Nexus vs Glean: Enterprise Knowledge Layer vs Enterprise Agents

Quick honest summary

Glean is an enterprise AI platform built around the knowledge layer. It connects to your company's tools, indexes the data, and helps employees find answers faster using natural language. It does this well, and for companies where the biggest problem is scattered information, Glean is a strong choice. Glean is now adding agent capabilities on top of that knowledge layer, but the architecture is fundamentally designed around search, knowledge, and content, not around completing deep business processes. The depth of automation it can handle is bounded by that foundation.

Nexus is a different category. It is an enterprise AI solution (platform plus service) where autonomous agents go beyond the knowledge layer entirely. They complete entire business workflows end-to-end: sales research, customer onboarding, support triage, compliance monitoring. Nexus agents do not just find information. They collect it, validate it across systems, make decisions within guardrails, escalate when uncertain, and execute actions. They combine information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration. They do not just find the answer. They complete the work the answer points to. And Nexus comes with Forward Deployed Engineers embedded in your team to ensure agents deliver measurable outcomes, not just technology.

The core question: is your bottleneck finding information, or completing the work that information points to?

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Side-by-side comparison

Dimension	Glean	Nexus
Core function	Knowledge-layer platform: finds, summarizes, and surfaces information Works across company systems Adding agent actions on top of search foundation	Autonomous AI agents that go beyond the knowledge layer Complete entire workflows end-to-end across enterprise systems Backed by embedded Forward Deployed Engineers
Category	Knowledge-layer platform, adding agents Automation depth bounded by search-first architecture	Agent-first enterprise AI solution Platform + service Built for deep process execution from day one
Who builds and owns it	IT deploys the platform Employees use it to search and ask questions	Business teams build and deploy agents Supported by Forward Deployed Engineers No engineering dependency
How it handles complexity	Surfaces relevant information for the employee to act on Adding agent actions on top of knowledge layer 100+ native actions across connected apps Depth of process automation bounded by search-first design	Agents adapt intelligently to exceptions Escalate with full context when uncertain Built for multi-step, multi-system process execution Includes decision logic, not just information retrieval
Completes work autonomously?	Primarily finds and presents information Agent capabilities are emerging but built on knowledge-layer architecture Actions across supported apps	Agents execute, validate, route, decide, and escalate independently Goes beyond finding answers to completing the work answers point to Core architecture, not bolted onto a search foundation
Deployment model	SaaS platform IT connects data sources for indexing Weeks to months for full indexing	3-month POC tied to measurable outcomes Forward Deployed Engineers embedded with your team Production agents in days to weeks
Integrations	100+ enterprise connectors for indexing and search Google Workspace, Slack, Confluence, Salesforce, and others 100+ native agent actions	4,000+ integrations across CRMs, ERPs, communication tools, and custom APIs Deploy across Slack, Teams, WhatsApp, email, phone, and web
Pricing model	Per-seat licensing $50/user/month starting price Minimum enterprise contracts ($50K-60K/year) Generative AI add-on ~$15/user/month extra Mandatory 10% support fee on ARR	Per-agent pricing tied to value delivered Not tied to headcount 3-month POC with measurable outcomes first Annual commitment after POC
Security and compliance	SOC 2 Type II Data governance controls Granular permissions	SOC 2 Type II, ISO 27001, ISO 42001, GDPR Full audit trails Decision traceability Role-based access
Support model	Standard enterprise SaaS support Requires 1-2 FTEs internally Manage connectors, permissions, and index sync Self-serve model suited to knowledge-layer tools	Forward Deployed Engineers embedded in your organization Change management guidance Ongoing optimization 100% POC-to-contract conversion rate Required for going beyond the knowledge layer
Best for	Companies where the bottleneck is finding information Information scattered across tools Need a knowledge layer, not a process execution layer	Enterprises where the bottleneck is completing work, not finding information High-volume workflows that cross multiple systems Measurable financial outcomes with FDE support

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When Glean is the better choice

Being honest about this matters. Glean is the right fit in several scenarios, all of which share a common thread: the bottleneck is at the knowledge layer, not the execution layer.

• Your biggest problem is finding information, not acting on it. If employees waste hours every week searching through Slack, Confluence, Google Drive, and SharePoint for answers that already exist somewhere, Glean solves that problem well. It indexes your company's knowledge and makes it searchable with natural language. That is genuinely valuable. If the work gets done once people have the right information, a knowledge-layer tool is the right investment.

