Sistava vs Glean: Enterprise Search or AI Employee?
Comparison — — by Mahmoud Zalt
A practical comparison of Sistava and Glean for company knowledge, grounded answers, action execution, and workflow automation.
Why this comparison matters
A lot of buyers think they want a knowledge base when what they actually want is a reliable answer system. They need the assistant to understand company documents, respect permissions, and return something usable. The real question is whether they need a search product or an employee-like system that can turn that answer into work.
Glean is a very strong enterprise answer engine, and it has grown well past search into an assistant and an agent builder. Sistava starts from the other end: the thing you adopt is a working employee, and knowledge is one of the inputs it uses. Both stories now include automation, so the honest line between them is not "search versus action". It is what you have to build before anything happens.
This page walks through what each product actually ships today, who tends to buy which one, and the four or five questions that usually settle the decision inside a real evaluation.
What Glean actually gives you
Glean gives you a permissions-aware index of your company's tools, an assistant that answers from it, and an agent layer you build on top. Glean describes the platform in six parts: Glean Search, Glean Assistant, Glean Agents, Enterprise Context, Glean Protect, and an open platform layer with APIs, a web SDK, and headless MCP support.
The foundation is the connector layer. Glean's own connectors page states 275+ out-of-the-box connectors across enterprise apps, spanning documents (Confluence, Box, Dropbox, Coda, Airtable), engineering and analytics (GitHub, GitLab, Databricks, Azure DevOps), sales tools (Gong, Salesforce, Apollo), project management (Asana, ClickUp), HR (BambooHR, Deel), and support (Freshdesk, Freshservice). Permissions are inherited from the source system rather than reimplemented, and Glean states that permission changes are reflected in results as soon as they change upstream.
On top of that index sits the Enterprise Graph, which maps how people, content and interactions relate, plus what Glean calls enterprise memory that learns your processes. That is the part competitors find hardest to copy: a fresh, permissioned, relationship-aware picture of a large company.
- Glean Assistant. Answers from company tools, documents, conversations and the web. Glean lists content creation (decks, images, spreadsheets), analysis across data warehouses and BI tools, skills and actions for recurring work, real-time voice, and turning meetings into searchable summaries and next steps.
- Glean Agents. Build and test agents, trigger them from events, route tasks between agents, and connect to external systems. Glean also exposes monitoring for adoption, error rates, thumbs up and down, and return on investment.
- Glean Protect. Single-tenant cloud infrastructure, content safeguards, permission enforcement, and validation of agent actions.
- Open platform. Open APIs, a UI-ready web SDK, headless MCP support, multi-cloud support, and model choice.
One practical note for buyers: Glean does not publish prices. Its pricing page carries no tiers, per-seat numbers, minimums, or contract terms, so budget only becomes knowable after a sales conversation. That is normal for the enterprise segment it serves, and it is worth planning your evaluation timeline around.
What an AI Employee gives you
Sistava gives you a named employee with a role, a set of duties, a set of tools, and a schedule. You do not assemble a workflow first. You hire the role, brief it in plain English the way you would brief a new joiner, point it at your documents and accounts, and it starts producing work that you review.
Knowledge sits inside that employee rather than beside it. You upload documents and URLs, connect the accounts it needs, and the employee carries what it learned from one task into the next. Its memory is scoped to the job it owns, which is a narrower and more forgiving problem than indexing an entire corporation.
The other half is the approval boundary. Anything with real consequences, sending an external email, changing a record, spending money, can be held for your yes before it happens. That is what makes the employee framing safe rather than reckless: the employee proposes, you approve, and the audit trail keeps both halves.
Comparison
| Dimension | Traditional | With Sista |
|---|---|---|
| Primary product | Work AI platform: search, assistant, and agents over indexed company context. | AI workforce platform: employees that own a role, with knowledge and memory inside. |
| What you adopt | An index plus agents your team builds and maintains. | A hired role that starts producing work the same day. |
| Typical buyer | IT, enterprise search, and productivity teams at larger companies. | Founders and lean business teams that want work completed, not only searched. |
| Knowledge ingestion | 275+ out-of-the-box connectors with permissions inherited from the source system. | Docs, URLs, connected accounts, and shared knowledge across the workforce. |
| Answer output | Answers, analysis, generated documents, and agent runs you configure. | Answers plus the next step, drafted and queued for your approval. |
| Pricing visibility | Not published. Quoted through sales. | Public plans from $25 per month, self-serve. |
The dividing line is the unit of adoption
Both products can end a sentence with work getting done. The difference shows up in what has to exist first. With an agent builder, someone on your side defines the trigger, the steps, the tools, the failure path, and the owner. That person is usually your most capable operator, and their time is the scarcest thing you have.
With a hired employee, the definition is the job description. You say what the role covers and what good looks like, and the mapping from intent to steps happens on the platform's side. It is a lower ceiling in some ways and a far lower floor in others: less control over the exact path, far less setup before the first useful output.
That trade determines almost everything else in this comparison, including who inside your company ends up owning the tool.
Where Glean is the better fit
- You want enterprise search first. Glean is excellent when the top priority is finding answers across many workplace tools, and its connector breadth is genuinely hard to match.
- Your company is large and distributed. Inheriting permissions from every source system, and reflecting changes to them immediately, matters far more at four thousand people than at four.
- Security review is the long pole. Single-tenant infrastructure, content safeguards, and validation of agent actions are exactly the answers an enterprise security team asks for.
