An honest read on how Clearskies and Glean differ — and how to pick based on where your revenue motion lives.
Glean indexes every doc, ticket, wiki, and message across your company. For finding 'how do we handle X' across engineering, support, HR, and product, Glean is the right shape.
If your company has Glean rolled out and your security team has approved it, leveraging the existing deployment is faster than introducing a new vendor.
Glean returns ranked documents. Clearskies returns a resolved customer story — identity-mapped, timeline-ordered, gap-detected — before your AI ever runs a query. Search-shape vs graph-shape.
Clearskies knows that sarah.kim@acme.com in email, Sarah Kim in Gong, and @sarah.kim in Slack are the same person on the same deal. Glean's search model doesn't natively resolve identity across systems.
Clearskies surfaces what's missing — a champion who went dark, a follow-up never sent. Search systems return what exists; they can't tell you what isn't there.
Clearskies ingests your sales methodology, stages, exit criteria, and team structure as first-class context. Then the system observes activity against it — calls, emails, CRM updates, deal outcomes — and refines the process model automatically. Glean indexes a process doc if you've written one; it can't reason about the process or improve it. With Clearskies, every interaction sharpens the model every workflow runs on.
Clearskies updates Salesforce and HubSpot fields from AI-prepared briefs. Glean is read-only enterprise search by design.
Glean is per-seat. Clearskies is usage-based with no per-user license fees — so you can roll AI across the whole revenue team without per-seat math.
Both connect to Salesforce, Gong, Slack, email. The shape of what they do with that data is different (see above) but coverage overlap is real.
| Clearskies | Glean | |
|---|---|---|
| Primary shape | Resolved customer context graph | Enterprise search index |
| Identity resolution across systems | First-class — sarah.kim@acme.com / Sarah Kim / @sarah.kim resolved automatically | Approximate / search-based |
| Timeline construction | Every interaction ordered chronologically per account, deal, and person | Per-doc; cross-source timelines not native |
| Gap detection | First-class — surfaces what's missing across systems | Not supported (search returns what exists) |
| Sales process modeling | Ingests methodology, stages, exit criteria, and team structure — and refines the model automatically from observed activity | Indexes process docs if you've written them down; no reasoning or refinement |
| Cross-system queries | One resolved answer pulling from CRM, calls, email, and Slack | Returns ranked results across sources; user synthesizes |
| Write-back to CRM | Yes — Salesforce, HubSpot custom fields | Read-only |
| Model-agnostic | Claude, ChatGPT, Gemini, any AI via MCP or API | Glean Chat primary; some API access |
| Coverage outside revenue ops | Optional — focused on revenue | Strong — engineering, support, HR, product, all of it |
| Already in your stack | New connection | If already deployed, no new procurement |
| Pricing model | Usage-based, no per-user fees | Per-seat |
| Time to value | Minutes for connection; days for first workflow | Weeks (enterprise rollout typical) |
“I have Clearskies MCP connected to Claude which also connects into Amplitude, M365, and Atlassian. I'm running daily and weekly CEO Briefs on sales, competition, product sentiment, customer success, and customer health. I'm getting a level of insight that would have been impossible or massively labor intensive before.”
— Jake Olsen, CEO at Stratus
Comparing other AI tools too? See every comparison. Or visit Glean.
Same data, both tools, one workflow you don’t fully trust today. See the difference on something real.