Nexus vs Relevance AI: Building Agents vs Deploying Them at Enterprise Scale
Relevance AI makes building AI agents accessible. Nexus bridges the gap to enterprise scale with Forward Deployed Engineers and production-grade governance.
Last updated: February 2026
Quick honest summary
Relevance AI has built an accessible, well-designed platform for creating AI agents and multi-agent workflows. Their "AI Workforce" concept is compelling: business teams can sign up, build agents, and coordinate them to automate tasks across tools like Slack, HubSpot, and Asana. For mid-market teams getting started with AI agents, especially for sales and marketing automation, it's a solid self-serve option. 40,000 agents were created on their platform in January 2025 alone. That kind of adoption signals real product-market fit.
Here's the thing about enterprise AI platforms like Relevance AI: they're closer to the agent paradigm than pure assistants, and that's genuinely meaningful. But the depth of enterprise-grade automation they can handle has a ceiling. Governance, compliance, deep system integration, and organizational change management are not problems a builder tool alone solves, no matter how polished the builder is. There's a gap between "building an agent" and "deploying agents that run critical business processes at scale." Most organizations discover this gap only after they've invested in building.
Nexus exists on the other side of that gap. It's not an agent builder. It's a deployment solution: platform combined with Forward Deployed Engineers who embed in your organization, handle integration complexity, manage change, and keep agents running in production. Enterprises that partner with Nexus get FDEs working alongside their team from day one, production-grade governance (SOC 2 Type II, ISO 27001, ISO 42001, GDPR), and agents designed to complete entire business processes autonomously across complex enterprise systems.
The core difference: Relevance AI gives you the tools to build AI agents yourself. Nexus bridges the gap between building and production deployment with a combination of platform and Forward Deployed Engineers. If you're exploring AI agents and want to move fast on your own, Relevance AI is a reasonable starting point. If you've already built agents and found the ceiling of what a builder alone can deliver, Nexus is built for what comes next.
Side-by-side comparison
| Dimension | Relevance AI | Nexus |
|---|---|---|
| What it is |
|
|
| Who builds and owns agents |
|
|
| Deployment model |
|
|
| Multi-agent capabilities |
|
|
| Exception handling |
|
|
| Autonomous completion |
|
|
| Enterprise governance |
|
|
| Integrations |
|
|
| Pricing model |
|
|
| Support model |
|
|
| Target market |
|
|
When Relevance AI is the better choice
Relevance AI is a good platform for specific scenarios, and it's worth being honest about that. The builder ceiling we described above is real, but not every organization is at that ceiling yet. If the following describes your situation, Relevance AI may be the right tool right now:
-
You want to start building AI agents today, without a formal engagement. Relevance AI's self-serve model lets you sign up, explore the platform, and build your first agent in hours. If your team is early in the AI agent journey and wants to experiment before committing to a structured engagement, that accessibility matters.
-
Your use cases are contained within standard business tools. If the workflows you're automating involve tools like HubSpot, Salesforce, Slack, or Google Workspace, and don't require deep integration across legacy ERPs, custom databases, or complex enterprise infrastructure, Relevance AI's integration set handles this well.
-
You have the internal capability to build, deploy, and manage agents without external support. If your team has the technical comfort to configure agents, troubleshoot issues, and iterate without embedded engineering help, a self-serve platform is the right fit. Not every organization needs FDEs and white-glove support. The question is whether your team also has the capacity to handle governance, compliance, and change management internally.
-
Budget is a primary constraint. Relevance AI's pricing starts at free and scales through affordable tiers. If you need AI agent capabilities but the investment required for an enterprise engagement isn't justified yet, it's a practical starting point.
-
Your workflows don't require enterprise-grade compliance. If you're not operating in a regulated industry or at a scale where audit trails, decision traceability, and certifications like ISO 27001 and ISO 42001 are requirements (not nice-to-haves), Relevance AI's security features may be sufficient.
-
You want to build an "AI Workforce" of specialized agents for a specific team. Relevance AI's multi-agent system is well-designed for coordinating a few agents on focused tasks: one agent researching, another drafting, a third distributing. For team-level automation, this works.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they've tried self-serve agent builders, built promising prototypes, then hit the ceiling when it's time to deploy at production scale across real enterprise systems with real compliance requirements and real organizational adoption challenges. Agent builders get you to the prototype. But governance, compliance, deep system integration, and organizational change management are not problems a builder tool alone solves. The gap between "building an agent" and "deploying agents that run critical business processes at scale" is where Nexus, and specifically the Forward Deployed Engineers, come in.
