Comparison
A Glean alternative for companies of 20–500 people
Glean is a genuinely good product built for genuinely large companies. If you have 8,000 employees, a dedicated IT function and a budget line for work AI, it is probably the right answer. This page is for the companies underneath that line.
The gap
Enterprise search and work-AI platforms are priced and sold per seat, with an annual commitment, a procurement cycle and an implementation. That model works when the buyer has thousands of seats to amortise it across. At 60 people it does not: the per-seat price is the same, the sales cycle costs you a quarter, and the connector surface you actually use is three systems, not ninety.
Meanwhile the need is sharper than it has ever been, because the thing asking questions is increasingly not a person. An agent reading your company's context needs the same two guarantees a person needs and enforces neither for itself: that what it reads is current, and that it is allowed to read it.
What Contextely does differently
Bought, not sold
Sign up, connect a source, run a query. No call, no pilot, no seat minimum, no annual commitment. Free for 500 retrievals a month and $49/mo after that.
Yours if you want it
The self-host path is a first-class option, not a lure. Run it on your own hardware with your own Postgres and nothing leaves your network except the condensation calls you configure.
Agent-first
An MCP server is the primary interface and the web app is secondary. Your agents are the users we designed for.
| Dimension | Enterprise work AI | Contextely |
|---|---|---|
| Buying motion | Sales-led, annual contract, seat minimum | Self-serve, monthly, free tier |
| Target size | Thousands of employees | 20–500 employees |
| Deployment | Vendor cloud | Hosted or self-hosted, same product |
| Connectors | Hundreds, most of which you do not use | The ones you point it at: Postgres/Supabase/Neon, and any MCP server |
| Permissions | Mirrors source-system ACLs across a large connector estate | Explicit entitlement scopes applied inside the retrieval scoring function |
| Freshness | Crawl and re-index on a schedule | Per-object TTL, re-fetched from the system of record at query time when it expires |
| Agent access | Increasingly available | The primary interface: MCP server plus REST mirror |
The row that matters
Permissions, as an actual screen
An enterprise platform mirrors your source systems' ACLs across a connector estate you mostly do not use. Contextely asks you to say, once and explicitly, what each member is entitled to, and then applies exactly that inside the retrieval scoring function.
It is less automatic. It is also the whole of the permission model, visible on one screen, which is the thing you can actually audit before you trust an agent with it.
People & scopes
Where Contextely is the wrong choice
If you need to search ninety SaaS systems with their native permission models mirrored faithfully, you need an enterprise connector estate and Contextely does not have one. If your problem is document search across a very large corpus rather than a small set of authoritative records, a search platform will serve you better. And if you have no system of record, if the knowledge genuinely only exists in people's heads, then no context layer fixes that.
Connect one system of record and run a query in five minutes.