Most teams evaluating tools to give AI company context end up looking at Glean and Gyld at some point. Both connect AI to your company's data. Both promise that employees stop wasting time hunting for information. But they are built on fundamentally different assumptions about what the problem actually is — and choosing the wrong one means either overpaying for features you'll never use or under-equipping the AI tools your team already relies on.
This guide breaks down Gyld vs Glean across the dimensions that actually matter: architecture, deployment, who controls what, and what happens when your engineers want to plug Claude or Cursor into your company's knowledge.
What each product actually does
Glean is an enterprise search and AI assistant platform. It indexes your company's apps — Google Drive, Slack, Confluence, Salesforce, and more than 100 others — builds a knowledge graph from that content, and surfaces answers through a chat interface. Employees ask questions; Glean searches across connected sources and returns an answer, typically inside Glean's own UI. As Glean describes it, the platform "inherently understands how you and your organization work" through deep indexing, activity signals, and people metadata.
Gyld is a business context layer for AI — a company brain that ingests data from your existing apps and exposes that knowledge as MCP servers (Model Context Protocol). Instead of routing employees into a new interface, Gyld makes your company's context available to the AI tools your team already uses: Claude, ChatGPT, Cursor, Codex, and any other agent that supports MCP. You choose what gets indexed, set permissions at the private / team / company level, and every answer comes with a source citation.
The clearest one-sentence distinction: Glean is an AI-powered search assistant your employees use. Gyld is a context layer that makes the AI tools your employees already use smarter.
Architecture: enterprise search platform vs. MCP context layer
Glean's architecture centers on a proprietary knowledge graph. It crawls connected apps, builds an index that incorporates activity signals and org-chart relationships, and serves that index through Glean's own search and chat UI. The model layer is flexible — Glean supports multiple LLMs — but the interface and the indexing pipeline are Glean's.
Gyld's architecture is different at the foundation. Rather than building a new interface, Gyld ingests your company's data from apps like Slack, Gmail, Outlook, Notion, Google Drive, HubSpot, Salesforce, and QuickBooks into a per-company knowledge base, then exposes that knowledge as MCP servers. Any AI agent that speaks MCP — which now includes Claude Code, ChatGPT, Cursor, and Codex — can query your company's context directly, without leaving the tool it's already in.
The MCP approach matters because it's where AI tooling is heading. Model Context Protocol is becoming the standard way for AI agents to reach external data. Gyld's bet is that the right place to surface company context is inside the agent, not in a separate search UI.
For a deeper look at how MCP fits into this picture, see MCP servers for business: turn your apps into AI context.
Deployment: who it's built for
Glean is built for large enterprises. The platform is sold through a sales process, typically deployed by IT or an internal AI team, and priced at a scale that reflects that. According to Read AI's comparison of enterprise AI tools, Glean and similar platforms are "expensive, slow to deploy, and built around a specific slice of the workday rather than the whole thing." Glean's own content positions it as a horizontal AI platform for enterprise teams — the kind of deployment that involves procurement, security reviews, and a rollout plan measured in months.
Gyld is built for founders, operators, and engineering teams who want to move faster. Setup is no-code: connect the apps you already use, choose what to index, set permissions, and your MCP server is live. There's no custom pipeline to build or maintain. The company controls exactly what context is indexed — nothing is ingested without explicit configuration.
This isn't a knock on Glean's approach. Enterprise-grade security reviews and a managed rollout are the right answer for a 5,000-person company with complex compliance requirements. But for a 20-person startup or a 200-person growth-stage company, that deployment model is friction you don't need.
Comparison: Gyld vs Glean at a glance
| Dimension | Gyld | Glean |
|---|---|---|
| Primary use case | Context layer for AI agents via MCP | Enterprise search and AI assistant |
| Interface | No new UI — works inside Claude, ChatGPT, Cursor, etc. | Glean's own chat and search UI |
| Deployment | Self-serve, no-code setup | Enterprise sales, IT-led rollout |
| Target customer | Startups, operators, engineering teams | Large enterprises |
| MCP support | Native — Gyld IS the MCP server | Not the core delivery mechanism |
| Permissions | Private / team / company-wide, configured by you | Inherits source-app permissions + org graph |
| Source citations | Yes, every answer | Yes |
| Model flexibility | Model-agnostic (works with any MCP-compatible agent) | Multi-model support inside Glean's platform |
| Pricing model | Accessible for smaller teams | Enterprise contract |
| Fine-tuning / RAG pipeline | No — managed context layer, no pipeline to maintain | No — proprietary knowledge graph |
Where Glean wins
Glean is the stronger choice when your primary goal is giving every employee a single place to search and ask questions across all company knowledge — and you have the IT resources and budget to deploy and manage it. Its knowledge graph, which incorporates activity signals and org-chart relationships, produces search results that are personalized in ways a simpler retrieval system can't match.
