problem_kicker
AI search over SharePoint often looks convincing in a prototype and becomes slow, stale or unsafe in production. Large tenants combine many sites, document versions, permission boundaries and continuously changing content; retrieval must respect all of them on every query.
DEMAND LANGUAGE / REAL-WORLD PROBLEM
“Why is our AI search good in the test but slow or wrong with real SharePoint permissions?”
“The document exists — why does search only know about it hours later?”
WHAT CAUSES THIS?
Full recrawls turn freshness into a batch problem and create avoidable load.
architecture_for SHAREPOINT AI SEARCH PERFORMANCE
We first map tenant boundaries, permission semantics, content churn and query classes. The production design then separates ingestion, permission projection, retrieval and answer generation so each stage can be benchmarked and degraded independently.
Authorization should be enforced before protected content becomes a usable retrieval candidate. ACL projections need explicit freshness semantics, deny-by-default behavior and tests for group changes, inherited permissions and deleted content.
Measure indexing lag, candidate retrieval latency, reranker latency, end-to-end p50/p95/p99, cache hit rate and quality under representative permission filters.
SharePoint · Microsoft Graph · hybrid retrieval · vector search · ACL
failure_kicker
measure_kicker
verify_intro
CTO / CIO FAQ
Not necessarily. The right design depends on retrieval latency, source limits, permission semantics and freshness targets. A production system can combine source metadata with controlled indexes.
Usually not by itself. Enterprise queries often need exact lexical matches, metadata filters and permission-aware ranking in addition to semantic similarity.
Measure the full query trace first. Candidate generation, ACL filtering, reranking and answer generation have different scaling behavior and should not be collapsed into one timing.
connected
