Conversational AI and Support Bots

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The conversational AI landscape: what these platforms do and where they stop

Conversational AI platforms help enterprises automate customer and employee interactions through chatbots, virtual assistants, and voice agents. The category includes tools for customer support deflection, IT helpdesk self-service, HR FAQ bots, and contact center automation. Vendors like Kore.ai, Ada, Yellow.ai, Cognigy, and Moveworks have built real businesses here, serving thousands of enterprises with products that handle high-volume conversations across chat, voice, and messaging channels.

These platforms solve a genuine problem. When a customer asks "where is my order?" or an employee asks "how many vacation days do I have left?", a well-built chatbot can resolve that in seconds. That reduces ticket volume, cuts wait times, and frees human agents for harder work. Gartner, Forrester, and G2 all recognize this as a mature, valuable category.

Here is where the gap appears. 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, checking compliance, handling exceptions, routing edge cases across departments, and taking action in CRMs, ERPs, and downstream tools. A chatbot can ask the question and relay the answer. It cannot validate the data, decide what to do when something is off, or complete the work end-to-end.

Conversational AI platforms are designed around the conversation. Agents are designed around the work.

That is the core distinction. It is the reason enterprises evaluate Nexus alongside (or instead of) these platforms. Nexus is a solution, not just software: a platform for autonomous agents paired with Forward Deployed Engineers who embed with your team to ensure those agents deliver measurable business outcomes.

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Category comparison: Nexus vs. conversational AI platforms

