Nexus vs Kore.ai: When the Conversation Is Only 10% of the Problem

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Quick honest summary

Kore.ai is a Gartner Magic Quadrant Leader in conversational AI, and that recognition is earned. Their XO Platform excels at building virtual assistants and chatbots for customer support, IT helpdesk, and employee self-service. With $223M+ in funding, 400+ Fortune 2000 customers (including AT&T, Coca-Cola, Airbus), and a recently launched Agent Platform for multi-agent orchestration, Kore.ai has built a serious presence in enterprise conversational AI.

Here is the question worth sitting with: how much of your enterprise work is actually the conversation itself?

In most cases, the answer is about 10%. The other 90% is the work behind the conversation: collecting data from multiple systems, validating it against business rules, making routing decisions, handling exceptions that do not fit standard paths, coordinating across departments, and taking action. Conversational AI platforms are designed around that 10%. They are built around the conversation. Nexus agents are designed around the work.

Where Kore.ai helps enterprises handle conversations, Nexus deploys autonomous agents that complete entire business workflows end-to-end: customer onboarding, sales intelligence, compliance monitoring, support operations, and more. Conversation is one channel among many. The real differentiator is the service layer: Forward Deployed Engineers embedded with your team, change management guidance, and ongoing optimization. Nexus is a solution (platform + service), not just software.

The right choice depends on where the bottleneck sits. If your primary goal is building conversational interfaces for customer-facing support, Kore.ai does that well. If the bottleneck is not the conversation but everything that happens after and around it, you are looking at a different category of problem. That is what Nexus is built for.

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Side-by-side comparison

Dimension	Kore.ai	Nexus
What it does	Conversational AI platform for virtual assistants and chatbots Contact center automation Recently added Agent Platform for multi-agent orchestration Designed around the conversation	Autonomous AI agents complete the full workflow, not just the conversation Handles the 90% behind the dialogue: validation, routing, exceptions, cross-system action Paired with Forward Deployed Engineers from day one
Primary paradigm	Conversation-first: the dialogue IS the product Chatbot/virtual assistant interface is the core Agent capabilities are a recent addition layered on top	Work-first: the agent completes the process, conversation is one interface Designed around the 90% that happens behind, after, and around the dialogue Conversation, email, Slack, API, background automation all treated equally
Who builds/owns it	IT or specialized bot-building teams Requires NLU training, dialog management, intent modeling Needs expertise in conversational design	Business teams who understand the work build and deploy agents No NLU training, no dialog trees FDEs handle integration complexity; business teams own the outcome
Deployment model	Software platform 6-18 months for complex enterprise scenarios Professional services available through partners	Platform + service with embedded FDEs from day one Production agents in 2-6 weeks 3-month POC tied to measurable outcomes
Handles exceptions?	Bots follow dialog flows; off-script inputs hit fallback paths Escalate to human agents when conversation goes beyond pre-defined intents Exceptions in the work behind the conversation are not addressed	Agents reason through exceptions in the workflow, not just the dialogue Handle edge cases in data, routing, and cross-system coordination autonomously Escalate with full context when genuine judgment is needed No dead ends in the conversation or the process
Scope of work	Customer-facing conversations: support, helpdesk, FAQ Employee self-service Growing into process automation via Agent Platform	Any enterprise workflow across all departments Sales, marketing, HR, support, compliance, onboarding All channels covered
Completes work autonomously?	Handles the conversational 10% The 90% behind it (processing, validation, routing, exceptions) still needs humans or separate automation Work pauses when the conversation ends	Agents handle the full 100%: conversation AND the work behind it Collect data, validate, decide, route, escalate, act across systems Conversation is where autonomous work begins, not where it ends
Deployment speed	Weeks to months for basic chatbots 6-18 months for complex enterprise deployments Requires NLU training, dialog design, integration work	Days to weeks for production agents Orange deployed in 4 weeks Major telecom deployed multi-agent suite in 12 weeks
Integrations	100+ pre-built connectors CRMs, ITSM tools, contact center platforms Additional integrations require development	4,000+ integrations (CRMs, ERPs, APIs) Deploy across Slack, Teams, WhatsApp, email, phone, web Single agent definition covers all channels
Service and support	Software platform with partner-driven services Gartner noted "scalable business model...without overreliance on professional services" You configure the conversation; you manage what happens behind it	Forward Deployed Engineers embedded with your team from day one FDEs handle integration complexity, change management, and ongoing optimization The service layer is how the 90% behind the conversation actually gets solved
Pricing	No public pricing Enterprise deployments start around $300K+ annually Tiered licensing and usage fees	Per-agent pricing tied to value delivered 3-month POC tied to specific outcomes
Security and compliance	Enterprise security, SOC 2 Data encryption On-premise deployment option	SOC 2 Type II, ISO 27001, ISO 42001, GDPR Full audit trails and decision traceability Role-based access
Best for	Building conversational interfaces (the 10%) Customer support, IT helpdesk, employee self-service Orgs where the conversation itself is the bottleneck	Completing the full workflow, including the 90% behind the conversation Any system and department Orgs that have realized the bottleneck is the work, not the dialogue

