Langdock vs Glean: Enterprise AI Assistants Compared (2025)
Langdock offers multi-model AI chat for European teams with EU data residency. Glean offers enterprise-wide AI search across 100+ connectors. Both help employees find information. Neither completes workflows. Here's an honest comparison.
Langdock and Glean both let employees ask questions against enterprise knowledge using AI — but they are built on different bets. Langdock bets on multi-model flexibility and European data sovereignty. Glean bets on breadth of enterprise search across 100+ connected systems. Understanding which bet matters for your organization is the real comparison.
Side-by-side comparison
| Dimension | Langdock | Glean | Nexus |
|---|---|---|---|
| Core job | Multi-model AI chat with company knowledge | Enterprise-wide AI search and answers | Autonomous workflow completion across systems |
| How employees use it | Open chat, choose an LLM, ask questions or draft content | Search bar across all company data, get AI-generated answers | Agents work inside existing channels (Slack, Teams, WhatsApp, email). Employees interact naturally or agents operate autonomously |
| Knowledge sources | 40+ integrations: Confluence, SharePoint, Google Drive, Notion, Microsoft 365 | 100+ connectors: Confluence, Slack, Drive, SharePoint, Salesforce, Jira, email, and more | 4,000+ integrations with full read/write access |
| Integration depth | Read-only: searches and retrieves | Read-only: indexes and retrieves | Read and write: agents collect, validate, decide, act, and escalate |
| Model flexibility | Multi-model: Claude, GPT-4o, Gemini, Llama, Mistral, DeepSeek | Proprietary search model + LLM layer | Model-agnostic: platform selects optimal model per task |
| Completes workflows? | No. Surfaces information for the employee to act on | No. Provides answers for the employee to act on | Yes. Agents execute end-to-end across systems |
| Handles exceptions? | No. Employee interprets and decides | No. Employee interprets and decides | Yes. Agents adapt to edge cases and escalate with full context |
| Data residency | EU-hosted in Frankfurt, Germany. ISO 27001, SOC 2 Type II. GDPR-first by design | US-based, SOC 2, enterprise security controls | SOC 2, ISO 27001, ISO 42001, GDPR. Deployed at regulated European enterprises |
| Deployment | SaaS. Connect sources, invite users, start chatting | SaaS. Connect sources, index data, deploy search | Platform + service. FDEs embed with your team, build agents, ensure production adoption |
| Pricing | Per-user (starting ~€25/mo, per Langdock pricing) | Per-user (starting ~$50/mo, minimum ~$60K/year, per Eesel research) | Per-agent, tied to value delivered |
| Best for | European teams wanting governed multi-model access to knowledge | Large organizations with information scattered across many tools | Enterprises needing AI that completes work, not just finds information |
Where Langdock wins
Multi-model flexibility. This is Langdock's genuine differentiator. Employees choose which LLM to use for each task — Claude for analysis, GPT-4o for drafting, Mistral for quick questions. Most enterprise AI tools lock you into one model provider. Langdock gives teams real choice, and that choice matters because different models have measurably different strengths on different task types.
European data residency. If your organization requires AI infrastructure hosted entirely in the EU with strict data sovereignty guarantees, Langdock was built for this. All data processing occurs in Frankfurt, Germany. The architecture is GDPR-first by design — not adapted from a US-first stack. Langdock holds ISO 27001 and SOC 2 Type II certifications. For regulated European enterprises, this is a meaningful structural difference from US-headquartered alternatives.
Content generation. Langdock's chat interface is better suited for interactive content creation: drafting emails, writing reports, generating summaries with a chosen model. If the primary job is "use AI to create content based on company knowledge," the conversational interface and model flexibility make Langdock the stronger option.
Speed of deployment for its use case. Connect knowledge sources, configure governance, invite users. Days, not weeks. Langdock reports 60,000+ monthly active users and notable deployments including Merck (23,000+ employees) and GetYourGuide (70% workforce adoption). For organizations that want employees using AI quickly, Langdock is fast to deploy and low-friction to adopt.