Dimension	Moveworks (ServiceNow)	Kore.ai	Ada	Yellow.ai	Cognigy (NICE)	Wonderful	Nexus
What it is	IT/employee self-service assistant, now part of ServiceNow	Conversational AI platform for virtual assistants and chatbots	Customer service automation platform	CX and EX chatbot platform with 135+ languages	Contact center AI for voice and chat	AI agent platform for customer-facing interactions across voice, chat, email, and in-app	Autonomous agent platform + embedded engineering service
Primary scope	Employee-facing: IT, HR, facilities within ServiceNow ecosystem	Customer support, IT helpdesk, employee self-service	Customer service ticket deflection	Multilingual customer and employee conversations	Contact center voice and chat automation	Customer service only; agents resolve interactions autonomously across 30+ countries	Any department, any workflow: sales, support, compliance, HR, onboarding, operations
Core paradigm	Employee asks, AI answers or routes	Conversation-first with recent agent add-ons	Resolves support conversations within trained scope	Conversation-first across 135+ languages and 35+ channels	Voice-first with NLU and telephony integration	Agent-first within customer service; agents complete real work (update accounts, schedule technicians, process billing)	Work-first: the agent collects, validates, decides, and acts. Conversation is one channel, not the center of the architecture
Completes work autonomously?	Routes or resolves employee requests. The 90% of work behind those requests (validation, cross-system checks, exception handling) still requires humans or other tools	Handles conversations. The work behind the conversation (data validation, multi-step execution, exception routing) requires additional automation or humans	Resolves conversations within scope. Does not complete multi-step workflows across systems or handle the process logic behind the conversation	Automates the conversation layer. The backend work (validation, compliance, decision logic, action) requires separate systems	Automates conversations. The work that follows (cross-system execution, exception handling, compliance checks) still depends on downstream systems and humans	Yes, within customer service. Agents complete real work autonomously with 80%+ resolution rates. Does not extend beyond customer-facing interactions	Agents execute the full 90%: collect data across systems, validate it, make decisions, handle exceptions, route edge cases, and take action end-to-end
Handles exceptions?	Routes to human helpdesk. Exceptions in the work itself (data mismatches, policy conflicts) are not addressed	Bots follow dialog flows; escalate when off-script. Exceptions in backend processes are outside the platform's scope	Escalates to human agent; users report loops on unusual questions. No mechanism for handling process-level exceptions	Routes to human agents when chatbot capability is exceeded. Work-level exceptions (compliance flags, cross-system conflicts) are not within scope	Escalates when conversations go off-script. Exceptions in the underlying work still require human intervention	Within customer service scope, agents adapt to tone, speaker characteristics, and cultural context. Escalation for out-of-scope requests	Agents handle both conversation and work exceptions: adapt within guardrails, route edge cases with full context, escalate intelligently. No dead ends
Who builds and owns it	IT deploys and administers	IT or specialized bot-building teams with NLU expertise	Support teams configure conversation flows	CX and IT teams deploy conversational flows	IT and contact center teams configure flows and NLU training	Joint effort between Wonderful's embedded country teams and the client. Agent Builder creates agents autonomously	Business teams build and own agents across any department, supported by FDEs
Deployment speed	Varies by environment complexity	Weeks for basic bots; 6-18 months for complex enterprise scenarios	Months for full setup per user reviews	Weeks to months depending on flow complexity and language coverage	Weeks to months depending on complexity	Embedded country teams deploy alongside clients. No setup fees	Days to weeks. Orange deployed in 4 weeks. Lambda deployed in days
Service model	Software with standard enterprise support. You configure the conversation; the work behind it is your problem	Software platform with partner-driven services. Partners help with conversation design, not with the backend process work	Self-serve software with documentation. Your team handles both conversation configuration and any process work beyond it	Software platform with professional services available. Services focus on conversation flows, not on the cross-system work that follows	Software with enterprise support, onboarding, and Cognigy Academy. Focus is on contact center conversation setup, not end-to-end process delivery	"Local by design": full-stack country teams (local CTOs, engineers, GMs) embedded alongside clients. 30+ countries	Platform + Forward Deployed Engineers embedded with your team. FDEs own both the conversation layer and the 90% of work behind it. Change management. Ongoing optimization
Integrations	Strong within ServiceNow ecosystem; Jira, Okta, Active Directory, Workday	250+ pre-built connectors for CRMs and ITSM tools	Support and CX tools: Zendesk, Salesforce Service Cloud, helpdesk platforms	150+ integrations for CRM, support platforms, and messaging channels	Telephony, webchat, messaging; focused on contact center stack	Customer service systems; omnichannel (voice, chat, email, in-app). Backend integration details limited	4,000+ integrations: CRMs, ERPs, communication tools, custom APIs. System-agnostic by design
Pricing model	Per-employee licensing ($100-200/employee/year); shifting toward bundled ServiceNow SKUs	Enterprise licensing; deployments typically $300K+ annually	Resolution-based: cost scales with conversation volume resolved	Usage-based tied to conversation volume and channels	Consumption-based per conversation/interaction with separate voice, chat, and LLM charges	Consumption-based. No setup fees. Available on Azure Marketplace	Per-agent, tied to value delivered. Not headcount, not conversation volume
Vendor independence	Now owned by ServiceNow. Roadmap serves ServiceNow ecosystem	Independent (for now)	Independent	Independent	Acquired by NICE for $955M. Now part of CXone Mpower platform	Independent. $134M raised from Index Ventures, Bessemer, Insight Partners. ~$700M valuation	Independent. Backed by Y Combinator and General Catalyst. System-agnostic
Security and compliance	SOC 2 Type II, ISO 27001	SOC 2 Type II, ISO 27001, HIPAA, GDPR; on-premise option available	SOC 2 Type II, GDPR, HIPAA	SOC 2 Type II, ISO 27001, GDPR, HIPAA	SOC 2 Type II, ISO 27001, GDPR, HIPAA	Basic enterprise security. No ISO certifications mentioned. Available on Azure Marketplace	SOC 2 Type II, ISO 27001, ISO 42001, GDPR. Full audit trails, decision traceability

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Quick decision guide

The question to ask: is the conversation the whole problem, or just the visible surface of a deeper process?

Choose a conversational AI platform if:

• The conversation IS the problem. Your challenge is answering FAQs, deflecting support tickets, or routing questions to the right team, and no complex work needs to happen behind those conversations
• The value you need starts and ends with the conversation itself: answering questions, collecting information, handing off to a human
• You have dedicated bot-building or CX teams who can configure, train, and maintain conversational flows over time
• You are deeply committed to a specific ecosystem (ServiceNow for Moveworks, NICE CXone for Cognigy) and want native integration within that stack
• The work behind the conversation (validation, compliance, multi-system coordination) is already handled by existing systems or human teams, and you do not need AI to touch it

Choose Nexus if:

• The conversation is only 10% of your problem. The real challenge is the work behind it: collecting data from multiple systems, validating it, making decisions, handling exceptions, routing edge cases, and taking action
• You need AI that completes entire business workflows, not just the conversation layer on top of them
• Your use cases span multiple departments: sales, marketing, customer support, HR, compliance, operations
• You need customer-facing and internal AI on one platform, not separate tools for each
• You want Forward Deployed Engineers embedded with your team to own the outcome, not just software with documentation and a support ticket queue
• Business teams (not IT or specialized bot builders) need to own and operate the AI
• You have tried conversational AI and found that the hard part was never the conversation. It was everything that needed to happen behind it