-
You need agents running production workloads, not prototypes. There's a measurable gap between building an agent that works in a demo and deploying one that handles thousands of enterprise interactions daily with full compliance, audit trails, and intelligent escalation. This is the core build vs buy question. Lambda's agent monitors 12,000+ enterprise accounts with consistent, reliable intelligence. That's production-grade, not prototype.
-
Your workflows cross multiple enterprise systems, including legacy infrastructure. When the work involves CRMs, ERPs, ticketing systems, legacy databases, WhatsApp, custom APIs, and internal tools that don't have standard connectors, you need 4,000+ integrations and FDEs who handle the integration complexity. Deep system integration is one of the core problems a builder tool alone cannot solve. Self-serve platforms with a shorter integrations list get you started. FDEs get you into production.
-
You need Forward Deployed Engineers, not documentation. This is the fundamental difference, and it's the reason the builder ceiling exists. Deploying AI at scale is 10% technology and 90% organizational change. Organizational change management is not a problem a builder tool solves. Nexus embeds real engineers in your organization who identify the highest-impact use cases, handle integration complexity, run pilots without requiring your internal resources, and manage the change management that makes adoption stick. Documentation and community forums don't do this. FDEs do.
-
Governance and compliance are non-negotiable. Governance and compliance are two of the core problems that sit beyond the builder ceiling. If you operate in a regulated industry, or at a scale where audit trails, decision traceability, and compliance certifications (SOC 2 Type II, ISO 27001, ISO 42001, GDPR) are requirements, you need governance built into the architecture from the ground up. Nexus agents log every decision: what data informed it, which rules applied, why it escalated or approved. FDEs configure this governance to your specific regulatory requirements. That's the level of transparency regulated enterprises require.
-
You've outgrown credit-based pricing. Credit-based pricing works for experimentation. At enterprise scale, it becomes unpredictable. Enterprise AI platforms generally follow per-seat or per-agent models that scale differently. Relevance AI's pricing model splits credits into Actions and Vendor Credits, and complex workflows with external LLM calls can deplete credits faster than expected. Nexus charges per-agent: an agent monitoring 12,000+ accounts costs the same whether it processes 1,000 or 100,000 queries. The pricing is tied to value delivered, not volume consumed.
-
You need agents that coordinate across the entire organization, not just one team. A European consulting firm deployed a fleet of agents across their entire consulting lifecycle: interviews, CV generation, project matching, proposals, and HR support. Each agent deployed in days. The fleet shares context and coordinates. This multi-department approach is also what separates Nexus from conversational AI platforms that focus on a single channel. That's a different scale from team-level automation.
What enterprises experienced
Lambda, a $4B+ AI company, chose to buy instead of build
Lambda is a $4B+ AI infrastructure company with world-class engineers. If any company could build sales automation agents internally, it's Lambda. AI is their business. Their CTO considered building internally, then concluded the opportunity cost of engineering time was too high.
Their Head of Sales Intelligence, Joaquin Paz (who has no engineering background), built a deep research agent using Nexus that monitors 12,000+ enterprise accounts. Result: $4B+ in cumulative pipeline identified, 24,000+ research hours added annually, $7M+ in projected annual value by 2026.
The key: Lambda didn't need a self-serve agent builder like Relevance AI. They could have built agents themselves with any builder or framework. What they needed was agents that deliver consistent, reliable intelligence at scale, with the governance, integration depth, and ongoing optimization that self-serve builder tools alone don't provide. Open-ended AI tools were too inconsistent (same question, different answer). Self-serve automation and workflow tools were too rigid (lots of hard coding, breaks when systems change). Nexus, with FDEs embedded in their workflow, delivered both intelligence and consistency.
"We looked at open-ended AI agents. They were smart but inconsistent. We looked at traditional automation. It was reliable but felt heavy. With Nexus, we got both: intelligent and consistent." , Joaquin Paz, Head of Sales Intelligence at Lambda
Orange Group, production deployment at Fortune 500 scale
Orange, a multi-billion euro telecom with 120,000+ employees, built autonomous customer onboarding agents using Nexus. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue.
This is the kind of deployment that sits beyond the builder ceiling. Multi-country, multi-language, millions of interactions, with full compliance, audit trails, and 100% team adoption. Governance, deep system integration, and organizational change management were all requirements, and FDEs handled each of them. The agents operate inside the channels the team already uses. There was nothing new to adopt. When the agent is confident, it approves. When uncertain, it escalates with full context. Every step visible. Every decision logged.
