Nexus vs Dust: Connecting Knowledge vs Completing Work
Dust connects company knowledge to LLMs for chat-based Q&A. Nexus agents complete workflows autonomously. Compare pricing, architecture, and real outcomes.
Last updated: February 2026
Quick honest summary
AI assistants and AI agents are not two points on the same spectrum. They are fundamentally different categories of technology, and understanding that distinction is the single most important thing on this page.
AI assistants are surface-level tools. They help with simple, single-user tasks: drafting emails, summarizing documents, answering questions from a knowledge base, searching across company data. That is genuinely useful work, and Dust does it well. But assistants cannot do deep, complex, autonomous work. They cannot orchestrate multi-step processes that span five different enterprise systems. They cannot collect data, validate it, make a decision, handle an exception, and take action, all without a human driving every step. They suggest; they do not complete.
Agents are a structurally different category. They combine conversational intelligence with process execution and autonomous decision-making. They do not assist a person doing the work; they are the entity completing the work. This same category distinction applies across AI assistants like Copilot and Langdock.
Dust is a well-designed AI assistant platform. It connects your company's knowledge (Notion, Slack, Google Drive, Confluence) to large language models and gives employees a smart chat interface to ask questions, draft content, and get AI-assisted answers. The team behind it (ex-OpenAI, ex-Stripe, backed by Sequoia) has built something thoughtful, especially for teams that want a knowledge layer across their organization. But like all assistants, Dust operates within a structural ceiling: the employee remains in the driver's seat for every decision and every action, and the work the AI can touch stays shallow.
Nexus is an autonomous agent platform with embedded engineering support. Nexus agents sit on the other side of that structural divide. They complete entire business workflows end-to-end: collecting information from multiple systems, validating against business rules, making decisions within guardrails, escalating exceptions with full context, and executing actions across CRMs, ERPs, communication channels, and custom systems. The agent is the control layer; humans step in for judgment, not for routine execution. And because deploying AI at scale is 10% technology and 90% organizational change, every Nexus engagement includes Forward Deployed Engineers who embed with your team to ensure agents deliver measurable outcomes.
The core question is not "which AI tool is better." It is: does your team need AI that helps individuals find information and draft responses? Or do you need AI that autonomously completes high-volume business processes across multiple systems, making decisions and handling exceptions along the way?
Side-by-side comparison
| Dimension | Dust | Nexus |
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| Depth of work |
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| Decision-making |
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| Exception handling |
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| What it delivers |
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| Who builds and owns it |
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| Deployment model |
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| Deployment speed |
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| Pricing model |
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| Integrations |
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| Security and compliance |
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| Support model |
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| Best for |
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When Dust is the better choice
Dust is the right choice when the work you need AI to do falls within what assistants can structurally deliver. Being honest about that matters.
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Your problem is knowledge access, not workflow completion. Assistants are purpose-built for surface-level work: finding information, answering questions, drafting content. If the problem is "our employees can't find what they need" or "our teams spend too much time searching across Notion, Confluence, and Slack," that is squarely within what Dust handles well. It connects your knowledge bases to LLMs and gives people a smart interface to query company data. No multi-system orchestration, no autonomous decision-making, just fast, accurate answers. That is a real need.
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Your use cases are single-user, single-step tasks. If the work is "help me draft this email," "summarize this document," or "what does our policy say about X," the assistant model is a natural fit. These are tasks where one person asks a question and gets an answer. The structural limitation of assistants (inability to orchestrate multi-step processes across systems) is irrelevant because the work itself is simple.
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You want employees to stay in the loop for every decision. Some use cases genuinely require human judgment at every step. If the goal is "help people think better and find information faster" rather than "complete processes autonomously," the assistant model is the right fit. The human-in-the-loop design is not a limitation here; it is the point.
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Per-seat economics work at your scale. At 29 euros per user per month, Dust is accessible for teams of any size. If your team is small enough that per-seat pricing makes sense, and the value you need is knowledge access and AI-assisted productivity for individuals, the math works.
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You're a European company and want a European AI vendor with strong data privacy defaults. Dust is Paris-based, Sequoia-backed, and offers EU hosting with zero data retention options. If vendor geography and data residency are top priorities alongside a knowledge access use case, that's a meaningful consideration.
