✨ Nue AI

meet nue ai preview availability nue ai is currently in preview if you’re interested in participating, please contact your nue account executive to sign up as a pilot customer this will give your organization early access to nue ai capabilities, including mcp servers, agentic services, and chatbot experiences, while helping shape future innovations nue ai brings trustworthy, context aware intelligence to revenue operations by combining nue’s structured data and apis with model agnostic orchestration and agentic experiences , it empowers ai assistants to answer complex questions, summarize records across multiple transactions, and take guardrailed actions spanning quoting, subscriptions, invoicing, and more instead of relying on unstructured prompts or disconnected tools, nue ai gives teams a secure, powerful way to turn live revenue data into intelligent, actionable workflows —bridging human and ai collaboration across the entire revenue lifecycle context aware ai for revenue operations the nue ai stack the nue ai stack is the foundation that powers intelligent, context aware automation across your entire revenue lifecycle it connects nue’s structured data , salesforce objects , and ai agents through a modular architecture built on mcp (model context protocol) servers and agentic services — enabling assistants to reason, act, and collaborate alongside your teams nue apis the source of truth at the base of the stack are nue’s core apis , which provide structured, reliable data across key revenue domains lifecycle manager – manages customer, subscription, and order lifecycles billing – handles invoicing, credits, payments, and collections self service – powers plg and direct sales experiences these apis inject revops context —like quotes, orders, product data, and billing events—into the ai layer in real time nue mcp servers the orchestration layer next, nue mcp servers standardize access to business logic and data they expose secure, structured tools—such as lifecycle manager, pricing engine, and self service—that ai agents can call dynamically, without embedding business rules directly into prompts or uis this layer is model agnostic and can be used across multiple ai providers (e g , gpt 5, claude, gemini), allowing organizations to reuse the same tools everywhere unified ai agent + human ecosystem once mcp servers are in place, a unified ai agent can orchestrate data and tools from multiple systems and work alongside humans to deliver powerful outcomes through nue agentic services , ai capabilities surface in intuitive, chat based interfaces this creates a human + ai ecosystem , where teams work faster and smarter using shared data and workflows not separate silos nue ai stack why mcp and agentic services matter ai in revops has historically struggled to move beyond chatbots and dashboards data is siloed across billing, support, crm, and product systems, and each ai application has to be custom wired mcp changes this model context protocol (mcp) provides a standardized way for ai models to securely discover and use business tools, while agentic services orchestrate these tools to execute complex tasks imagine preparing for a renewal conversation instead of checking multiple systems, you simply ask your ai assistant summarize smart revenue's account anything i should be aware of before a renewal conversation? within seconds, the assistant surfaces a comprehensive, grounded summary $24,410 in unpaid invoices and $2,930 in refunds and credits 2 open escalations with support 3 jira tickets tied to the next release renewal in 3 months with a high value deal in pipeline declining csat trends in intercom recommended action finance follow up + pre renewal check in behind the scenes, the assistant orchestrates multiple mcp servers — stripe for payments, atlassian for engineering, intercom for support, and nue for revenue and billing — to deliver a single intelligent response this ✨ future of ai in revops✨ is modular, model agnostic, and grounded grounded ai answers are based on structured, live data not hallucinations reusable one set of tools works across multiple models and uis fast actions happen in seconds, not hours scalable new systems plug in easily through mcp, without retraining models best of all, there is no need to wrangle and sync data across multiple saas systems into a separate data warehouse anymore