Why We Didn't Build Another Glean

Every time we describe Skybase, someone asks if it's "Glean for small teams." It's a fair question — both deal with company knowledge, both plug into your tools, both involve an AI agent. But the comparison misses what Glean is actually built to do, and why that model doesn't transfer down to a 10-person startup no matter how much you shrink the price.

Glean is a search layer. It sits on top of an enterprise's existing knowledge — the Confluence spaces, the SharePoint sites, the Salesforce records, the years of Slack history — and makes all of it searchable in one place. That's an enormous, genuinely hard problem at enterprise scale, where the knowledge already exists, it's just scattered across forty systems with different permission models. Glean's job is retrieval: find the right document, fast, out of a mountain that already exists.

The problem small teams actually have

Small teams don't have that mountain. A 10-person startup doesn't have forty years of Confluence sprawl — it has a half-abandoned Notion, a few pinned Slack messages, and a founder's memory. The knowledge isn't buried in an enterprise-scale haystack. Most of it was never written down at all.

Search doesn't fix that. You can build the best retrieval engine in the world and it will return zero good results over a mountain that's mostly empty. The bottleneck for a small team isn't finding the right document — it's that the document was never created in the first place, because nobody had time to write it up after the call.

Why we built a maintainer, not a search index

That's the actual reason we didn't build another Glean. If the problem is "knowledge exists but is hard to find," you build a search layer. If the problem is "knowledge exists in raw form — a call, a thread, a ticket — and never gets turned into something reusable," you need something that does the turning, not just the finding. So Skybase starts one step earlier:

• It ingests the raw material directly — Slack threads, call notes, email, tickets — instead of assuming a clean document already exists to index.
• It structures that raw material into pages automatically, so the knowledge base actually grows instead of staying empty.
• It tracks source and freshness on every page, because a five-person team's biggest risk isn't "can't find the doc," it's "found a doc from eight months ago and trusted it."
• It answers questions directly instead of returning a ranked list of links, because a team that size doesn't have the headcount to sift through search results.

Enterprise search also comes with enterprise weight — permissioning models, admin consoles, a rollout that assumes an IT team is running it. None of that is wrong for a company with 5,000 employees. It's just the wrong shape for a company with 15.

What this means in practice

If your team already has a large, mostly-accurate knowledge base and the problem is genuinely "we can't find anything in it," a search tool is the right layer, and Glean is built for exactly that at scale. If your team's real problem is that the knowledge barely gets written down before it goes stale, search won't help — there's nothing to search yet. That's the gap Skybase is built for: not a better index of what you already have, but a system that builds the knowledge base from your raw activity in the first place, and keeps it honest after.

For a closer side-by-side on where the two tools actually diverge, see Skybase vs Glean.