Glean vs Guru vs Dashworks vs Notion AI (2026)

The short answer: Glean, Guru, Dashworks, and Notion AI differ mainly in where they get their knowledge. Glean builds an enterprise-wide search index, Guru answers from verified wiki cards your team writes, Dashworks reads across connected apps in real time, and Notion AI works well inside Notion but not across your other tools. The right pick depends on your team size, budget, and how much upkeep you can sustain.

Three approaches: enterprise index, wiki-first, and connected assistant

Every AI knowledge tool answers one question: where does the answer live, and how does the tool reach it? There are three broad approaches, and understanding them makes any comparison far easier.

• Enterprise index (Glean): crawls and indexes your stack into a central search graph, then answers over that index. Powerful at scale, but heavier to deploy.
• Wiki-first (Guru, Notion AI): answers come from curated, human-written content. Accurate when maintained, stale when neglected.
• Connected assistant (Dashworks, and flat-priced tools like Holka): reads live across the tools you already use at query time, then answers in plain English with source attribution.

None is universally "best." The trade-off is coverage versus maintenance versus cost. Below, each tool gets a fair look before we get into criteria and team-size fit.

Glean: enterprise-scale search with quote-based pricing

Glean is a common reference point for enterprise search and a frequent starting place for anyone hunting enterprise search alternatives. It indexes content across your apps, layers permissions-aware search on top, and adds an assistant that can reason over the index. For large organizations with dedicated IT, it is a capable platform.

The friction tends to show up in two places. First, enterprise pricing is often quote-based rather than published, so smaller teams struggle to estimate cost. Second, an index-everything model generally needs setup, tuning, and ongoing administration that assumes you have people to run it. For a 30-person company, that can be more platform than you need. A fuller breakdown lives in our Glean comparison.

Best for: large enterprises with IT resources and a genuine search-at-scale problem.

Guru: verified wiki cards you maintain

Guru takes the opposite tack. Instead of indexing everything, it centers on verified cards, short, human-authored knowledge snippets with an owner and a verification date. When a card is marked verified, readers have a signal that it is current. It is a clean model for support and onboarding, where a canonical answer matters more than breadth.

The cost is maintenance. Cards stay trustworthy only if someone keeps writing and re-verifying them, and knowledge that lives in Slack threads, tickets, or deal notes never becomes a card unless a person copies it over. So Guru reflects what you have documented, not necessarily what is happening across your tools right now. Teams evaluating a Guru alternative are usually reacting to that upkeep burden, or to the gap between verified cards and live reality. See the Guru comparison for detail.

Best for: support and onboarding teams that can commit to card upkeep.

Dashworks: real-time cross-tool reads for mid-market

Dashworks sits closer to the connected-assistant model. Rather than forcing everything into an index or a wiki, it reads across connected apps at query time and returns an answer with links back to the source. That live approach reflects what is in your tools today without a curation step, which is part of why it appeals to mid-market teams without an IT department to run a heavy index.

The main things to weigh are the usual ones for this category: how deep each integration goes, how pricing scales as you add people, and how tightly it fits your particular stack. If a real-time connected assistant is the model you want, it is worth comparing options carefully, our Dashworks comparison walks through the differences and where a Dashworks alternative might fit better on price or coverage.

Best for: mid-market teams wanting live answers without heavy setup.

Notion AI: strong inside Notion, limited across your stack

Notion AI is good at what it does: summarizing, drafting, and answering questions over content that already lives in Notion. If your company runs on Notion, it feels like a natural extension of the workspace and needs no new setup.

The limitation is one of scope. Notion AI is centered on your Notion workspace; it has added a handful of connectors (to tools like Slack, Google Drive, and GitHub), but it isn't built to read broadly across your whole stack, Gmail, your CRM, Stripe and the rest, the way a dedicated company brain is. So a question that spans many tools, "what did we promise this customer, and did they pay?", quickly runs past what it's designed for. It leans toward being a document assistant rather than a cross-tool company brain. The Notion AI comparison covers where that boundary bites.

Best for: Notion-centric teams that keep most knowledge in one workspace.

An honest criteria checklist: setup, price, security, coverage, citations

When you strip away the marketing, an AI knowledge tool comparison comes down to five practical questions. Score each contender on all five rather than on a single headline feature.

Citations deserve emphasis. An assistant that answers confidently without showing its sources is a liability, because you cannot tell a real answer from a plausible guess. Source attribution, naming the connected tool behind each fact, is what makes an answer checkable. Coverage matters for a similar reason: the best AI company assistant is the one that can see the tool where the answer actually lives, whether that is Slack, Gmail, or your CRM.

Where each tool fits by team size

• Large enterprise (500+): Glean's index model can earn its complexity, and you likely have IT to run it.
• Support-heavy orgs: Guru's verified cards work well if you can staff the upkeep.
• Notion-native teams: Notion AI is the easy, no-setup option, as long as your knowledge really is all in Notion.
• Mid-market and SMB (10-200): a connected assistant that reads live across tools usually gives the most coverage for the least setup and cost.

Where a flat-priced connected assistant fits

For many startups and mid-market teams, the sticking points above, quote-based enterprise pricing, wiki maintenance, single-tool blindness, are what push them to look for a Glean alternative that is simpler and cheaper to run. That is the gap a flat-priced connected assistant like Holka is built for.

The model is straightforward: connect the tools you already use, ask a question in plain English, and get an answer that reads across all of them at once with source attribution on every fact. It is read-only by default; any write, like drafting an email or a Slack message, is surfaced as a draft that needs one-tap confirmation before it sends. Access tokens are encrypted at rest, and your data stays in your own tools rather than being bulk-copied into a new index. Pricing is flat, a free tier of 40 messages a month plus a single Pro plan at $29 a month for unlimited messages, with no seat minimums, which removes the per-seat math that makes enterprise tools hard to budget. You can browse the full integrations list or see how flat pricing compares against per-seat models.

The point is not that a connected assistant beats every tool on every axis. It is that for a team without an IT department, live cross-tool coverage plus honest citations plus a predictable price often matters more than raw index scale.

How to run a one-week evaluation

You do not need a quarter-long procurement cycle to decide. Run a focused, one-week test and let real questions settle it.

1. Day 1, Connect your five noisiest tools. Pick the apps where answers actually hide: email, chat, CRM, docs, billing.
2. Day 2, Write 10 real questions. Use ones you genuinely asked a colleague last month, spanning at least two tools each.
3. Days 3-4, Ask all contenders the same questions. Judge on whether the answer is correct and whether it shows its sources.
4. Day 5, Score setup and citations. Note which tools needed IT help and which cited every fact.
5. Weekend, Check the bill. Model the cost at your real headcount, one year out, including seat growth.

The tool that answers cross-tool questions correctly, cites its sources, and costs a predictable amount at your size is the right one, regardless of which category it belongs to. A comparison is only ever a shortcut; your own ten questions are the real test.