A European consulting firm (400+ employees), agent fleet across the business
A European consulting firm deployed a fleet of agents across their entire consulting lifecycle: interview agents, CV generation, project matching, proposal automation, and HR support. Proposal turnaround went from days to hours. Tens of thousands of hours freed monthly.
This is the pattern that matters: not a single agent solving a single problem, but a coordinated multi-agent system transforming how the organization works. Each agent deployed in days, with FDEs managing the integration complexity and organizational change across every department. The fleet approach, where agents share context and coordinate, is what separates enterprise deployment from one-off team automation. A builder tool can create individual agents. Deploying a fleet that coordinates across an entire business requires Forward Deployed Engineers.
Key differences explained
Self-serve AI Workforce vs. enterprise deployment solution: different problems entirely
Relevance AI's "AI Workforce" concept is well-executed for what it is: a self-serve platform where business teams build, coordinate, and manage AI agents on their own. As an agent builder, it's closer to the agent paradigm than pure assistants. For teams that have the internal capability to build and manage agents, it works.
But most enterprises hit a specific ceiling. Building an agent that works in a demo is one problem. Deploying an agent that handles enterprise-scale workflows, across legacy systems, with compliance requirements, with exception handling that doesn't fail silently, with teams that need organizational change management, is a different problem entirely. Governance, compliance, deep system integration, and organizational change management are not problems a builder tool alone solves.
This is where the distinction between "platform" and "solution" matters. Relevance AI is a platform: it gives you the tools to build. Nexus is a solution: platform plus Forward Deployed Engineers who bridge the gap between building and production deployment. FDEs handle integration complexity, identify the highest-impact use cases, manage organizational change, and ensure agents actually work in production. The 90% of AI deployment that isn't technology (change management, adoption, governance, integration) is exactly what self-serve builder platforms don't address, and it's exactly what FDEs are designed to own.
Credit-based vs. outcome-based pricing: the math changes at scale
Relevance AI's pricing model uses credits that split into Actions (what agents do) and Vendor Credits (AI model costs). For experimentation and smaller deployments, this is practical and transparent. At enterprise scale, it becomes harder to predict. Complex workflows that use external LLMs or rich context inputs can deplete credits faster than expected. Builder-tool pricing is designed for building. It doesn't always map well to production-scale execution.
Nexus charges per-agent. An agent monitoring 12,000+ enterprise accounts costs the same whether it processes 1,000 or 100,000 queries. The FDE engagement is built into the model, not charged separately, because the engineering support is inseparable from the deployment. Lambda generates $4B+ in pipeline visibility from agents that cost a fraction of what equivalent credit-based usage would cost at that volume. Every Nexus engagement starts with a 3-month proof of concept tied to measurable outcomes. You see the math before committing.
Governance depth: compliance features vs. compliance architecture
Relevance AI offers real security features: SOC 2 Type II compliance, GDPR adherence, SSO, RBAC, and data residency on their Enterprise plan. These are important and genuine.
Enterprise governance goes deeper than access control and compliance certifications. This is one of the clearest examples of the builder ceiling: compliance at enterprise scale requires architectural decisions, not just feature checkboxes. Nexus provides SOC 2 Type II, ISO 27001, ISO 42001, and GDPR compliance, plus full audit trails where every agent decision is traceable: what data informed it, which rules applied, why it escalated or approved. FDEs configure this governance to match your specific regulatory landscape. When Orange's agents handle customer onboarding across multiple countries, every step is visible, every decision logged, every escalation tracked. Decision transparency is built into the architecture and tuned by FDEs who understand your compliance requirements. That's the difference between compliance features and compliance architecture.
Integration breadth and depth: connectors vs. enterprise infrastructure
Relevance AI integrates with popular business tools: HubSpot, Salesforce, Zapier, Google Docs. For workflows that stay within these tools, the integrations work. Users have noted that connecting to apps outside the natively supported set requires API configuration and technical skills, which can be a friction point. This is another dimension of the builder ceiling: deep system integration, especially with legacy infrastructure, is not something a self-serve builder alone can solve.
Enterprise workflows rarely stay within standard tools. They span CRMs, ERPs, ticketing systems, legacy databases, custom APIs, and communication channels (Slack, Teams, WhatsApp, email, phone). Nexus connects across 4,000+ systems, and Forward Deployed Engineers handle the integration complexity so your team doesn't have to. FDEs connect agents to systems that don't have standard connectors, build custom integrations where needed, and ensure everything works together in production. One agent, deployed across six different channels, zero code changes.
