# A workplace assistant over your own documents, decoded

_Teardown_

Decoded from Glean (Glean), Glean Enterprise Graph (Glean), Microsoft 365 Copilot (Microsoft), Microsoft 365 Copilot semantic index (Microsoft).

## What you see

Type a question into the search or chat box built into a company's own tools, something like
where the signed contract for this account is or what the team decided about pricing, and instead
of a page of links you get an answer in a sentence or two, with a few citations pointing at the
document, email, ticket or chat message it came from. Glean and Microsoft 365 Copilot both sell a version of this: a
box that answers from an organization's own files, mail and chat instead of the open web.

What's easy to miss trying this once is that the box does not answer everyone the same way. Ask it
the same question from two different desks and the citations that come back can differ, because
each answer is built only from what that person is already allowed to open. Nobody types a
permissions request; the scoping happens before the question is answered at all.

## What is happening underneath

The lowest layer turns text into numbers before anything gets compared. Every document, and the
question itself, becomes a vector, a list of numbers positioned so similar meanings sit near each
other, so a search can match a question worded one way against a passage using different words.
Microsoft documents building this at real scale: its semantic index lets an organization "search
through billions of vectors (mathematical representations of features or attributes) and return
related results"[2]. Glean's own workplace-search page names the
same mechanism more plainly: "Vector search powered by deep learning-based LLMs enables semantic
understanding for natural language queries."[3] Neither page describes a model choosing
anything at this layer; it is [embeddings and
search](/gradient_ascent/techniques/embeddings-search/), level 2, a fixed comparison the code runs before a model sees the question.

Onto that comparison, Microsoft's documentation adds one more fixed step it calls grounding:
"Copilot preprocesses the input prompt by using grounding and accesses Microsoft Graph in the
user's tenant"[1], appending whatever the search turned up to the prompt before the
model answers. Glean's own account of its process names the same two moves in order: "Understand,"
matching the query by meaning, then "Generate," where "Glean's AI understands your query's context
and retrieves the most relevant answers, drawing from up-to-date information across your
tools"[3]. One search, one answer, cited: this is [RAG](/gradient_ascent/techniques/rag/),
still level 2. Neither page describes a second search running after the first comes back thin.

Glean documents a separate structure beside the vector index, for questions a single passage
can't answer alone. Its Enterprise Graph "builds on a rich knowledge graph that identifies high
value entities — such as projects, people, customers, and products"[4], and its own FAQ
for the product says it "enables multi-hop reasoning across projects, people, documentation,
support issues, and other connected work"[4]: a question needing a project and the
person who owns it joined, rather than one document stating both. This is [knowledge graphs](/gradient_ascent/techniques/knowledge-graphs/). Neither Microsoft source here
describes anything like it for Microsoft 365 Copilot; grounding there is documented as a single
retrieval step, not a graph walk.

Every layer above runs inside a boundary neither maker describes as the model's to decide.
Microsoft's architecture documentation states it as a system limit, not a setting: "Copilot only
accesses data that an individual user is authorized to access, based on, for example, existing
Microsoft 365 role-based access controls", and "Copilot can't access data that the user doesn't
have permission to access"[1]. Its semantic-index documentation repeats the same
boundary at the retrieval step itself: "the grounding process only accesses content that the
current user is authorized to access."[2] Glean states the identical property of its own
search: "Results are real-time and permissions-aware, so everyone sees only what they should", and
"Glean enforces the existing permissions of your data sources in results, so users only see what
they are allowed to access"[3]. This is [safety, privacy
and governance](/gradient_ascent/techniques/safety/)'s territory, and it is the one part of the system that is not itself a
retrieval technique: a check against the app's own access-control list, run on every answer
regardless of what the model would otherwise have shown.