Where Glean wins
Breadth of enterprise search. This is Glean's genuine differentiator. 100+ connectors mean Glean indexes practically everything your organization uses: Confluence, Slack, Google Drive, SharePoint, Salesforce, Jira, Zendesk, GitHub, email, and more. No other tool on this list searches across that many sources with that depth. Langdock, by contrast, supports 40+ integrations and focuses more on the chat and content-generation experience than exhaustive search coverage.
Search quality at scale. Glean's search isn't keyword matching with an LLM layer on top. It understands organizational context, user permissions, document relevance, and relationships between content sources. For enterprises with thousands of employees and decades of accumulated documentation scattered across dozens of tools, Glean's search quality is meaningfully better than what any general-purpose assistant can match.
Knowledge synthesis from multiple sources. When an employee asks a question, Glean can synthesize an answer drawing from a Confluence page, a Slack conversation, and a Salesforce record simultaneously. That cross-source synthesis is something single-knowledge-base tools can't replicate.
Adoption through integration. Glean embeds into the tools employees already use — browser extension, Slack, Teams — rather than requiring them to open a separate interface. For driving adoption, meeting employees where they already work is a real advantage. Glean has also expanded beyond search into a broader agentic layer, positioning itself as connective tissue between enterprise systems and AI models (TechCrunch, February 2026).
Vendor stability. Glean has raised over $260M in funding and was valued at $7 billion as of mid-2025, making it one of the best-capitalized players in enterprise AI search. For procurement teams assessing long-term vendor viability, this is a relevant data point.
Where both fall short
This is the section that matters most for enterprises evaluating either tool.
Both are read-only. Langdock retrieves information from your knowledge sources. Glean indexes your systems and retrieves information. Neither writes to your systems. Neither creates a ticket, updates a CRM record, processes a request, or triggers an action. The AI's scope ends at "here is the information you asked for." The actual work — validation, decision, execution, exception handling — remains the employee's job.
Both depend on the employee to execute. This is the structural ceiling of the AI assistant category. The AI surfaces the answer. The employee validates it, makes the decision, takes the action, handles the exception, and moves to the next step. For simple knowledge lookups, that's fine. For complex business processes spanning multiple systems and requiring dozens of decisions, the assistant saves minutes on the search step while hours of manual work remain unchanged.
Neither handles exceptions. Enterprise processes are messy. Data doesn't match expectations. Edge cases appear constantly. Policies conflict. When that happens, both Langdock and Glean give the employee information to work with. Neither can navigate the exception itself.
Adoption plateaus after the initial spike. This is the pattern enterprises consistently report across AI assistants. Usage spikes in the first weeks when novelty is high, then plateaus or declines by month three. The AI proved helpful for quick lookups and drafting, but it didn't change how core business processes work. The high-volume, high-stakes work that drives revenue and costs stayed manual. This isn't a failure of either product specifically — it's a structural limitation of the assistant category.
Langdock and Glean alternative for workflow completion
If the gap between "finding information" and "completing the process" is where your organization loses time, money, and quality, neither Langdock nor Glean closes it. Both make the "find" step faster. Neither touches the "validate, decide, act, handle exceptions, and complete" steps.
Nexus is a different category. It doesn't compete with Langdock or Glean on knowledge retrieval. It deploys autonomous agents that use enterprise knowledge to complete entire workflows end-to-end.
What that looks like in practice:
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Orange Group (multi-billion euro telecom, 120,000+ employees): Agents don't just find product information. They use it to complete customer onboarding across CRM, ERP, and WhatsApp. Business team deployed in 4 weeks. 50% conversion improvement. 90% autonomous resolution. 100% team adoption. They previously used a CX chatbot with a 27% drop-out rate.
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European telecom (13,000+ employees): Knowledge access wasn't the problem — acting on it at scale was. Spent 6 months with Copilot Studio, couldn't deliver a single production use case. Deployed a dozen Nexus agents. 40% of support volume freed across millions of interactions.
