Nexus vs Ada: Automating the Conversation vs. Completing the Work Behind It
Ada automates customer service conversations. Nexus agents handle the 90% behind them: validation, decisions, exceptions, and cross-system action. See how.
Last updated: February 2026
Quick honest summary
Ada is a customer service automation platform. It helps support teams deflect tickets, resolve common inquiries, and reduce the load on human agents. It does this well for one department: customer support. Ada has recently introduced what it calls "AI agents" and a reasoning engine, but its scope remains customer experience.
Here is the structural question worth considering: conversation is only about 10% of the problem. The other 90% is the complex work behind the conversation. Collecting data from multiple systems. Validating it. Making decisions. Handling exceptions. Routing edge cases. Taking action. Conversational AI platforms are designed around the conversation. Agents are designed around the work.
Nexus is built around that 90%. It is an enterprise agent platform paired with a white-glove service layer. Forward Deployed Engineers embed with your team. Agents complete entire business workflows across every department, from customer onboarding and sales research to compliance monitoring and HR operations. The platform handles the technology. The service layer handles the organizational change that makes AI actually stick.
The right choice depends on which part of the problem you are trying to solve. If you only need to automate the conversation itself (deflecting common support questions), Ada does that. But if the real cost and complexity in your organization sits in the 90% behind the conversation (the cross-system validation, decision logic, exception handling, and autonomous action that actually resolves what a ticket represents), you need AI designed around the work, not the dialogue. And you need engineers embedded alongside your team to make sure it reaches production.
Side-by-side comparison
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When Ada is the better choice
Ada is the right choice in specific scenarios, and it is worth being honest about that:
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The conversation itself is genuinely the bottleneck, not the work behind it. If the problem you are solving is strictly "too many support tickets, not enough agents," and the underlying processes are already efficient, then automating the conversation layer is the right move. Ada is built for exactly that. It is a focused tool for a focused problem.
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You need a customer-facing support chatbot deployed on messaging channels. Ada is purpose-built for the dialogue layer of support. If your support team needs a chatbot on your website or messaging channels that answers FAQs, troubleshoots common issues, and routes complex cases to human agents, Ada addresses that use case.
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Your AI ambitions are limited to customer service. If the rest of your organization does not need AI agents (no sales, marketing, HR, or compliance workflows to automate), then Ada's focus on support conversations is not a limitation. You get a tool built specifically for one department, for the conversational portion of the work.
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You have a strong internal team that can manage configuration and optimization. Ada is software, not a solution. If your team has the resources and expertise to configure conversation flows, manage ongoing AI training, and handle integration complexity without external support, that model works.
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You want resolution-based pricing for a predictable, high-volume support operation. Ada's pricing model ties cost to resolved conversations. If you have predictable support volume and want to pay per resolution, that model can work for support-only use cases.
When Nexus is the better choice
Enterprises that partner with Nexus tend to share a specific pattern: they have realized that the conversation is a small piece of the problem. The real complexity (and the real cost) sits in the work behind the conversation: the cross-system data collection, validation, decision-making, exception handling, and action. They need AI designed around that work, not just the dialogue layer, and they need the engineering and change management support to actually reach production.
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You need AI across departments, not just in support. Ada automates customer service conversations. But what about sales research, customer onboarding, compliance monitoring, HR operations, marketing workflows? This scope limitation is shared by all conversational AI platforms, from Kore.ai to Yellow.ai. If AI needs to work across your organization, not just within the support team, you need a platform that was built for that. Nexus deploys agents wherever the business needs them.
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You need agents that complete the work behind conversations, not just the conversations themselves. Ada resolves the dialogue: a customer asks, Ada answers. But resolving a support ticket through dialogue is not the same as completing the work the ticket represents. That work is the 90%. Nexus agents complete entire business processes: collecting information, validating against systems, making decisions within guardrails, routing to the right teams, and executing actions across multiple systems. There is a meaningful difference between answering a customer's question and autonomously onboarding that customer end-to-end.
