Why your team can't find anything in SharePoint

SharePoint's search limitations are well-documented, but the deeper problem is usually the folder structure and permissions model underneath it, not the search feature itself.

Why SharePoint's native search underperforms for most teams

SharePoint's built-in search relies heavily on keyword matching and metadata, which works reasonably well when documents are consistently tagged and organized — and works poorly when they're not, which describes the overwhelming majority of real-world SharePoint environments we've audited. Beyond the search technology itself, SharePoint sites often accumulate years of inconsistent folder structures as different people, with different organizational instincts, add content over time, with no enforced convention.

The permissions complexity that makes this worse

SharePoint's granular permission model, while powerful, means search results are filtered per-user based on complex nested permission inheritance that can produce confusing outcomes — a document technically exists and is findable in theory, but a specific user's search returns nothing because of an inherited permission restriction three folder levels up that nobody remembers setting. This makes "I searched and found nothing" ambiguous: is the document genuinely not there, or is it a permissions issue masquerading as a search failure? Most users can't tell the difference, which erodes trust in search generally.

Our approach: semantic search layered on top, not replacing SharePoint

Rather than migrating content out of SharePoint (often impractical given how deeply embedded it is in a Microsoft-centric organization's workflow), we build a semantic search layer that indexes SharePoint content via the Graph API, respecting the same underlying permission model, but adding vector-based semantic search on top of SharePoint's more limited native keyword search. This means a query can match conceptually related content even when exact keywords don't align, while still correctly filtering results based on each user's actual SharePoint permissions — so results aren't just more relevant, they're also correctly scoped.

A concrete example

A professional services firm with roughly 15 years of accumulated SharePoint content across multiple sites had a well-documented internal complaint that "SharePoint search never finds anything." Our audit found two compounding issues: content organization had gone through at least three different informal conventions over the years with no migration between them, and native search relevance was poor even for content that did exist, because SharePoint's keyword matching didn't handle the natural language, conversational way people actually phrased their searches.

We built a semantic search layer via the Graph API, correctly respecting existing permissions, without requiring any migration of the underlying content or folder restructuring (which would have been a much larger, riskier project on its own). Search success rate — measured via a before/after survey asking staff whether their last SharePoint search found what they needed — improved from roughly 35% reporting success to over 80% reporting success, without touching the underlying document organization at all.

How Ndakum approaches it

We build semantic search layers on top of your existing SharePoint (or similar) environment in our AI Knowledge Assistant work — respecting existing permissions, without requiring a disruptive content migration.