## Which page explains each part

| What you see | What it is | Page |
|---|---|---|
| A question is matched to passages that never use its exact words | Text compared as vectors instead of by keyword | [Embeddings and search](/gradient_ascent/techniques/embeddings-search/) |
| One search, then one answer, with citations | Retrieved passages handed to the model once | [RAG](/gradient_ascent/techniques/rag/) |
| An answer joins a fact about a project to a fact about the person who owns it | Entities and relationships walked as a graph | [Knowledge graphs](/gradient_ascent/techniques/knowledge-graphs/) |
| The same question from two desks returns two different sets of citations | Retrieval filtered by the asker's own access, not the model's judgment | [Safety, privacy and governance](/gradient_ascent/techniques/safety/) |

## What the makers say

Microsoft is specific about what the tenant boundary does not grant on its own: "Operating inside
the Microsoft 365 service boundary doesn't grant Copilot tenant-wide visibility."[1] And
about what does not leave it: "Prompts, responses, and data accessed through semantic indexing
aren't used to train foundation LLMs, including those used by Microsoft Copilot."[2]

Glean describes how its own graph gets built without a person hand-labeling any of it: "Glean
builds these graphs entirely using machine learning by understanding the data structures of
enterprise apps and automatically inferring entities. All of this happens in each customer's
single-tenant environment to ensure data privacy."[4] And it ties that structure directly
to what a searcher sees: "Glean builds your company's knowledge graph—understanding people,
content, and interactions— so every result is personalized to you."[3]

## Where it fails

Microsoft documents where the grounding step's own coverage stops short. Its supported-content
table for the semantic index lists delegated mailboxes, shared mailboxes and archived mailbox data
as not supported at either level, so a question about mail someone else manages for you can come
back with nothing found[2]. Freshness has a stated limit too: "New documents that are
added to SharePoint Online sites that are accessible, via site inheritance, by two or more users
are indexed daily"[2], not the moment they're saved.

Neither maker publishes a number for how often a citation actually supports the sentence next to
it, and this page does not invent one. Underneath the permission check, both systems are still a
search, and [RAG](/gradient_ascent/techniques/rag/)'s own failure modes predict the rest: a
chunk boundary splitting a fact in two, a passage cited without being the one the answer actually
used, two sources disagreeing with no sign either was checked. The permission check decides which
documents an answer may draw on. It does not decide whether the ones it drew on support the
sentence they are attached to.

## If you build one

The retrieval half of this is the site's own [document
Q&A](/gradient_ascent/recipes/document-qa/) recipe, level 2: chunk, embed, retrieve the top few, answer once. What the recipe does
not add, because a single-user example has nobody to filter for, is the check both makers describe
running on every retrieval rather than every model turn. Build that filter into the query that
fetches candidate passages, against the access-control list the document already lives behind, and
never phrase it as an instruction the model is asked to follow. [Safety, privacy and governance](/gradient_ascent/techniques/safety/) is where that instinct comes from:
a permission the model must remember to respect is one that gets forgotten eventually, under a
long conversation or an injected instruction. A permission the retrieval step never fetches cannot
be leaked by anything the model says afterward.

Reach for a [knowledge graph](/gradient_ascent/techniques/knowledge-graphs/) only once questions
need facts joined across documents a single search keeps missing; Glean's own account of building
one describes real, ongoing extraction work[4], not a setting to switch on.


## Sources

1. [Microsoft Copilot architecture and how it works](https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-architecture) — Microsoft (Microsoft Learn) (accessed 2026-09-19)
2. [Semantic indexing for Microsoft Copilot](https://learn.microsoft.com/en-us/microsoftsearch/semantic-index-for-copilot) — Microsoft (Microsoft Learn) (accessed 2026-09-19)
3. [Workplace Search AI – Instantly Find Answers Across All Apps](https://www.glean.com/product/workplace-search) — Glean (accessed 2026-09-19)
4. [Enterprise Graph: Powering AI with Deep Organizational Knowledge](https://www.glean.com/product/knowledge-graph) — Glean (accessed 2026-09-19)


## Techniques it decodes into

- [Retrieval-augmented generation (RAG)](/gradient_ascent/techniques/rag/) (measured): Searching your documents and giving the results to the model.
- [Embeddings and search](/gradient_ascent/techniques/embeddings-search/) (sourced): Finding text by meaning instead of by keyword.
- [Knowledge graphs and GraphRAG](/gradient_ascent/techniques/knowledge-graphs/) (sourced): Storing facts as entities and relations, for questions that span several documents.
- [Safety, privacy and governance](/gradient_ascent/techniques/safety/) (sourced): Prompt injection, permissions, data handling and audit.

Last reviewed 2026-09-19. This teardown expires 2027-03-18.