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You want embedded engineers, not just software. Deploying AI at scale is 10% technology and 90% organizational change. Nexus embeds Forward Deployed Engineers with your team to identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, and guide adoption. This is why Nexus has a 100% POC-to-contract conversion rate. Other platforms sell software and leave implementation to you.
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Your support challenges go beyond FAQ deflection. If your support operations involve complex workflows (compliance requirements, multi-system validation, regulatory audit trails, cross-department coordination), automating the conversation handles the easy part and leaves the hard part untouched. You need agents that orchestrate the entire process behind those conversations. A multi-billion euro telecom operator deployed Nexus agents across support, compliance, registration, and escalation handling, freeing 40% of support capacity while maintaining 100% regulatory compliance. The agents did not just talk. They validated, decided, routed, and acted.
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You have tried chatbot solutions and hit a ceiling. This is a pattern we see often, and it parallels the experience enterprises have with AI assistants like Copilot. The chatbot handles the easy questions well, but the complex cases (the ones that actually cost the most) still end up with human agents. The structural reason is that conversational AI is designed around the conversation, not around the work behind it. It optimizes the 10% and leaves the 90% untouched. Nexus agents handle complexity because they are designed around the work. They reason through exceptions, access multiple systems, and complete processes that chatbots structurally cannot reach.
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Business teams need to own the AI, not just the support team. Ada is configured and owned by the support function. Nexus agents are built and owned by whatever business team understands the workflow: sales, operations, HR, marketing. No engineering dependency. No waiting for IT.
What enterprises experienced
Orange Group: autonomous agents across the business, not just support
Orange, a multi-billion euro telecom with 120,000+ employees, did not need a CX chatbot like Ada. A customer service chatbot would have resolved the surface-level conversation: "What plan are you interested in?" "What is your address?" That is the 10%, and Ada would count each as a successful resolution. They needed autonomous agents that could handle the 90% behind that conversation: collecting customer information, validating data against multiple backend systems, checking compatibility, routing unusual cases, and escalating complex issues, all without human intervention for routine cases.
Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% team adoption because the agents live inside the channels teams already use (Slack, email, WhatsApp). Not a chatbot sitting on a website. An agent embedded in how the business actually works.
The distinction matters: this was not about deflecting support tickets. It was about an autonomous agent completing a revenue-generating business process end-to-end. And Orange's business team built it, not engineering. Nexus's Forward Deployed Engineers worked alongside them from day one, handling integration complexity and change management so the team could focus on the business logic.
A multi-billion euro telecom operator: support is just the starting point
A major European telecom (13,000+ employees, over half a billion in revenue) needed more than a support chatbot. They needed agents across support, compliance, registration, data harmonization, and escalation handling. A coordinated system that worked across departments and maintained regulatory compliance across millions of interactions.
The result: 40% of support capacity freed. Full regulatory compliance maintained. 12-week deployment. The agents handle exceptions intelligently; when regulations change, the agents adapt without requiring a rebuild.
A chatbot could have handled the conversation layer of simple support queries. That is the 10%. But the value came from agents that completed the work behind those queries: the compliance checks, the cross-department coordination, the regulatory adaptation. The 90%. Support was one piece of a much larger picture.
Lambda: a multi-billion-dollar AI company chose to buy, not build
Lambda, a multi-billion-dollar AI infrastructure company with world-class AI engineers, could have built sales automation agents internally using developer frameworks or open-source tools. AI is literally their business. Their Head of Sales Intelligence, who has no engineering background, built a sales research agent on Nexus that now monitors 12,000+ enterprise accounts, identifies buying signals, and surfaces competitive intelligence. The result: $4B+ in pipeline opportunity identified, 24,000+ hours of research capacity added annually.
Lambda is now expanding beyond sales intelligence into marketing operations and customer engagement, building what they call an "agentic layer" across their entire go-to-market organization. Anticipated value: over $7M by 2026.
If a multi-billion-dollar AI company chose Nexus over building internally, the question for most enterprises is: what is your opportunity cost of trying to build this yourself?
The real differences, explained
Designed around the conversation vs. designed around the work: different categories
This is the fundamental distinction, and it matters more than any feature comparison.