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Your team wants to experiment with AI in a self-serve model. Dust's builder experience is well-designed. Non-technical users can create assistants, connect knowledge sources, and start getting value quickly without a formal implementation process. For teams that want to explore what AI can do before committing to deeper process transformation, it is a reasonable starting point.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they have hit the structural ceiling of AI assistants. They deployed an assistant platform, saw initial excitement, then watched usage plateau. The AI helps people find information and draft content, but it cannot touch the deep, complex, multi-system work that actually drives business outcomes. Processes stay manual. The same high-volume workflows still require the same number of people. Leadership asks where the transformation is.
The reason is structural, not a matter of configuration or adoption. Assistants are architecturally limited to surface-level tasks. They cannot orchestrate work across systems, make autonomous decisions, handle exceptions, or complete entire business processes. No amount of fine-tuning an assistant closes that gap, because the gap is in the category itself.
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You need AI that completes business processes, not just answers questions about them. Customer onboarding, lead research, support triage, compliance monitoring, proposal generation: these are multi-step workflows that cross five or more systems and require consistent execution at scale. They involve collecting data, validating it, making decisions, handling edge cases, and taking action. An assistant can tell you the onboarding procedure. An agent performs the onboarding. Dust can surface information about these processes. Nexus agents handle the entire workflow autonomously.
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Your previous AI initiative delivered adoption but not transformation. This is the pattern we see most often. An AI assistant gets deployed. People use it for drafting emails and answering questions. Usage is real but impact is marginal. The high-volume, high-stakes work that actually moves business metrics remains untouched. This is not a failure of the specific tool; it is a structural limitation of the assistant category. Assistants are conversational aids, not autonomous workers. That distinction explains why adoption never translates to transformation. Nexus is built for the deep, complex work that assistants structurally cannot reach.
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You need a partner, not just a platform. Deploying AI at scale is 10% technology and 90% organizational change. Nexus embeds Forward Deployed Engineers with your team: real engineers who identify the highest-impact use cases, design agents for your specific reality, handle integration complexity, and run pilots without requiring your internal resources. This is why Nexus has a 100% POC-to-contract conversion rate.
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Your workflows require multi-system orchestration. If the work involves Salesforce, SAP, custom databases, WhatsApp, and email (not just documents and Slack messages), you need AI that can orchestrate across your entire tech stack. Assistants read from knowledge sources; they do not orchestrate across operational systems. Unlike workflow automation platforms like Zapier or n8n that follow rigid rules, Nexus agents handle exceptions intelligently. Nexus connects to 4,000+ enterprise systems with full read and write access, and deploys agents into the channels teams already use.
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Per-seat pricing doesn't match your scale or your goals. Per-seat models mean costs grow linearly with headcount. At 29 euros per user across thousands of employees, the math gets expensive, and you're paying for access to an assistant, not for outcomes delivered. Nexus charges per-agent: an agent handling customer onboarding for thousands of customers costs the same whether you have 500 employees or 50,000.
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Business teams need to own the AI, not just use it. Dust gives employees an interface to interact with AI. Nexus gives business teams the ability to build and deploy autonomous agents that complete work. The team that understands the process owns the agent, supported by Forward Deployed Engineers who ensure it delivers.
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Governance and auditability are requirements, not nice-to-haves. When AI is making autonomous decisions (approving, routing, escalating), traceability is non-negotiable. Every Nexus agent decision is logged: what data informed it, which rules applied, why it escalated or approved. Complete audit trails are built into the architecture. SOC 2 Type II, ISO 27001, ISO 42001, GDPR. For enterprises where compliance isn't optional, this is the baseline. For a deeper look at the build-vs-buy decision, see our build vs buy analysis.
What enterprises experienced
Orange Group: autonomous workflow completion, not assisted interaction
Orange, a multi-billion euro telecom with 120,000+ employees, had every option available: internal engineering teams, enterprise AI assistants, external agencies. The question was never whether AI could help their employees answer questions faster. The question was whether AI could complete customer onboarding workflows autonomously across multiple countries and languages.