• You need a company-wide knowledge layer. Glean connects to your existing tools and starts indexing. If the priority is giving every employee a single place to search across all company systems, Glean provides that without requiring workflow design or agent configuration. At $200M+ ARR and customers spanning over 27 countries, they have validated this use case at scale. Glean does the knowledge layer well.

• Your team needs better answers to internal questions. "What is our return policy for enterprise customers?" "Where is the Q3 competitive analysis?" "What did the engineering team decide about the API migration?" If these are the questions your team asks daily, Glean's AI-powered search and assistant handles them well.

• You want to improve how your company surfaces institutional knowledge. Companies with significant tribal knowledge, where critical information lives in people's heads or buried in old Slack threads, benefit from Glean's ability to index, rank, and surface that knowledge to anyone who needs it.

• You are primarily looking for a self-serve AI assistant. Glean's third-generation assistant now reasons through multi-step challenges, orchestrates sub-agents, and supports real-time voice interaction. If you want every employee to have a capable AI assistant for daily tasks, Glean's per-seat model is designed for that. This is the knowledge-layer paradigm at its strongest: making every individual more effective at finding and using information.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they already have access to information. The problem is that finding it does not complete the work. Knowing the answer and executing on it are fundamentally different challenges.

This is where the knowledge-layer architecture hits its ceiling. Enterprise AI platforms built around search, knowledge, and content excel at surfacing information and answering questions. Some are adding agent capabilities on top, but the depth of automation they can handle is bounded by that foundation. When work requires orchestrating across multiple systems, handling exceptions intelligently, and executing multi-step workflows autonomously, you need something that goes beyond the knowledge layer: agents that combine information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration. Finding the right information is valuable, but it is only the first step of most enterprise workflows.

• You need AI that completes business processes, not just finds information about them. Sales research across thousands of accounts, customer onboarding across multiple countries, support triage with compliance requirements. These are multi-step workflows that require collecting data, validating it across systems, making decisions, handling exceptions, and taking action. A knowledge-layer tool surfaces the inputs. Nexus agents complete the entire workflow. This is the same structural gap that separates AI assistants like Copilot from autonomous agents. That is the difference between the knowledge layer and the execution layer.

• Your team already knows where the information is. They need help acting on it at scale. Lambda's sales team did not struggle to find account information. They struggled to analyze 12,000+ enterprise accounts at the depth required to identify buying signals and competitive movements. The bottleneck was not search. It was the 2 hours of research and synthesis required per account, multiplied across thousands. Nexus agents handled the entire research workflow autonomously.

• You want AI that works across systems, not just reads from them. Glean indexes your tools for search and is adding write actions across 100+ connected apps. But there is a structural difference between a knowledge-layer platform that adds actions and an agent platform built for deep process execution. Nexus agents operate bidirectionally across 4,000+ enterprise systems: pulling data from CRMs, validating against ERPs, communicating via WhatsApp or email, updating ticketing systems. When Orange onboards a customer, the agent collects information, validates it against multiple backend systems, checks compatibility, routes unusual cases, and escalates complex issues. That is not a search problem with actions bolted on. It is autonomous workflow execution that goes well beyond what knowledge-layer architecture supports.

• You need more than software. You need a partner. Nexus is not a platform you deploy and figure out on your own. Forward Deployed Engineers (real engineers, not support reps) embed with your team to identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, and run pilots without consuming your internal resources. This is why Nexus converts 100% of POCs to annual contracts. Deploying enterprise AI is 10% technology and 90% organizational change.

• Business teams need to own workflows without engineering dependency. Lambda's Head of Sales Intelligence, Joaquin Paz, has no engineering background. He built their research agent himself. At Orange, business teams built and deployed customer onboarding agents in 4 weeks. This is not about "no-code" as a feature. It is about business teams owning the outcome, with engineers embedded to support them. For a deeper look at that decision, see our build vs buy analysis.

• Per-seat pricing does not match your use case. Glean's per-seat model means every employee who might search needs a license, with costs adding up quickly across large organizations ($50+/user/month before add-ons). Nexus charges per agent. An agent that handles research across 12,000 accounts or onboards thousands of customers costs the same regardless of how many employees are in your organization.