Ada is a customer service platform. It is designed around the conversation: one department (support), one interaction type (customer dialogue), and one outcome (ticket deflection and resolution). It recently introduced a "reasoning engine" and "AI agents," but these capabilities remain within the customer experience scope. Its integrations connect to helpdesk tools. Its pricing is based on support resolutions. It optimizes the 10% of the problem that is the conversation itself.
Nexus is an enterprise agent platform with an embedded service layer. It is designed around the work behind conversations. Built for any department, any workflow type, and any outcome the business needs. Agents do not just have conversations. They complete the processes that conversations initiate: collecting data from multiple systems, validating it, making decisions within guardrails, handling exceptions, routing edge cases, and executing actions independently. Customer support is one possible use case, not the entire product.
This is not a criticism of Ada. If the conversation is the bottleneck, a tool designed around conversations makes sense. But if the real complexity sits in the work behind the conversation (and for most enterprises, it does), you need AI designed around that work. The scope is fundamentally different.
Software vs. solution: the service layer matters
Ada sells software. You configure it, manage it, and optimize it. If implementation takes months (as user reviews suggest), that is your team's time and resources. And critically, the software is scoped to the conversation layer. The complex work behind it (the integrations, the decision logic, the exception handling) is still your problem to solve.
Nexus sells a solution: platform plus service. Forward Deployed Engineers are real engineers embedded in your organization. They do not just help you set up conversation flows. They map the 90% of work behind your conversations: the systems that need to connect, the decisions that need to be made, the exceptions that need to be handled. They identify the highest-impact use cases (not guessing based on templates). They design agents that fit your specific reality (not generic off-the-shelf). They handle integration complexity so your team does not have to learn a new platform from scratch. They guide change management because agents change how work gets done.
This is why every Nexus POC converts to a production contract. The service layer is the difference between software that automates conversations and a solution that completes the work behind them.
Conversations vs. the work behind them: the 10%/90% split
Think about what actually happens when a customer contacts your business. The conversation (greeting them, understanding their request, answering their question) is roughly 10% of the effort. The other 90% is what happens behind that conversation: pulling records from the CRM, checking inventory in the ERP, validating compliance against regulatory databases, creating tickets across systems, routing exceptions to the right team, sending confirmations, scheduling follow-ups.
Ada automates the 10%. It resolves conversations. A customer asks a question; Ada answers it or routes it. The scope is the dialogue.
Nexus agents complete the 90%. An agent handles customer onboarding by collecting information via conversation, then validating it against a CRM, checking system compatibility, creating records in the ERP, sending confirmation via email, and scheduling a follow-up in the calendar. The conversation is one step in a larger process, not the process itself.
Orange's onboarding agent does not just talk to customers. It completes the entire onboarding workflow autonomously, from first contact through validation, approval, and handoff. That is why they saw $4M+ in yearly revenue impact. The agent is not deflecting questions. It is completing revenue-generating work. The conversation was the easy part.
Resolution-based pricing vs. value-based pricing
Ada charges per resolution. The more successfully your AI resolves customer inquiries, the more you pay. This creates a structural tension: success drives higher costs. For high-volume operations, this can mean annual costs in the six figures (users report $300,000+ at scale), and the ambiguity around what counts as a "resolution" can make budget forecasting difficult.
Nexus charges per agent. An agent that handles customer onboarding for thousands of customers or monitors 12,000+ accounts costs the same regardless of volume. Orange generates $4M+ yearly revenue from agents that cost a fraction of what resolution-based pricing would require at their scale. Every Nexus engagement starts with a 3-month POC tied to measurable outcomes, so you see the math before committing.
One department vs. the whole organization
Ada lives in customer service. It integrates with support tools (Zendesk, Salesforce Service Cloud, helpdesk platforms). Its agents are built by support teams for support outcomes.
Nexus connects to 4,000+ enterprise systems and deploys agents across any department. The same platform handles sales intelligence (Lambda: $4B+ pipeline identified), customer onboarding (Orange: 50% conversion improvement), support operations (a major telecom: 40% capacity freed), compliance monitoring, HR workflows, and marketing operations. One platform, every department, no artificial limits on where AI can work.