Their business team (not engineering) built autonomous customer onboarding agents using Nexus. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% team adoption.
That last number matters most. 100% adoption happened because the agents live inside the channels the team already works in. There was nothing new to adopt. No separate chat interface to remember, no new login, no training required. The agent collects customer information, validates against systems, checks compatibility, routes exceptions, and escalates complex cases. All autonomously.
A knowledge-connected chat tool like Dust could have helped Orange employees find onboarding procedures or draft customer communications. Those are surface-level tasks, and any good assistant handles them. But Orange did not need help finding information. They needed AI that collects customer data from multiple systems, validates compatibility, makes routing decisions, handles edge cases, and completes the onboarding, all without a human driving every step. That is deep, complex, autonomous work, and it sits beyond the structural ceiling of any assistant.
Lambda: $4B+ pipeline from autonomous research agents
Lambda ($4B+ valuation, 500M+ ARR) is an AI infrastructure company with world-class AI engineers. If any company could build internal AI tools, it's Lambda. AI is literally their business.
Their CTO considered building internally. The conclusion: the opportunity cost of engineering time was too high. Every hour their engineers spent on internal sales automation was an hour not spent on their core AI infrastructure product.
Lambda built autonomous research agents using Nexus that monitor 12,000+ enterprise accounts, identify buying signals, gather competitive intelligence, and synthesize insights. The agent was built by the Head of Sales Intelligence, who has no engineering background, and deployed in days. Result: $4B+ in cumulative pipeline identified, 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts), and a projected value exceeding $7M by 2026.
Before Nexus, Lambda tried open-ended AI tools, including assistant-style products and developer frameworks. Smart but shallow: they could answer individual questions and help with one-off research, but they could not monitor 12,000 accounts continuously, correlate signals across data sources, make prioritization decisions, and synthesize findings into actionable intelligence at scale. That work requires multi-system orchestration, autonomous decision-making, and consistent execution, none of which the assistant category can deliver structurally. They also looked at traditional automation: reliable but rigid, heavy hard-coding, broke when systems changed.
Nexus agents gave them both: intelligence and consistency. Depth and autonomy without sacrificing reliability.
Key differences explained
Assists vs. completes: the structural divide
This is not a feature difference. It is a category difference, and it explains everything else on this page.
AI assistants are surface-level tools. They help with simple tasks that a single person does at a single moment: drafting an email, summarizing a document, answering a question from a knowledge base, searching across company data. The employee is still doing the work. The AI makes them faster at finding, synthesizing, and drafting. But the assistant cannot go deeper. It cannot collect data from five systems, validate it against business rules, make a decision, handle an exception, route the result to the right person, and log the outcome. That is not a configuration gap. It is a structural limitation of the assistant category.
Dust is a well-executed example of that category. It connects company knowledge to LLMs, gives employees a chat interface, and helps individuals work faster. Within the bounds of what assistants can do, it does it well.
Nexus agents sit on the other side of the structural divide. They complete entire workflows independently: collecting information, validating against systems, making decisions within guardrails, escalating when uncertain, and executing actions across multiple systems. The agent is the control layer. Humans step in for judgment calls, not for routine execution. This is not "a smarter assistant." It is a fundamentally different architecture that handles both the conversation and the deep, complex, multi-step work behind it.
The practical consequence: if your goal is helping individuals access information faster, an assistant delivers that. If your goal is transforming business processes, reducing the number of people required for high-volume workflows, and generating measurable outcomes at the organizational level, you need the other category entirely.
Platform vs. platform plus service
Self-serve works for surface-level tools. Connecting knowledge sources, configuring a chat interface, and letting employees start asking questions does not require deep implementation support. Dust is a self-serve SaaS platform, and that model fits what it does: you sign up, connect data sources, build assistants, and your team starts using them.
Autonomous agents that complete deep, multi-system workflows are a different kind of deployment. The work is more complex: integrating with CRMs, ERPs, ticketing systems, and communication channels; mapping business rules into agent guardrails; handling exception routing; ensuring audit trails. This is why Nexus is a platform combined with a service layer. Every engagement includes Forward Deployed Engineers who embed with your team. They identify the highest-impact use cases (not guessing based on templates). They design agents for your specific workflows and systems. They handle integration complexity so your team doesn't have to learn a new platform. They guide change management because deploying AI that does the work, not just assists the worker, changes how entire teams operate.