- You have engineers to build with. Open APIs, a web SDK, and headless MCP support pay off when you have a platform team that wants to embed the assistant in your own products.
- You do not want to rethink your operating model. Glean fits into the existing enterprise workflow instead of asking you to adopt an AI workforce concept.
Where Sistava is stronger
- You want answers to become actions without a build step. The employee already has a role, so "handle this" is the whole configuration.
- You want persistent employee-like memory. Layered memory scoped to one job means the assistant gets sharper at that job over time instead of broader and vaguer.
- You have nobody to run an internal AI program. There is no platform team to staff, no agent library to maintain, and no internal adoption campaign to run.
- You need a published price today. Plans are listed, you can start on your own card, and you can leave without a renewal conversation.
- You want cross-functional coverage from one place. The same platform supports support, sales, marketing, ops, and executive assistance.
None of that makes Glean the wrong choice. It makes it a different choice for a different company shape. A two hundred person company with a real IT function and a messy Confluence is a Glean story. A twelve person company where the founder is still writing the follow-up emails is not.
Pre-built teams are the fastest way to see the difference in practice. Each one arrives with the duties and tools for its function already attached, so the first useful output happens in the same session rather than after a configuration project. The table below is the pattern we see most often when buyers run both evaluations side by side.
| Buyer | Needs | Likely winner |
|---|---|---|
| Enterprise search team | Permissions-aware search and answers | Glean |
| Operations team | Knowledge that turns into execution | Sistava |
| IT/security team | Governed retrieval across many apps | Glean |
| RevOps / support | Answers plus workflow handoff | Sistava |
| Solo founder or lean team | Work completed without an internal AI program | Sistava |
| Leadership | Institutional knowledge that acts like a team member | Sistava |
Five questions that settle it on the demo
Vendor demos are optimised for their own strengths, so bring your own script. These five questions surface the real differences faster than any feature grid, and both vendors can answer all of them honestly.
- Who builds the first working automation, and how long does it take? Ask for a timeline in days and a named role on your side. If the answer needs a platform engineer you do not have, that is the answer.
- What happens when a permission changes upstream? Ask how quickly a revoked document stops appearing in answers, and whether that applies to agent runs as well as search.
- Where does an action stop for a human? Ask to see the approval step for an outbound email or a record change, not a slide describing it.
- What does month two look like without you? Ask what maintenance the system needs once the champion moves on to something else.
- What is the total cost at your real headcount? Ask for the number with the seats you would actually license, not the pilot group.
Migration checklist
- Map your knowledge sources — List every place your knowledge lives: docs, URLs, chat, drive, ticketing, and internal tools. Mark which ones hold something a customer or a regulator would care about.
- Test retrieval depth — Ask a question that requires synthesis across multiple sources and check how much context the answer really uses. Then ask the same question as someone with fewer permissions and confirm the answer changes.
- Test the next step — Decide whether you only need an answer or whether the answer should trigger follow-up work. Run one end-to-end task, not a query.
- Count the people it needs — Write down who configures it, who maintains it, and who reviews its output. If any of those names is missing from your org, prefer the option that does not need them.
- Choose the operating model — Use Glean if governed search across a large company is the whole job. Use Sistava if knowledge should live inside a workforce that produces the work.
Run that checklist against both products in the same week, with the same five questions and the same test corpus. Evaluations that stretch over a quarter lose their control group: the corpus changes, the champion changes, and the comparison quietly becomes two unrelated pilots. One week, one set of questions, one decision.
Sources
- Glean product overview, for the six platform components.
- Glean Assistant, for assistant capabilities and content generation.
- Glean AI Agents, for agent building, orchestration and monitoring.
- Glean connectors, for the connector count and permission inheritance.
FAQ
Is Glean just enterprise search?
No. Glean started as enterprise search and now describes a platform with search, an assistant, an agent builder, a security layer called Glean Protect, and open APIs. Treating it as search only will lead you to the wrong evaluation.
What is the main difference between Sistava and Glean?
The unit you adopt. Glean gives you a governed index of your company plus agents your team builds on top of it. Sistava gives you an employee that already owns a role, briefed in plain English, with knowledge and memory inside it.
How much does Glean cost?
Glean does not publish pricing. Its pricing page lists no tiers, per-seat numbers, minimums or contract terms, so you get a figure through a sales conversation. Sistava publishes its plans and you can start without talking to anyone.
How many tools can Glean connect to?
Glean's connectors page states more than 275 out-of-the-box connectors across enterprise apps, including Confluence, Salesforce, GitHub, Asana, BambooHR and Freshdesk. Permissions are inherited from each source system rather than managed separately.
Can an AI employee replace enterprise search for a small company?
For a small company, usually yes. Under roughly one hundred people, the knowledge problem is normally a handful of drives, a chat history and a help centre, which an employee can hold directly. Above that, a dedicated index earns its keep.
Can we run both?
Yes, and some teams do. Glean handles governed retrieval across the whole company while an AI employee owns a specific function like support or outbound. They overlap in the middle, so pick one to own the answer layer rather than letting both index everything.
Search ends at the answer. An employee starts where the answer stops. If your bottleneck is that nobody can find anything, buy the index. If your bottleneck is that the finding was never the hard part, and the work after it is what keeps slipping, hire the role instead. Most companies know which sentence describes them within about ten seconds, and that instinct is usually right.