Specific platform guidance:

• Moveworks is strongest for IT self-service within the ServiceNow ecosystem. Good at the conversation layer for employee requests. The work behind those requests (cross-system validation, multi-step provisioning, exception handling) is not within scope. Consider whether ServiceNow lock-in aligns with your long-term strategy.
• Kore.ai is a Gartner Leader with deep conversational AI features. Best for teams with NLU expertise and longer implementation timelines. Its new Agent Platform adds orchestration, but the architecture still centers on the conversation, not the work behind it.
• Ada is focused on customer service ticket deflection. Strong at the 10% (the conversation). If the value you need is in the 90% behind it (process execution, cross-system workflows), Ada does not reach there. Resolution-based pricing can also become expensive at scale.
• Yellow.ai has the strongest multilingual coverage (135+ languages) and deep APAC expertise. Best for high-volume multilingual conversations. Like other platforms in this category, it handles the conversation layer but not the backend work.
• Cognigy is the strongest for voice AI and contact center automation. Now part of NICE. Excellent at voice-based conversations, but the work that follows those conversations (fulfillment, validation, cross-system action) requires separate tooling.
• Wonderful is genuinely different from other platforms in this category. Unlike traditional chatbot vendors, Wonderful builds agents that complete real work within customer service: updating accounts, scheduling technicians, processing billing changes. Their "local by design" model with embedded country teams and 80%+ autonomous resolution rates is impressive. If your AI needs are concentrated in customer-facing interactions and cultural fluency across markets is critical, Wonderful has built a focused, strong product in under two years. The limitation is scope: Wonderful does not extend beyond customer service. If you need agents across sales, compliance, HR, or operations, you need a platform built for that.

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Detailed comparisons

Comparison	One-line summary
Nexus vs Moveworks (ServiceNow)	IT employee self-service assistant (now owned by ServiceNow) that handles the conversation vs. autonomous agents and FDEs that handle the work behind it
Nexus vs Kore.ai	Gartner Leader in conversational AI (the 10%) vs. autonomous agents that complete the 90% of work behind those conversations
Nexus vs Ada	Customer service ticket deflection (the conversation layer) vs. agents that complete multi-step processes across departments and systems
Nexus vs Yellow.ai	Multilingual CX and EX chatbot platform (135+ languages) for the conversation vs. autonomous agents that handle the cross-system work behind it
Nexus vs Cognigy (NICE)	Contact center voice and chat AI (now part of NICE CXone) for conversation automation vs. agents that complete the full workflow, not just the call
Nexus vs Wonderful	Genuinely agentic customer service AI (80%+ resolution, 30+ countries) vs. autonomous agents across every department with enterprise-grade governance

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The pattern enterprises describe

Orange Group, a multi-billion euro telecom with 120,000+ employees, did not need a chatbot. They needed autonomous agents that complete customer onboarding end-to-end across multiple European markets. The onboarding process involves collecting customer data, validating it against eligibility systems, checking device compatibility, verifying compliance requirements per market, handling exceptions (mismatched addresses, failed credit checks, edge-case device configurations), and routing decisions across multiple backend systems. That is the 90%.

A conversational AI platform could have handled the front-end chat: "What device would you like? What plan interests you?" That is the 10%.

Orange's business team (not engineering) built onboarding agents with Nexus, supported by Forward Deployed Engineers who embedded with the team to handle both the conversation layer and the complex work behind it. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue. 100% team adoption.

The value did not come from the conversation. It came from everything behind it.

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Worth exploring?

If your team has deployed conversational AI and found that the conversation was the easy part, if the real bottleneck is the 90% of work behind it (data collection across systems, validation, decision-making, exception handling, cross-department routing, and action), it might be worth seeing what agents designed around the work look like in practice. Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable outcomes. Forward Deployed Engineers embed with your team from day one. You see the value before you commit, and you can exit anytime.

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Related categories

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• Workflow Automation -- Zapier, Workato, UiPath, n8n: rule-based automation vs. intelligent agents that adapt
• Enterprise AI Platforms -- Direct competitors: Glean, Writer, Dify, Relevance AI
• Developer Frameworks -- LangGraph, CrewAI, AutoGen: should engineers build from scratch or should business teams deploy in weeks?
• Back to all comparisons -->