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What enterprises experienced

Lambda: going beyond the knowledge layer

Lambda is a $4B+ AI infrastructure company with world-class AI engineers. If any company could build sales automation internally, it is Lambda. AI is literally their business.

Their challenge: monitoring 12,000+ enterprise accounts for buying signals, competitive movements, and market intelligence. The problem was not finding information -- a search-based tool like Glean could surface individual data points. It was synthesizing those data points at scale and turning them into actionable intelligence. No knowledge-layer platform, however sophisticated its enterprise graph, could execute the entire research process autonomously. What Lambda needed was an agent that could.

They tried open-ended AI tools first. Too inconsistent; same question, different results every time. They looked at traditional automation and workflow tools like Zapier. Too rigid; heavy hard-coding, breaks when systems change.

Joaquin Paz, Head of Sales Intelligence (no engineering background), built an autonomous research agent using Nexus. The agent performs 2 hours of deep analysis per account across dozens of data sources, delivering structured intelligence to account executives.

The results:

• $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring
• 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts)
• $7M+ projected annual value as they expand to a full agent fleet
• Agent adapts as Lambda adds data sources or changes segmentation, without requiring a rebuild

As Joaquin put it: "We looked at open-ended AI agents; they were smart but inconsistent. Same question, different answer every time. We looked at traditional automation; it was reliable but felt heavy, lots of hard coding. With Nexus, we got both: intelligent and consistent."

Lambda is now expanding beyond a single agent to build what they call an "agentic layer": a network of specialized agents across their entire go-to-market organization, including marketing operations, campaign management, and customer engagement.

Orange Group: autonomous workflow completion at enterprise scale

Orange is a multi-billion euro telecom operator with 120,000+ employees across Europe and Africa. They have significant internal engineering resources and could build anything they want.

They built autonomous customer onboarding agents using Nexus. The agent collects customer information in real-time, validates data against backend systems, checks compatibility, routes unusual cases to appropriate teams, and escalates complex issues with full context. All of this across multiple countries and languages, with full compliance and audit trails.

The results:

• Deployed in 4 weeks (business team built it, not engineering)
• 50% conversion improvement
• $4M+ incremental yearly revenue
• 100% team adoption because agents live inside channels teams already use
• Full governance: when the agent is confident, it proceeds; when uncertain, it escalates with context. Every step visible, every decision logged

The key: this is not a knowledge-layer problem. Orange did not need help finding customer data. They needed AI that could go beyond search and complete the onboarding workflow end-to-end, autonomously, at scale, with compliance built into every step. That required agents that combine information retrieval with deep process execution, not a knowledge tool with actions added on top.

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Key differences explained

The knowledge layer vs. the execution layer: different categories

This is the fundamental distinction, and it matters more than any feature comparison.

Enterprise AI platforms like Glean are essentially knowledge-layer tools and assistant builders. They connect to your company's systems, index the data, and help employees find answers faster. They excel at finding information, answering questions, and surfacing knowledge. The employee is still doing the work. The AI finds the information; the human decides what to do with it and executes. Glean is adding agent capabilities (100+ native actions, sub-agent orchestration, agent builder), but the architecture is fundamentally designed around search, knowledge, and content, not around completing deep business processes.

Nexus agents go beyond the knowledge layer. They combine information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration. They complete entire workflows independently: collecting information, validating against systems, making decisions within guardrails, handling exceptions, escalating when uncertain, and executing actions across multiple systems. The agent does not just find the answer. It completes the work the answer points to.

This is not a criticism of Glean. Enterprise search is a real problem, and Glean has built a strong business solving it ($200M+ ARR, $7.2B valuation). But there is a meaningful difference between making information accessible and making work get done. Conversational AI platforms face a similar boundary: they handle the dialogue but not the work behind it. The depth of automation that knowledge-layer platforms can handle is bounded by their search-first foundation. They can add agent actions, but the architecture was not designed for deep process execution, exception handling, or autonomous multi-system orchestration. If your bottleneck is "we cannot find things," Glean addresses that. If your bottleneck is "we know what needs to happen but cannot do it at scale," that is a different problem. It requires agents that operate beyond the knowledge layer.

