The short answer
You cannot search Slack, Google Drive and Confluence together from inside any of them, because each one only indexes its own content. The three practical options are: search each app separately and stitch the answer together yourself, build a custom index that pulls all three in through their APIs, or connect all three to a unified search tool that queries them in one go and returns a single answer with citations. Only the third option preserves each app's permissions automatically while giving you one answer instead of three result lists.
Last updated: July 2026.
Here is a question that sounds trivial and is not: "What did we agree with Acme about the renewal discount?"
The contract is a PDF in Google Drive. The discount was actually negotiated down in a Slack thread with the account team in March. The reason for it, the bit that explains why we did it, is a paragraph in a Confluence page about pricing exceptions. No single search box in your company can see all three. So you open Slack, scroll, give up, open Drive, guess at filenames, then ask a colleague who is in a meeting.
This is the actual daily experience of working in a modern software stack, and it is the reason cross-app search exists as a category. Let us go through what genuinely works.
Why each app's search cannot do this
It is worth understanding the constraint, because it explains why the workarounds are the shape they are. Slack indexes Slack messages. Drive indexes Drive files. Confluence indexes Confluence pages. Each index is built from, and permissioned by, its own system. None of them has any mechanism to see inside the others, and none of them ever will, because they are competing products from companies with no incentive to index each other.
Worse, each one is a different kind of search. Slack search is conversational and chronological, and terrible at recall past a few weeks. Drive search is filename-and-content matching, and only as good as your naming discipline. Confluence search is wiki-shaped and will happily return an outdated page with no signal that it is outdated. So you are not just searching three places, you are using three different mental models to do it.
Option 1: search each app separately (and what to at least do well)
The default. It is free, and for a question whose answer lives entirely in one app it is fine. If you are going to do this, learn each app's operators, because most people never do and it costs them hours:
| App | Operators worth knowing | Where it fails |
|---|---|---|
| Slack | in:#channel, from:@person, before: and after:, quotes for exact phrases, has:link | Recall drops off fast with age; you must remember roughly who said it and where |
| Google Drive | type:pdf, owner:, before: and after:, title:, full-text search inside documents | Shared drives and personal drives fragment results; scanned PDFs are invisible unless the text was extracted |
| Confluence | CQL, for example space = ENG, label = policy, lastmodified > now("-30d") | Stale pages rank alongside current ones with no freshness signal |
That table is the ceiling of the manual approach. It makes you faster inside each silo. It does nothing about the fact that your answer is split across three of them, and it does not help at all when you do not know which app the answer is in, which is most of the time.
Option 2: build your own unified index
The engineering path. You pull content from each app's API, normalize it, chunk it, embed it into a vector store, and put a retrieval layer and a UI on top. Every app in question has a workable API, so this is entirely possible.
It is also the option that consistently gets underestimated, and the reason is almost never retrieval quality. It is permissions. To be safe, your index has to know that Dana can see the #leadership channel and Sam cannot, that this Drive folder is restricted to the finance team, and that this Confluence space is locked to HR. Then it has to keep knowing that as people join teams, leave channels and change roles, and it has to enforce it at query time, per user, on every request.
Get that wrong in a way that leaks a compensation spreadsheet into a general search result and you have caused a serious incident. This is the single hardest part of internal search, it is far harder than the AI, and it is why homegrown projects tend to stall at the demo stage: the demo works because it ignores permissions, and the production version cannot.
Add ongoing connector maintenance (APIs change, tokens expire, rate limits bite) and you have a permanent internal service, not a project. Build it if search is genuinely core to your product. Do not build it so that people can find the renewal terms.
Option 3: connect all three to one search
A unified search tool connects to Slack, Google Drive and Confluence through their official APIs, inherits each system's permissions rather than reinventing them, and searches all of them in a single query. The good ones do not hand you three merged lists of links, they read the retrieved material and write you an answer with inline citations to the exact message, file and page it came from.
Back to the Acme question. One query, and you get: the agreed discount, with a citation to the contract PDF in Drive; the negotiation, cited to the March Slack thread; and the policy justification, cited to the Confluence page. The three fragments assembled into one answer, each traceable to its source so you can verify it in a click. That is the whole value proposition, and it is why searching across all company apps in one query is a different activity from searching three apps quickly.
The setup is genuinely a connection step rather than a build. You authorize each app through OAuth, the tool syncs and respects the access controls already defined in Slack, Drive and Confluence, and people start asking questions in plain English. Our Slack and Google Drive search page walks through what that looks like across those two specifically, and Confluence search covers the wiki side.
Does unified search respect permissions?
It has to, and this is the question to interrogate hardest with any vendor. The correct answer is that permissions are inherited from each source system and enforced at query time, per user, at the item level. That means the search does not maintain its own separate idea of who can see what. It asks the source, or mirrors the source's access control list, and applies it to every result before the answer is composed.
The wrong answer, and it is more common than it should be, is an index built once with a service account that can see everything, with permissions filtered afterwards or checked at the folder level rather than the item level. Ask specifically: is enforcement at query time or index time, is it item-level or source-level, and what happens the moment someone is removed from a private channel? If the answer is vague, walk. We wrote up why in permission-aware enterprise search.
What about searching everything else too?
Slack, Drive and Confluence is the most common trio, but almost nobody stops there. The answer to a real question routinely also touches Jira, Gmail, Notion and Salesforce. The good news is that once the connector-and-permissions model is in place, adding sources is incremental rather than a new project each time.
One distinction worth drawing, because it trips people up: internal search is not the same thing as a customer-facing assistant. Unified enterprise search is permission-scoped and answers your employees from private company knowledge. If what you want instead is an assistant answering visitors on your website from your public content, that is a different product with a different security model, and conflating the two is how private material ends up somewhere public.
The practical recommendation
If your questions live inside one app, learn that app's operators and move on. If your questions routinely span Slack, Drive and Confluence, which for most teams they do, the manual approach is a tax you pay every single day, and building your own is a permissions project wearing an AI project's clothes.
Connecting the three to one permission-aware search that returns a cited answer is the option that actually matches the problem. You can see it working on your own apps in a few minutes, which is the only test that ever really settles it.