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When Kore.ai is the better choice

Kore.ai is the right choice in specific scenarios, and it is worth being honest about that:

• Your primary need is a customer support chatbot or virtual assistant. If the goal is deflecting common support questions, routing tickets, and reducing call center volume through a conversational interface, Kore.ai is purpose-built for this. Their NLU engine, dialog management, and contact center AI integrations are mature and battle-tested across 400+ Fortune 2000 customers.

• You need a Gartner-recognized conversational AI platform. If your procurement process requires analyst validation and you are specifically looking for a conversational AI platform, Kore.ai's position as a 2025 Magic Quadrant Leader carries real weight. Gartner rated them highest for "Ability to Execute" and noted their comprehensive, feature-rich platform for GenAI enablement and process management.

• Your use case is contained within the conversation itself. Some use cases genuinely live in the 10%. If the value starts and ends with the conversation (answering questions, collecting information, routing to the right department) and the back-end processing is already handled by existing systems, Kore.ai handles that conversational layer well. The 90% behind the conversation is not your bottleneck.

• You have a dedicated bot-building team and long deployment timelines are acceptable. Kore.ai provides strong tooling for teams that specialize in building and maintaining conversational experiences: NLU training, intent modeling, dialog management, conversation analytics. If you have the team and the runway for a 6-18 month implementation, the platform gives them robust capabilities.

• You need on-premise deployment. Kore.ai offers on-premise options for organizations with strict data residency requirements. This is a meaningful differentiator for specific regulated environments.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they have deployed conversational AI and realized the 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, and taking action. Conversational AI platforms are designed around the conversation, not around the work. They resolve tickets through dialogue. Agents complete the entire workflow the ticket represents.

These enterprises have also learned that deploying AI at scale is itself a 10/90 problem: 10% technology and 90% organizational change. That is why the FDE model matters as much as the platform.

• You need AI that completes workflows, not just conversations. Customer onboarding is not a conversation. It is a multi-step process involving data collection, system validation, compatibility checks, routing, and follow-up across multiple systems. Kore.ai can handle the conversational front-end. Nexus agents handle the entire workflow autonomously, with conversation as just one interface. Orange deployed autonomous onboarding agents in 4 weeks: 50% conversion improvement, $4M+ incremental yearly revenue.

• You need embedded engineering support, not just software. Kore.ai sells a platform. Nexus provides a solution: platform plus Forward Deployed Engineers who embed with your team from day one, identify the highest-impact use cases, handle integration complexity, run change management, and optimize continuously. This is why Nexus has a 100% POC-to-contract conversion rate. Every pilot delivers measurable value because there is an engineering team ensuring it does.

• Your challenge spans multiple departments, not just customer support. Kore.ai's core strength is customer support, IT helpdesk, and employee self-service. But what about sales operations, marketing workflows, HR onboarding, compliance monitoring? See how this same scope limitation applies to other conversational AI platforms like Ada and Yellow.ai. Lambda built sales intelligence agents that monitor 12,000+ enterprise accounts, uncovering $4B+ in pipeline. Companies we work with build agents for sales, support, compliance, registration, and more on the same platform.

• You have built a chatbot and the ROI is not there. Many enterprises deploy conversational AI and find that deflecting FAQ questions saves less than expected. The reason: the expensive, high-volume work is not in the conversation. It is in the 90% behind it: the compliance validation, cross-system data harmonization, registration processing, and exception handling that humans still have to coordinate across multiple systems. Unlike rigid workflow automation tools that break on exceptions, Nexus agents adapt intelligently. A major European telecom had exactly this pattern. Conversational AI covered the front end. The work behind those conversations still required manual coordination. They deployed Nexus agents and freed 40% of their support capacity, because the agents completed the work, not just the dialogue.