Knowledge-layer architecture vs. agent-first architecture: why it matters

Glean started as enterprise search and is adding agent capabilities on top. Nexus was built agent-first from day one. This distinction shapes how each platform handles the hard parts of enterprise AI, and it is not a gap that closes easily.

Knowledge-layer platforms that add agents inherit the assumptions of the knowledge paradigm: the human is ultimately making decisions and the AI is providing inputs. The architecture was designed to find and surface information, not to execute deep business processes. Agent actions are an extension of search, not a rethinking of it. Agent-first architecture starts from the opposite premise: the agent completes the work, and humans step in for judgment calls. This leads to fundamentally different approaches to exception handling, multi-system orchestration, escalation logic, audit trails, and governance.

Glean's agent builder now supports 100+ native actions and MCP host support, which expands what agents can do within connected apps. But there is a structural difference between taking actions within apps and orchestrating complete workflows across 4,000+ systems with decision logic, exception handling, and intelligent escalation. The depth of automation a knowledge-layer platform can support is bounded by a foundation that was built to retrieve and present, not to execute and decide. Lambda's agent analyzes 12,000+ accounts annually across dozens of data sources, adapting as priorities shift. Orange's agent autonomously handles customer onboarding across multiple countries with full compliance. These require agent-first architecture where deep process execution is the core design principle, not a feature added to a search product.

Software vs. solution: the service layer difference

Knowledge-layer platforms are typically self-serve software. Glean is enterprise software. You purchase licenses, connect your data sources, and your team uses the platform. Glean recommends 1-2 dedicated FTEs internally to manage connectors, permissions, and index synchronization. That model works for a knowledge tool where the primary interaction is employees asking questions and getting answers.

Going beyond the knowledge layer requires more than software. Nexus is a solution: platform plus service. Forward Deployed Engineers embed with your team from day one. They help identify the highest-impact use cases (not guessing based on templates), design agents for your specific workflows, handle integration complexity, provide change management guidance, and continuously optimize performance. This is not premium support. It is an embedded engineering partnership.

This matters because deploying autonomous agents that execute deep business processes is 10% technology and 90% organizational change. The technology works. The hard part is identifying the right workflows, designing for edge cases, getting teams to trust the system, and building momentum from quick wins to enterprise-wide impact. Nexus includes that service layer by design because agents that complete work require a fundamentally different deployment approach than tools that surface information. Forward Deployed Engineers are not optional. They are what makes the difference between deploying a platform and delivering outcomes.

Knowledge-layer integrations vs. execution-layer integrations

Glean connects to 100+ enterprise apps primarily for indexing and search. Its agent capabilities now include 100+ native actions in connected applications, allowing agents to take action within those apps. These integrations serve the knowledge layer: they exist to pull information in for indexing and to take limited actions within connected apps.

Nexus integrations serve the execution layer. Agents operate across 4,000+ enterprise systems with deep bidirectional integration. The same agent can pull data from your CRM, validate it against your ERP, send a message via WhatsApp, update a ticket in your support system, and escalate to a human in Slack, all within a single workflow. Agents deploy across Slack, Teams, WhatsApp, email, phone, and web widgets. One agent, multiple channels, zero code changes.

This is the practical consequence of the knowledge-layer vs. agent-first distinction. Lambda's research agent does not just find information about enterprise accounts. It synthesizes data from dozens of sources, identifies patterns, scores buying signals, and delivers structured intelligence. That requires deep integration across many systems simultaneously, orchestrated by an agent that executes a complete process, not a knowledge tool that surfaces information with actions in a few connected apps.

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Frequently asked questions

Can I use both Glean and Nexus?

Yes, and some enterprises do. They operate at different layers. Glean serves the knowledge layer: helping employees find information across company systems, which is useful for day-to-day knowledge questions. Nexus operates at the execution layer: completing business processes end-to-end across systems autonomously. Glean for finding, Nexus for doing. They do not conflict because they address fundamentally different bottlenecks.

We already invested in Glean. Does Nexus replace it?

No. Nexus does not replace Glean's knowledge-layer capabilities. If employees benefit from faster access to company knowledge, Glean continues to serve that purpose. Nexus addresses a different gap: workflows that require AI to go beyond the knowledge layer and execute across systems, not just surface information. The question is whether finding information faster actually solves your bottleneck, or whether the real constraint is what happens after the information is found. Many enterprises discover they need both a knowledge layer and an execution layer.