Frequently asked questions
Can I start with Relevance AI and move to Nexus later?
Yes, and some teams do exactly this. Relevance AI is a reasonable way to validate that AI agents can work for your use cases. The experience you gain building agents is valuable context: you understand your workflows, what works, and where you hit the ceiling. When the conversation shifts from "can we build an agent?" to "can we deploy agents at production scale with governance, compliance, and organizational change management?", that's the gap Nexus and its Forward Deployed Engineers are designed to bridge.
How long does Nexus take to deploy compared to Relevance AI's self-serve model?
Relevance AI is faster to start. You can sign up and build an agent in hours. That's the advantage of a builder tool. Nexus takes longer to begin because the engagement includes what builder tools don't cover: identifying the right use cases, configuring integrations across complex systems, setting up governance, and planning organizational change management. Most enterprise POCs go live within 2 to 6 weeks, with a Forward Deployed Engineer handling integration and configuration alongside your team. Orange deployed production agents in 4 weeks. Lambda went from start to production in days for their initial agent. The difference: Nexus agents are production-ready from day one, not prototypes that need additional work to cross the gap into production.
We're a mid-market company. Is Nexus right for us?
Nexus works with enterprises of 500+ FTE where the workflows, compliance requirements, or system complexity justify the engagement model (Forward Deployed Engineers, 3-month POC, white-glove support). If your workflows are straightforward, your systems are standard, and you have the internal capability to manage agents self-serve, a builder platform like Relevance AI may be the better fit today. You may not have reached the builder ceiling yet. If you're running into the gap between prototype and production, or if governance, compliance, deep system integration, or organizational change management are blocking your AI deployment, it's worth a conversation.
What if we've already built agents in Relevance AI?
That's useful context, not wasted effort. You understand your workflows. You know what works and where you've hit the builder ceiling. Nexus doesn't require you to start over conceptually. When the challenge shifts from "can we build an agent?" to "can we deploy agents at production scale across enterprise systems with full governance and organizational adoption?", that's the gap Nexus bridges. Forward Deployed Engineers take what you've learned and move it into production with the governance, integration depth, and change management that builder tools alone cannot provide.
How does pricing compare?
Relevance AI's pricing ranges from free to $234/month for Team, with Enterprise at custom pricing. Costs scale with Actions and Vendor Credits (AI model costs passed through at no markup). Nexus pricing is per-agent and depends on what you're automating. Lambda generates $4B+ in pipeline visibility. Orange generates $4M+ in yearly revenue. Every Nexus engagement starts with a 3-month POC tied to measurable outcomes, so you see the ROI before committing to an annual contract.
Relevance AI raised $24M in Series B. How does Nexus compare in terms of backing?
Both companies have strong investor support. Relevance AI raised $24M led by Bessemer Venture Partners, with participation from Insight Partners, King River Capital, and Peak XV, bringing total funding to $37M. Nexus is backed by Y Combinator (F25 batch) and General Catalyst, with $4M in seed funding. Nexus has $1M+ ARR with enterprise customers and a 100% POC-to-contract conversion rate. The difference isn't funding size; it's what the investment goes toward. Relevance AI is investing in platform scale for broad adoption, making the builder more accessible. Nexus is investing in the layer that sits beyond the builder: Forward Deployed Engineers, change management, and ongoing optimization that bridge the gap between building agents and deploying them at enterprise scale.
Worth exploring?
If your team has been building with self-serve AI agent platforms and hit the ceiling of what a builder tool alone can deliver, or if governance, compliance, deep system integration, and organizational change management are the blockers standing between your agents and production, it might be worth seeing how Nexus bridges that gap. Lambda built $4B+ in pipeline visibility without scaling headcount. Orange achieved 100% team adoption and $4M+ yearly revenue with agents deployed in 4 weeks. Both started with Forward Deployed Engineers embedded from day one.
Every engagement starts with a 3-month proof of concept tied to specific outcomes. You can exit anytime.
Related comparisons
- Nexus vs Microsoft Copilot - AI assistants vs. autonomous agents: why Orange chose Nexus after Copilot adoption dropped
- Nexus vs Zapier - Rigid automation vs. intelligent agents that adapt when exceptions occur
- Nexus vs LangGraph - Developer frameworks vs. enterprise agent deployment with white-glove support
- Nexus vs Glean - Enterprise AI that finds information vs. enterprise AI that completes work
- Back to all comparisons →
Related comparisons
Your next
step is clear
Every engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one.