• Business teams need to own the AI, not wait for IT. Kore.ai platforms require dedicated bot-building teams with NLU expertise, intent modeling skills, and conversational design knowledge. G2 and Capterra reviewers consistently note the platform's complexity for non-technical users. This engineering dependency is the same challenge enterprises face with developer frameworks and open-source builders like Dify. Nexus agents are built and owned by the business teams who understand the workflows. At Orange, the business team (not engineering) built and deployed their onboarding agents in 4 weeks. At Lambda, a non-technical sales operations leader built their intelligence agent without engineering support.

• You cannot wait 6-18 months for production deployment. Kore.ai's implementation timelines for complex scenarios can stretch to 6-18 months, with extensive NLU training, dialog design, and integration work. Nexus production agents go live in 2-6 weeks because Forward Deployed Engineers handle integration complexity alongside your team, and agents do not require NLU training or dialog flow design.

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What enterprises experienced

Orange Group: the conversation was 10% of onboarding. The other 90% was the problem.

Orange, a multi-billion euro telecom with 120,000+ employees, needed to transform customer onboarding across multiple European markets. A conversational AI platform like Kore.ai's XO Platform could have handled the initial conversation: collecting customer information and answering questions through well-designed dialog flows. That is the 10%. But the actual onboarding workflow (the 90%) involves real-time data validation, system compatibility checks, intelligent routing, and exception handling across multiple back-end systems. No conversational AI platform was designed to handle that.

Orange's business team built autonomous onboarding agents using Nexus. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. The agent does not just have a conversation with the customer. It completes the entire onboarding process end-to-end, escalating to a human only when genuine judgment is needed.

When the agent can confidently approve, it approves. When uncertain, it escalates to the salesperson with full context. The salesperson reviews, adds judgment, approves or modifies. Every step visible. Every decision logged. Result: 100% adoption, 50% conversion increase, 100% compliance. Not heavy-handed controls; governance woven into the work itself.

A multi-billion euro telecom: the conversation was covered. The 90% behind it was not.

A major European telecom (13,000+ employees, 500M+ revenue) had the conversational layer covered. They had invested in conversational AI for customer-facing interactions. That 10% worked. But the 90% behind those conversations (compliance validation, cross-system data harmonization, registration processing, escalation routing) still required humans to coordinate across multiple systems. The conversational AI was designed around the conversation. Nobody had addressed the work.

They deployed a suite of Nexus agents: support agents, compliance agents, registration agents, and escalation handlers. The agents do not just converse. They collect data from multiple systems, validate it, make routing decisions, handle exceptions, and complete the work. 40% of their support capacity was freed. Full regulatory compliance maintained across millions of interactions. 12-week deployment timeline, with FDEs handling integration complexity.

The conversational AI handled the front-end. Nexus agents handled everything behind it.

Lambda.ai: a $4B+ AI company that chose to buy, not build

Lambda, a $4B+ AI infrastructure company, could have built sales intelligence tools internally. AI is literally their business. Their CTO evaluated building in-house and concluded the opportunity cost was too high. Every hour their engineers spent on internal tools was an hour not spent on their core AI infrastructure product.

Lambda's Head of Sales Intelligence, who has no engineering background, built their AMO Enterprise Evolution agent using Nexus. The agent monitors 12,000+ enterprise accounts annually, synthesizing data from dozens of sources to identify buying signals and market opportunities. Result: $4B+ in pipeline discovered, 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts), and deployment in weeks, not months.

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Key differences explained

The 10/90 split: why this is a category question, not a feature question

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

Think about customer onboarding, compliance validation, or sales intelligence as workflows. The conversation (collecting information, answering questions, confirming details) is maybe 10% of the actual work. The other 90% is what happens behind, after, and around that conversation: pulling data from multiple systems, validating it against business rules, checking compatibility, routing to the right team, handling the cases that do not fit standard paths, updating records across departments, and following up. That 90% is where the cost sits. It is where the errors happen. It is where humans spend their time coordinating.

Kore.ai is a conversational AI platform. It excels at the 10%: understanding what a user is saying, managing dialog flows, routing conversations to the right place. The conversation is the product. Their recently launched Agent Platform extends into multi-agent orchestration, but the foundation remains conversation-centric. The platform is designed around the conversation.

