable surfacing decisions. Answer exactly why a piece of content was shown, suppressed, or deprioritized for any specific user query.
Learn moreTest new retrieval governance rules in shadow mode. See how they would change what gets surfaced before activating them for real users.
Learn moreVerify that your content metadata supports the governance rules you want to apply. Identify gaps between what your rules require and what your content provides.
Learn moreIndustries
These patterns apply across industries. The business rules change; the governance model doesn't.