Key structural differences:
- Nexus connects to 4,000+ enterprise systems with full read and write access (not read-only retrieval)
- Agents make decisions within guardrails and handle exceptions intelligently
- Agents escalate with full context when uncertain — no silent failures
- Business teams build and own agents without an engineering dependency
- Forward Deployed Engineers embed with your team to ensure agents reach production and adoption
- Per-agent pricing tied to value delivered, not per-seat licensing
- 100% POC-to-contract conversion rate — every pilot converts because FDEs ensure results before the contract conversation starts
How to decide
Choose Langdock if:
- European data residency is your top requirement
- You want employees to use multiple LLMs with company context
- The primary job is knowledge access, content drafting, and AI-assisted productivity
- You want fast deployment with low IT overhead
- Your budget is structured around per-seat licensing
Choose Glean if:
- Information is scattered across dozens of tools and employees can't find things
- Enterprise-wide search quality is the primary bottleneck
- You need AI answers that synthesize from multiple sources
- You need deep connector coverage across 100+ systems
- Vendor scale and funding stability matter for your procurement process
Choose Nexus if:
- Your AI assistant adoption has plateaued or declined after the initial spike
- The bottleneck isn't finding information — it's the multi-step process that follows
- You need AI that completes workflows across CRM, ERP, ticketing, WhatsApp, email
- You want business teams to own the AI, not depend on IT or engineering
- You want pricing tied to outcomes, not headcount
The gap between a knowledge assistant and an autonomous agent isn't a feature gap. It's a category gap. Langdock and Glean are good at what they do. The question is whether what they do is what your organization actually needs.
Frequently asked questions
What is the difference between Langdock and Glean? Langdock is an AI assistant platform built primarily for European enterprises. It gives employees access to multiple LLMs (Claude, GPT-4o, Gemini, Mistral, Llama) through a governed chat interface connected to company knowledge sources. Glean is an enterprise search platform that indexes 100+ data sources — Confluence, Slack, Salesforce, Jira, email, and more — and lets employees find information through natural language queries. Langdock emphasizes multi-model flexibility and EU data residency. Glean emphasizes breadth of search coverage and cross-source synthesis.
Is Langdock GDPR compliant? Yes. Langdock is engineered specifically for GDPR compliance. All data processing occurs in Frankfurt, Germany (EU). Langdock holds ISO 27001 and SOC 2 Type II certifications and offers configurable data retention policies, audit logging, and network-level controls. For European enterprises where data sovereignty is a hard requirement, Langdock's architecture is GDPR-first rather than GDPR-adapted.
How many connectors does Glean support? Glean supports 100+ enterprise connectors, covering tools including Confluence, Slack, Google Drive, SharePoint, Salesforce, Jira, Zendesk, GitHub, and email systems. This breadth of integration coverage is Glean's primary differentiator — it can index practically every data source a large enterprise uses.
Which is better for European enterprises: Langdock or Glean? For European enterprises where EU data residency is a hard requirement, Langdock is the better choice — it processes all data in Frankfurt, Germany and is built GDPR-first. Glean is US-based. If search breadth across many systems matters more than data residency, Glean's connector coverage may be the stronger fit. If neither knowledge retrieval nor data residency is the primary problem — and completing actual workflows is — neither tool directly addresses that need.
Does Glean integrate with Slack and Microsoft Teams? Yes. Glean integrates with both Slack and Microsoft Teams, both as a data source it indexes and as a surface where employees can access Glean's search and assistant features directly. This in-workflow access is a meaningful adoption advantage — employees don't need to leave the tools they already use.
Worth exploring?
If your AI assistant hasn't delivered the business process transformation your team expected, it's likely not a training problem or a configuration problem. It's a category problem.
Every Nexus engagement starts with a 3-month proof of concept tied to measurable outcomes. Forward Deployed Engineers embed with your team from day one. You see the results before committing. You can exit anytime.
100% of clients who started a POC converted to an annual contract. Every one.
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