Nexus agents are designed around the work. Conversation is one interface among many. Agents also operate through email, Slack, Teams, WhatsApp, background automation, and API integrations. The agent's job is not to have a conversation. The agent's job is to complete the process. Sometimes that involves conversation. Sometimes it does not.

This is not a criticism of Kore.ai. They are genuinely strong at conversational AI, and Gartner's recognition reflects that. But if the bottleneck in your organization is the 90% (the process, the validation, the cross-system coordination, the exception handling) then improving the conversation does not solve the problem. You need a different category of solution.

Software vs. solution: the FDE model and why it exists

Gartner recognized Kore.ai for its "scalable business model focused on tiered licensing and usage fees without overreliance on professional services." That is a genuine strength for enterprises that want self-serve software for the conversational layer.

But here is the thing: the 90% behind the conversation is hard. It involves integrating with multiple enterprise systems, mapping business logic that lives in people's heads, handling edge cases that no one documented, and navigating organizational resistance to new workflows. Software alone does not solve that. This is why Nexus embeds Forward Deployed Engineers with your team from day one.

FDEs are real engineers who identify the highest-impact use cases (usually where the 90% is most painful), design agents that fit your specific reality, handle integration complexity across your systems, run change management, and optimize continuously. Deploying AI that completes work across systems and departments is itself a 10/90 problem: 10% technology and 90% organizational change. The FDE model exists because that organizational change does not happen through a software license.

This is why Orange's business team deployed production agents in 4 weeks, not 6-18 months. The FDE team handled the complexity of integrating with back-end systems, mapping onboarding logic, and managing the transition. This is why Nexus has a 100% POC-to-contract conversion rate: every pilot is engineered to deliver measurable value before you commit.

Most enterprise AI vendors sell software and leave you to figure out the hard parts. Nexus succeeds when you succeed. The service layer is not an add-on. It is how the 90% actually gets addressed.

Bot-building teams vs. business ownership

Kore.ai requires expertise in NLU, intent modeling, dialog design, and conversation flow management. This means dedicated bot-building teams, often within IT or a specialized center of excellence. Changes to the bot require going back through the same team. G2 and Capterra reviewers note the platform's steep learning curve and the coordination required across teams for testing and deployment.

Nexus agents are built by the business teams who understand the workflow. No NLU training. No intent libraries. No dialog trees. The business team defines what the agent should do, what systems it connects to, and how it should handle exceptions. Changes happen in hours, not weeks. At Lambda, a non-technical sales operations leader built their intelligence agent without engineering support. At Orange, the business team deployed their onboarding agents in 4 weeks.

Dialog flows vs. intelligent adaptation

Conversational AI platforms are built around dialog flows: structured paths that a conversation can follow. When a user says something unexpected, the bot either follows a fallback path or escalates. The logic is pre-defined. Kore.ai's XO Platform v11 introduced DialogGPT as the default intent identification mode, which reduces configuration effort compared to traditional NLP. But the underlying architecture remains dialog-driven. And critically, dialog flows only govern the conversation. The 90% of work behind the conversation (system interactions, validation logic, exception handling) still needs separate automation or human coordination.

Nexus agents understand business logic and adapt across the full workflow, not just the conversation. When something unexpected happens (an edge case in the data, a validation failure across systems, a request that does not fit existing categories) the agent reasons within its guardrails, handles what it can, and escalates with full context when it cannot. No dead ends in the conversation or in the process. The agent is the control layer for the entire workflow, not a scripted interface for one channel of it.

Kore.ai's evolution toward agents

It is worth noting that Kore.ai announced its Agent Platform in March 2025 for building, deploying, and orchestrating agentic applications. This is a meaningful step. The platform supports varying levels of autonomy, from orchestrated to fully autonomous agents, with multi-agent orchestration capabilities.

However, there is an important distinction between adding agent capabilities to a conversational AI platform and building agent-first from the ground up. Kore.ai's agent capabilities sit on top of a conversational foundation. The platform was designed around the conversation, and agents were added later. Nexus was built from the start around the work: autonomous workflow completion across systems, departments, and channels, with the FDE model to handle the organizational complexity that software alone cannot address. The architecture reflects what was built first, and what was added later.

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Frequently asked questions

Can I use both Kore.ai and Nexus?