For large organizations with deep Atlassian, Salesforce, and Microsoft 365 footprints, Glean's 100+ native connectors and its enterprise security model (SOC 2, data residency options, permission inheritance from source apps) make it a credible choice. Glean's own positioning emphasizes its multi-model flexibility and enterprise-grade security as differentiators — and those claims hold up for the audience it's built for.
If you need a polished UI that non-technical employees can use without training, Glean delivers that. Gyld doesn't — it surfaces context inside tools like Claude or Cursor, which means it's most useful to people who are already using those tools.
Where Gyld wins
Gyld is the stronger choice when your team is already using AI agents — Claude, ChatGPT, Cursor, Codex — and you want those agents to understand your business without switching to a new tool or building a custom RAG pipeline.
The MCP delivery model is the key differentiator. When a developer asks Claude Code "what's our current API rate limit for the Acme integration?" and Claude has access to Gyld's MCP server, the answer comes from your actual Slack threads, Notion docs, and HubSpot notes — cited, permissioned, and current. That context lives in the agent the developer is already using, not in a separate search tab they have to remember to open.
Gyld also wins on control and speed. You decide what gets indexed. You set permissions. There's no sales process, no IT deployment project, and no RAG pipeline to build or maintain. For a technical team that moves fast, that matters.
For teams evaluating whether to build their own context pipeline instead, Gyld vs RAG walks through why a managed context layer is usually the faster path.
The agent-first question
The most important question to ask before choosing between these tools isn't "which has more connectors?" It's: where do your people actually work?
If your team's primary AI interaction is through a chat interface — asking questions, summarizing documents, drafting emails — Glean's UI-first model fits that workflow. Employees search in Glean the way they used to search in Google.
If your team's primary AI interaction is through agents — Claude running in a terminal, Cursor writing code, ChatGPT handling customer queries — then the context needs to be in the agent, not in a separate tool. That's what Gyld is built for.
As AI agent adoption grows, more workflows are shifting to the second category. Cassidy AI's comparison of enterprise AI tools frames this as the distinction between AI automation and enterprise search — tools that do things vs. tools that find things. Gyld sits firmly on the automation side of that line, because context delivered via MCP is context an agent can act on, not just retrieve.
How to make the decision
Use this as a quick filter:
Choose Glean if:
- You're a large enterprise (500+ employees) with an IT team to manage deployment
- Your primary goal is a universal search interface for all employees
- You need enterprise security certifications and compliance features out of the box
- Non-technical employees are your primary users
Choose Gyld if:
- Your team is already using Claude, ChatGPT, Cursor, or other MCP-compatible agents
- You want company context inside your existing AI tools, not in a new UI
- You're a startup or growth-stage company that needs to move fast without a procurement cycle
- You want to control exactly what gets indexed and how permissions work
- You don't want to build or maintain a RAG pipeline
For a side-by-side breakdown of specific features, see the Gyld vs Glean comparison page.
Key takeaways
- Glean is an enterprise search platform with its own UI; Gyld is a context layer that delivers company knowledge to AI agents via MCP
- Glean is built for large enterprises with IT-led deployments; Gyld is built for technical teams who want to move fast
- The right choice depends on where your team actually works: in a search interface, or inside AI agents like Claude and Cursor
If your team is already living in AI agents and you want those agents to understand your business, start building your company brain at Gyld.
Frequently asked questions
What is the main difference between Gyld and Glean?
Glean is an AI-powered enterprise search platform with its own chat and search UI. Gyld is a business context layer that exposes your company's knowledge as MCP servers, making it available inside AI agents like Claude, ChatGPT, and Cursor — without a new interface. Glean is for employees who want to search company knowledge; Gyld is for teams who want their existing AI agents to have that knowledge.
Does Glean support MCP?
Glean delivers enterprise context to agents, but its primary delivery mechanism is its own platform and UI, not MCP servers. Gyld is built around MCP as the core delivery model — your company's knowledge becomes an MCP server that any compatible agent can query directly.
Is Gyld cheaper than Glean?
Glean is priced for large enterprises and sold through a sales process. Gyld is accessible to smaller teams and can be set up without a sales cycle. Specific pricing for both products is available on their respective websites.
Can I use Gyld with Claude or Cursor?
Yes. Gyld exposes your company's knowledge as MCP servers, which Claude Code, Cursor, ChatGPT, and Codex can connect to directly. When you ask those agents a question, they can retrieve context from your Slack, Notion, HubSpot, Google Drive, and other connected apps — with source citations.
Do I need to build a RAG pipeline to use Gyld?
No. Gyld is a managed context layer — you connect your apps, configure what gets indexed, and set permissions. There's no RAG pipeline to build or maintain. That's one of the core differences from building your own solution, as explained in the Gyld vs RAG comparison.