Companies we work with do not deploy AI to one department and stop. Once they see what happens when AI completes the work behind conversations (not just the conversations themselves), they expand. Orange started with customer onboarding. Lambda started with sales research and is now expanding across their entire go-to-market organization. The telecom operator built agents across support, compliance, and registration. Every department has work behind its conversations. The platform scales with your ambitions, not constrained by a single department's needs.
Frequently asked questions
Can I use both Ada and Nexus?
Yes. If Ada is working well for basic customer service deflection and your support team is happy with it, there is no reason to change that. Nexus addresses a different need: autonomous agents that complete workflows across your entire organization (sales, operations, compliance, HR, and beyond), backed by embedded engineering support. Some enterprises use a support-specific tool for simple ticket deflection and Nexus for the complex, multi-system workflows that a chatbot cannot reach.
We already invested in Ada. Does Nexus replace it?
Not necessarily for basic support chat. If Ada is effectively automating the conversation layer (the 10%), it can continue doing that. The question is whether automating conversations alone is delivering the business transformation your leadership expects. If the real cost and complexity sits in the 90% behind those conversations (completing workflows across departments, handling complex processes end-to-end, driving measurable revenue impact), Nexus addresses that gap. The two can coexist.
Ada says they do "AI agents" now. How is that different from Nexus?
Ada recently launched what it calls a "reasoning engine" and expanded into "agentic" capabilities. These are extensions of their customer service platform: useful for actions that flow naturally from support conversations. But they are still designed around the conversation. They enhance the 10%. Nexus was built agent-first, designed around the 90% of work behind conversations: autonomous execution, multi-system orchestration, exception handling, and enterprise governance are core architecture, not features added to a chatbot platform. The difference shows up in scope and complexity: Nexus agents complete multi-step business processes across 4,000+ enterprise systems and are supported by Forward Deployed Engineers. Ada's agents operate within the CX domain.
Ada's pricing has gotten expensive at our volume. How does Nexus compare?
Ada's resolution-based pricing can scale into six figures for high-volume operations, and costs increase as AI performance improves (more resolutions equals higher bills). Nexus uses per-agent pricing tied to value delivered. An agent handling thousands of interactions costs the same regardless of volume. Orange generates $4M+ yearly revenue from agents that cost a fraction of what volume-based pricing would require at their scale. Every Nexus engagement starts with a 3-month POC tied to measurable outcomes, so you see the ROI before committing.
We do not have internal engineers to build and maintain AI agents. Is that a problem with Nexus?
The opposite. Nexus's Forward Deployed Engineers embed with your team specifically so you do not need internal AI engineering resources. Business teams (not engineers) build and own agents on the platform. Nexus FDEs handle integration complexity, agent design, and technical optimization. Lambda's sales research agent was built by their Head of Sales Intelligence, who has no engineering background. Orange's onboarding agents were built by their business team, not engineering. The service layer is a core part of what Nexus delivers.
We are mostly focused on improving customer support. Should we still consider Nexus?
It depends on whether your bottleneck is the conversation or the work behind it. If the goal is straightforward ticket deflection for common questions (automating the dialogue layer), Ada may be the simpler choice. But if your support challenges involve the 90% behind the conversation (multi-system validation, compliance requirements, cross-department coordination, intelligent escalation), those are agent problems, not chatbot problems. A multi-billion euro telecom operator chose Nexus for exactly this reason: they needed support agents that maintained regulatory compliance across millions of interactions, coordinated with compliance and registration workflows, and adapted when regulations changed. That is work behind the conversation, not the conversation itself.
Worth exploring?
If your team has realized that automating the conversation is only about 10% of the problem, if the real complexity and cost sits in the work behind those conversations (the validation, decisions, exceptions, and actions across systems), it might be worth seeing how others have approached the 90%. Orange achieved $4M+ yearly revenue with autonomous onboarding agents that complete the full workflow, not just the dialogue. Lambda built an intelligence layer across 12,000+ enterprise accounts with agents that collect, analyze, and act on data across systems.
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.
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