Yes, and this is actually a common pattern. Kore.ai handles the conversational 10% (FAQ deflection, basic support routing, employee self-service) while Nexus agents handle the 90% behind those conversations: the workflows, the cross-system coordination, the exception handling, the autonomous completion of work. They address different parts of the problem and can coexist.

We already invested in Kore.ai. Is that wasted?

Not at all. If Kore.ai is handling the conversational layer well, keep it. The question is whether the conversation is where your bottleneck actually sits. If the high-impact opportunity is in the 90% behind those conversations (onboarding workflows, compliance validation, cross-system processing, sales intelligence) Nexus addresses that gap. Your Kore.ai investment handles the 10% it was designed for. Nexus extends into the 90% that conversational AI was never built to reach.

Kore.ai now has an Agent Platform. Is it comparable to Nexus?

Kore.ai's Agent Platform, announced in March 2025, adds multi-agent orchestration and autonomous agent capabilities. It is a genuine step forward. The difference is architectural: Kore.ai extended a platform designed around the conversation with agent features. Nexus was built from the ground up around the work (the 90% behind the conversation). And Nexus includes Forward Deployed Engineers embedded with your team, not just software. The deployment model, speed to production, and ongoing support are fundamentally different. Adding agent capabilities to a conversation platform is not the same as building an agent platform from scratch.

How is Nexus different from adding workflow automation behind our chatbot?

That is a common approach: connect a chatbot to handle the conversational 10%, then bolt on a workflow tool for the 90% behind it. The problem is those workflow tools are rigid. They follow pre-defined paths. They break on exceptions. When something unexpected happens (and in enterprise workflows, it always does), the automation fails and a human has to step in. You end up with two fragile systems stitched together, neither of which handles the hard parts. Nexus agents handle the entire workflow intelligently as one system. They reason through exceptions, escalate with context, and complete work autonomously across the full 100%.

How long does it take to deploy Nexus?

Most enterprise POCs go live within 2-6 weeks, with a Forward Deployed Engineer handling integration and configuration alongside your team. Orange deployed customer onboarding agents in 4 weeks. A major European telecom deployed a multi-agent suite in 12 weeks. Every engagement starts with a 3-month proof of concept tied to measurable outcomes, so you see the results before committing. You can exit anytime.

Does Nexus handle customer-facing conversations like Kore.ai does?

Nexus agents can handle customer-facing conversations, deployed through WhatsApp, web widgets, email, phone, Slack, or Teams. But the conversation is not the end goal. It is the 10%. The agent uses the conversation to collect information, then autonomously completes the 90% behind it: validation, processing, routing, exception handling, cross-system coordination. The difference: a Kore.ai conversation is where work pauses and waits for a human or additional automation to act. A Nexus agent's conversation is where the autonomous work begins.

We need a chatbot specifically for IT helpdesk. Which should we choose?

It depends on whether the conversation or the work is your bottleneck. If the use case is strictly answering employee questions about password resets, software access, and common IT issues, and the goal is conversational deflection, Kore.ai handles this well. But if the real cost is not the conversation, it is the work behind it (actually resetting the password, provisioning the access, updating the ticket, following up with the employee across systems), Nexus agents handle the full process. Most IT helpdesk cost sits in the 90%, not the 10%.

Kore.ai starts at $300K+ annually. How does Nexus pricing compare?

Nexus uses per-agent pricing tied to value delivered, not tiered licensing or per-seat fees. Every engagement starts with a 3-month POC so you see measurable results before committing to an annual contract. Orange generated $4M+ in incremental yearly revenue. Lambda projects $7M+ in annual value. The investment should be evaluated against the specific outcomes it delivers for your workflows.

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Worth exploring?

If your conversational AI handles the 10% well but the 90% behind it (the workflows, the cross-system coordination, the exception handling) still requires humans to manage, it might be worth seeing how enterprises have addressed that gap. Orange achieved 50% conversion improvement and $4M+ yearly revenue with agents that complete onboarding end-to-end, not just the onboarding conversation. A major European telecom freed 40% of support capacity by deploying agents that handle the work behind the conversations, not just the conversations themselves. Lambda uncovered $4B+ in pipeline with agents that monitor 12,000+ accounts.

Every engagement starts with a 3-month proof of concept tied to specific outcomes. A Forward Deployed Engineer embeds with your team from day one to tackle the 90% that software alone does not solve. You can exit anytime.

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