Glean is adding agents. How is that different from Nexus?

Glean has made significant progress with agents. Their agent builder now supports 100+ native actions, MCP host support, Fast and Thinking modes, and sub-agent orchestration. These capabilities make Glean's agents useful for tasks that extend naturally from search and knowledge work.

But adding agent capabilities to a knowledge-layer platform is not the same as building an agent-first platform. The depth of automation a knowledge-layer architecture can handle is bounded by a foundation designed around search, knowledge, and content. Nexus was built agent-first, meaning autonomous execution, exception handling, deep process execution across 4,000+ integrations, and enterprise governance are core architecture, not extensions of a search product. Lambda's agent autonomously analyzes 12,000+ accounts across dozens of data sources. Orange's agents handle customer onboarding across multiple countries with full compliance. These workflows require agents that go beyond the knowledge layer: combining information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration.

The other difference is the service layer. Nexus includes Forward Deployed Engineers embedded with your team to ensure agents deliver measurable outcomes. Glean is self-serve software with standard enterprise support. Going beyond the knowledge layer requires that embedded partnership.

How does pricing compare?

Glean uses per-seat pricing starting around $50/user/month, with generative AI features as an additional ~$15/user/month add-on, and a mandatory 10% support fee. Minimum enterprise contracts typically start around $50K-60K/year, with Fortune 500 deals exceeding $5M annually. Cost scales with headcount.

Nexus uses per-agent pricing tied to value delivered. Lambda generates $7M+ in projected impact from agents that cost a fraction of what per-seat licensing across their organization would require. Every Nexus engagement starts with a 3-month proof of concept tied to specific measurable outcomes, so you see the ROI before committing to an annual contract. If the POC does not deliver, you walk away.

Glean has $200M+ ARR and $7.2B valuation. Why consider Nexus?

Glean has built an impressive business as a knowledge-layer platform. Their growth validates that enterprises need better ways to access information. But that validation also reveals the boundary: the knowledge layer is necessary but not sufficient for many enterprise workflows. Many enterprises that adopted Glean for search still have a separate, unsolved problem: high-volume workflows that require AI to go beyond finding answers and actually complete work across systems. Lambda and Orange are examples. Both could access information. Neither could execute on it at the scale they needed until they deployed Nexus agents with Forward Deployed Engineers guiding the implementation.

Our team mostly needs better internal search. Should we still consider Nexus?

If the primary problem is employees struggling to find information across company tools, Glean is likely the better fit. It is a strong knowledge-layer platform, and that is a real problem worth solving. Nexus is built for a different problem. The question worth asking: does finding information faster actually resolve your bottleneck, or does the real cost come from what happens after the information is found? If teams spend hours on research, synthesis, validation, and multi-step processes that cross systems, you need AI that goes beyond the knowledge layer. That is where Nexus agents and Forward Deployed Engineers deliver.

What about Glean's Enterprise Graph and enterprise context?

Glean's Enterprise Graph (combining memory, connectors, indexes, personal and enterprise graphs, and governance) is a strong foundation for understanding company context. It helps their assistant and agents reason about work with real understanding of organizational structure and knowledge. This is the knowledge layer at its most sophisticated: deep contextual understanding of information across the organization.

Nexus approaches context differently because agents that execute deep business processes need different things than tools that surface knowledge. Rather than building a separate knowledge graph, Nexus agents connect directly to 4,000+ systems and access live data during execution (real-time RAG), combined with vectorized knowledge bases for stable company knowledge. This means agents work with current data, not data that was last indexed. For workflows where data freshness matters (live CRM data, real-time system checks, current inventory), direct system access avoids the index sync delays that can introduce uncertainty. When an agent is making decisions and executing actions autonomously, it needs real-time data, not last-indexed data.

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Worth exploring?

If your team already has access to information but the bottleneck is completing the work that information points to, it might be worth seeing how enterprises are going beyond the knowledge layer. Lambda built agents that autonomously analyze 12,000+ accounts and identified $4B+ in pipeline. Orange achieved 50% conversion improvement and $4M+ yearly revenue with agents that complete customer onboarding end-to-end. Neither needed better search. Both needed agents that execute.

Every engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers embed with your team from day one. You see results before committing, and you can exit anytime.

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