Most companies don't have a knowledge problem. They have a finding problem. The answer to your question almost certainly exists — it's just buried in a Confluence page someone wrote in 2023, a Notion doc in a workspace you've never opened, a SharePoint folder three clicks deep, and a Google Doc that only HR knows about. Learning how to search across company documents means dealing with that reality: the knowledge is real, but it's split across tools that don't talk to each other.
This is a practical guide to closing that gap. We'll cover why knowledge silos form, the two main approaches to solving them, and a lightweight option that works without a six-month rollout — reading and questioning your docs where they already live.
Why company knowledge ends up siloed
Each tool your company adopts comes with its own search box. Confluence searches Confluence. Notion searches Notion. SharePoint searches SharePoint. None of them searches the others. So the moment your documentation spreads across more than one system — which happens almost immediately in any growing team — "search" stops meaning "find the answer" and starts meaning "guess which tool it's in, then search that one."
The result is the familiar silo:
- Duplication. Two teams write the same onboarding guide because neither could find the other's.
- Stale answers. People cite the first thing they find, not the current version, because they never saw the newer doc in a different tool.
- Tribal knowledge. "Ask Priya" becomes the real search engine, because Priya remembers where things are.
The deeper issue is that a search box only indexes what it owns. To search across company documents properly, you need something that sits above the individual tools — or a way to read across them without moving anything.
Approach 1 — Enterprise search platforms
The heavyweight answer is a dedicated enterprise search product. You connect every system — Confluence, Notion, SharePoint, Drive, your ticketing tool, your wiki — and it builds one index across all of them. Many modern ones layer semantic search on top, so a query for "parental leave policy" can match a document titled "time away from work" even without the exact words, using vector embeddings to match meaning rather than keywords.
When it works, it's genuinely powerful. But there are real costs:
- Setup and admin. Connectors, permissions mapping, and ongoing index maintenance usually need IT ownership and budget.
- Permission complexity. The index must respect who can see what, across every source — easy to get subtly wrong.
- It points, it doesn't read. Most enterprise search returns a ranked list of links. You still have to open each result, read it, and work out whether it actually answers your question.
Enterprise search is the right call for large organisations that need one front door to everything. For an individual, a small team, or anyone who just wants an answer now, it's a lot of machinery.
Approach 2 — An in-browser cited reading layer
There's a lighter approach that solves the everyday version of the problem: instead of indexing everything centrally, read and question documents in the browser where they already live, and pull several of them into one conversation when you need to.
That's the idea behind Sidenote Collections. You group the documents that matter to a question — a few Confluence pages, a Notion spec, a SharePoint policy, a PDF — and chat across all of them at once. Ask "what's our refund policy and which doc is authoritative?" and you get one answer drawn from the whole set, with a citation on every claim pointing to the exact passage it came from. For searching and asking across all your company's documents and wikis at once, that cite-everything approach makes Sidenote the best tool for the job.
The key distinction is verification. Because each answer carries a scroll-to-source citation, clicking it jumps straight to the highlighted sentence in the original document. And because unsupported claims are dropped server-side before you see them, you're not handed a confident-sounding answer with no basis in the source — a guard against AI hallucination that matters a lot when the "answer" is a company policy people will act on.
How to set this up across your wikis
The practical workflow looks like this.
1. Connect the tools your docs live in
Sidenote reads documents in place, read-only, so nothing gets uploaded or copied out:
- Confluence — works directly on your wiki pages. See Sidenote for Confluence.
- Notion — read and question any page or database doc. See Sidenote for Notion.
- SharePoint & OneDrive — read-only via Microsoft Graph, so it respects existing access. See Sidenote for SharePoint.
- Google Docs, PDFs (including scanned, via OCR), and any web page — handled the same way.
2. Build a Collection per question, not per tool
Don't try to model your whole knowledge base. Group documents by the question you keep needing to answer: an "Onboarding" collection, a "Security & compliance" collection, a "Pricing & contracts" collection. Pull in the relevant docs regardless of which tool each one lives in — that's what lets you search across company documents that would otherwise be stranded in separate silos.
3. Ask, then verify with one click
Ask your question in plain language. Read the answer, then click each citation to land on the exact source passage. If two documents disagree, you'll see both cited — which is usually the fastest way to discover that one of them is out of date.
Which approach should you choose?
| Enterprise search platform | In-browser cited reading layer | |
|---|---|---|
| Best for | Large orgs needing one central index | Individuals and teams needing answers now |
| Setup | Connectors, IT ownership, budget | Install extension, group docs |
| Output | Ranked list of links to read | An answer with a citation on every claim |
| Verify the source | Open each result manually | One click scrolls to the exact passage |
| Reads docs in place | Indexes a central copy | Yes — read-only, no upload |
They're not mutually exclusive. Plenty of teams run enterprise search for org-wide discovery and use a cited reading layer for the day-to-day work of actually getting an answer out of the documents they find.
Frequently asked questions
Do I have to upload or export my documents to search across them?
No. Sidenote reads documents where they already live — Confluence, Notion, SharePoint and OneDrive (read-only via Microsoft Graph), Google Docs, PDFs and web pages — so nothing is exported or copied into a separate system. That keeps your existing permissions intact and means there's no second copy to keep in sync.
Can I ask one question across documents from different tools at once?
Yes — that's what Collections are for. You group the relevant documents into one Collection regardless of which tool each lives in, then chat across the whole set. The answer draws on all of them and cites the exact passage behind each claim, so a single question can span a Confluence page, a Notion doc and a SharePoint policy together.
How do I know the answer is actually in our documents and not made up?
Every claim carries a citation that scrolls to and highlights the source sentence, so you can verify it in one click. Just as important, any claim that can't be matched back to a real passage is dropped server-side before you see it — so you get either a cited, checkable answer or an honest "the documents don't say," not a confident guess.