This distinction explains why Nexus has a 100% POC-to-contract conversion rate. It's not that the technology is infallible. It's that every deployment is supported by engineers who ensure it delivers measurable outcomes before you commit.
For enterprises that have seen AI tools deployed and abandoned, this model addresses the root cause: a surface-level tool without support gets surface-level results. An autonomous agent platform with embedded engineering support delivers the deep transformation that justifies the investment.
Chat interface vs. embedded in existing channels
The assistant model requires a human to initiate every interaction. Someone opens a chat interface, types a question, and receives an answer. Dust implements this through a web app and Slack integration, and it works well for knowledge queries. But this design reinforces the structural limitation: the AI is reactive, waiting for a person to ask something, and the depth of work never goes beyond what that single interaction can produce.
Nexus agents deploy into the channels teams already use: Slack, Teams, WhatsApp, email, phone, web widgets. One agent, multiple channels, zero code changes. The agent doesn't wait for someone to ask a question. It processes workflows as they arrive, wherever they arrive, orchestrating across systems and making decisions autonomously.
Orange saw 100% adoption because the agent was invisible. It was embedded in the tools the sales team already used. Nothing new to learn, nothing extra to open. But more importantly, the agent was completing the work, not waiting for employees to ask it questions about the work. That is the difference between a surface-level tool employees have to remember to use and an autonomous agent that is woven into how work actually gets done.
Knowledge access vs. workflow execution
This distinction is where the structural limitation of assistants becomes most visible in practice.
Dust's architecture is built around connecting knowledge sources to conversational AI. It reads from your knowledge bases and gives conversational answers. That is its strength and its boundary. Their recent additions (scheduled agents, MCP actions for Jira and GitHub, webhook triggers) are expanding toward automation, which reflects where the market is heading. But adding action capabilities to an assistant does not make it an agent; the underlying architecture still centers on a human asking questions and receiving answers, with limited write actions bolted on.
Nexus's architecture is built from the ground up around agents that execute. An agent doesn't just tell you what the customer onboarding procedure is. It performs the onboarding: collecting customer data, validating against backend systems, checking compatibility, routing exceptions, notifying stakeholders, and logging the result. It doesn't just find the relevant compliance document. It runs the compliance check, applies the business rules, logs the result, and escalates exceptions to the right person with full context.
This is the difference between AI that operates at the surface (answering questions about work) and AI that operates at depth (doing the work). For enterprises with high-volume, multi-step processes, this distinction determines whether AI delivers marginal productivity gains for individuals or measurable business transformation at the organizational level.
Frequently asked questions
Can I use both Dust and Nexus?
Yes, and for some organizations it makes sense precisely because they are different categories of technology. Dust handles the surface-level work: helping employees find information, answer questions, and draft content from company data. Nexus handles the deep work: autonomous workflow completion across your entire enterprise stack, with multi-system orchestration, decision-making, and exception handling. You might use Dust for internal knowledge queries and content drafting while Nexus agents handle customer onboarding, sales intelligence, support triage, and compliance workflows. They do not compete; they operate at different depths.
We're evaluating Dust because we want a European AI vendor. Is Nexus European too?
Yes. Nexus is headquartered in Brussels with offices in San Francisco. Y Combinator F25 batch. Backed by General Catalyst and Y Combinator ($4M seed). SOC 2 Type II, ISO 27001, ISO 42001, and GDPR certified. If European vendor geography and data compliance matter to your decision, both Dust and Nexus meet that requirement. The difference is what each platform is built to do.
How long does it take to deploy Nexus compared to Dust?
Dust can be set up quickly for knowledge integration: connect your data sources, configure permissions, and employees start querying. Nexus has a different deployment model because it's solving a different problem. Most enterprise POCs go live within 2 to 6 weeks, with a Forward Deployed Engineer handling integration, configuration, and change management alongside your team. The output is autonomous agents completing real work in production, not a chat interface.
Dust recently added scheduled agents and MCP actions. Doesn't that close the gap?
Dust's product evolution is moving in the right direction. Scheduled agents, webhook triggers, and MCP actions for tools like Jira and GitHub are meaningful additions that extend what the assistant can do. But there is a difference between adding action capabilities to a surface-level tool and building a platform from the ground up for deep, autonomous workflow execution. An assistant with action buttons is still an assistant: the human is still in the loop for every decision, the orchestration across systems is still limited, and the AI still cannot independently collect data from five sources, validate it, make a judgment call, handle an exception, and complete the process. Nexus agents operate across 4,000+ enterprise integrations, handle complex multi-step workflows with intelligent exception handling, make decisions within guardrails, and deploy into every communication channel. The architecture is fundamentally different. Adding actions to a chat layer does not turn it into an agent that serves as the control layer for business processes.
What if our team already uses Dust and likes it?
That usage isn't wasted. Dust still serves its purpose as a knowledge access layer, and it does that well. The question is whether surface-level assistance, helping individuals draft and search and summarize, is delivering the business outcomes your leadership expects from AI investment. If it is, keep using it. If the answer is that you need AI to complete entire business processes autonomously, orchestrate across systems, make decisions, and handle exceptions, that is work that sits beyond the structural ceiling of any assistant. Nexus addresses that gap. The two can coexist.
Our team tried AI assistants and usage plateaued. Will Nexus be different?
This is the most common pattern we hear, and the explanation is structural. AI assistant usage plateaus because assistants only help with surface-level tasks: drafting, answering questions, summarizing. Those tasks are useful but occasional. Employees use the assistant when they remember to, but the AI never changes how core business processes work. The high-volume workflows that consume the most resources remain entirely manual.
Nexus agents are a different category, not a better version of the same one. They integrate into existing channels and complete workflows autonomously. Orange saw 100% adoption because the agent lives inside the tools the team already uses and does the work itself; employees review and add judgment where needed. There is nothing new to adopt, no new habit to form. The agent handles the deep, multi-step process work that assistants cannot structurally touch.
How does pricing compare?
Dust uses per-seat pricing: 29 euros per user per month on Pro, custom pricing for Enterprise (100+ users). Costs scale with headcount.
Nexus pricing is per-agent and depends on what you're automating. Orange generates $4M+ yearly from agents that cost a fraction of what equivalent per-seat licensing would across their 120,000+ employees. Every Nexus engagement starts with a 3-month POC tied to measurable outcomes, so you see the math before committing. You can exit anytime.
What makes Nexus's Forward Deployed Engineers different from typical vendor support?
Forward Deployed Engineers aren't a support desk. They're real engineers who embed with your team during deployment. They identify which workflows will deliver the highest impact. They design agents for your specific systems and processes. They handle integration complexity. They guide change management so your team actually adopts what gets built. This is why Nexus converts 100% of POCs to contracts: every deployment is engineered to deliver measurable outcomes, not just shipped and hoped for.
Worth exploring?
If AI assistants haven't delivered the business process transformation your team expected, the reason is likely structural: assistants help with simple, surface-level tasks, but they cannot do the deep, complex, autonomous work that transforms how an organization operates. That is not a failure of the specific tool. It is a limitation of the category.
It might be worth seeing how Orange achieved 100% adoption and $4M+ yearly revenue with agents that complete work autonomously. Or how Lambda (a $4B+ AI company that could have built internally) chose Nexus and identified $4B+ in pipeline across 12,000+ accounts. Both needed AI that goes beyond answering questions: AI that orchestrates across systems, makes decisions, handles exceptions, and completes entire workflows.
If you need a knowledge layer that helps individuals find information faster, Dust does that well. If you need autonomous agents that complete high-volume workflows and deliver measurable business outcomes, that is a different category, and that is what Nexus is built for.
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 can exit anytime.
Related comparisons
- Nexus vs Microsoft Copilot -- Per-seat AI assistant vs. per-agent autonomous workflows
- Nexus vs Glean -- Enterprise AI that finds information vs. enterprise AI that completes work
- AI Agents vs AI Assistants -- The full category comparison: Copilot, Dust, Glean, and Langdock
- Back to all comparisons -->
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