Every go-to-market team has questions its dashboards were never built to answer. Which accounts should sales call this morning? Which campaigns are moving pipeline, and which are only collecting clicks? Which customers have gone quiet three months before renewal? Too often the answer is an export, a spreadsheet and a lost afternoon, and by the time it is ready the question has moved on.
N.Rich is launching N.Rich AI, the buildable ABM platform for go-to-market teams. Describe what the team needs in a sentence, and N.Rich AI builds it as an app on your own account data. When the question changes, the app changes with it. The limit is no longer what a dashboard was designed to show. It is what the team can think to ask.
N.Rich AI is available now.
N.Rich AI is the buildable ABM platform from N.Rich. Teams describe the tool they need, and N.Rich AI builds it as an app on the account data N.Rich already collects. Everything the platform already does keeps doing its job: intent data shows which accounts are in-market, the DSP puts ads in front of them, and account-level engagement ties both to pipeline. N.Rich AI works on that same data and lets teams build whatever they need on top of it, with no export step.
That is what buildable means. A configurable platform offers the views its vendor decided you need. A buildable platform starts from the job your team has, described in your team’s words.
Underneath, every app reads one governed data layer. Ad engagement, website visits, intent, LinkedIn campaigns and CRM pipeline are unified at the account level and read through a semantic layer tuned for go-to-market, so every app starts from the same numbers.
Apps on N.Rich AI start from an idea about how go-to-market should work. The app is what makes the idea practical.
Take a common one: the accounts worth a rep’s time this week are the ones where several signals overlap, not the ones making the most noise on a single channel. Most teams agree with that, and few can act on it, because the signals live in different tools. High-Intent Accounts, one of the apps built on N.Rich AI, puts them on one screen: the ICP accounts visiting pricing or competitor comparison pages, how their activity is trending, and a suggested next step for the rep.
Then the team changes its mind. Enterprise deals turn out to start on the competitor comparison pages rather than on pricing, so the demand generation lead asks the app, in plain language, to weight those visits higher, and N.Rich AI rebuilds it. The EMEA team wants the same logic on its own ICP tiers, so it clones the app and adjusts it. Sales wants it where they already work, so the app is published into HubSpot, where it opens on the company record with that account already selected.
An N.Rich AI app open natively within the HubSpot UI. Company names are illustrative.
No app on N.Rich AI is finished. It changes when the motion does.
The same pattern works well beyond intent. A quarterly performance report that used to be assembled by hand can run as an app the team opens whenever it needs it. Tier 1 Churn Risk Monitor flags high-value customers whose engagement is falling before the renewal call. The Closed Won Deal Analyzer reconstructs every won deal’s journey, including the evaluation that happened before the opportunity existed.
N.Rich AI is built for B2B revenue teams running account-based go-to-market. Each role reaches the same account data through its own question.
For a decade, go-to-market platforms have been dashboards. Each one answers the questions its designers anticipated. The questions a team actually has this week, about this campaign, this segment or this renewal, fall outside them, and the dashboards become places people stop opening.
Each tool in the stack does one part of the job. A CRM stores the data. A dashboard displays it. A general-purpose chatbot answers one question and then forgets the context. The work in between, deciding which accounts matter this week and who should do what about them, still happens in spreadsheets, Slack threads and someone’s memory.
More dashboards do not fix that. Each new view shows the same gap from a different angle.
With traditional SaaS, the UI is what it is and the data is what it is. The alternative is building everything yourself. Most revenue teams sit in the middle, and that’s where N.Rich AI fits.
George Storm, CRO, N.Rich
Building software has stopped being the bottleneck. A tool that used to take a sprint of engineering time now takes a well-described sentence, and go-to-market teams are already building their own with general AI assistants.
Those tools hit the same wall. A general assistant can read a CRM, but it has not seen which accounts are researching your category, which of them engaged with last month’s ads, or which visited your pricing page without filling in a form. Without that context, the app looks right and answers wrong.
The build step is now the easy part. The data underneath is the hard part, and it is the part N.Rich has spent ten years on: unifying account-level engagement, intent and CRM data for more than 250 go-to-market teams. That is the case for AI-native ABM: the build layer only works when it sits on data that already knows your accounts.
Most companies building something with AI end up inside an existing framework, where you can bolt it on to create forms or a dashboard and call that "The magic of AI". With N.Rich AI you can create anything, and that reaches well beyond marketing.
A marketer can show the CEO what marketing did in the last 90 days. A rep can build the answer to a prospect’s problem during the conversation. And leadership gets a view of the business it can trust, on top of the systems it already has.
Markus Ståhlberg, Co-founder and CEO, N.Rich
An app on N.Rich AI today answers a question and puts the answer where the team works. The next step is for the same layer to act on it.
That is the direction: N.Rich AI growing from the place where teams build the tools they need into the orchestration layer for the whole go-to-market motion. Signals come in, decisions get made, and actions on accounts, budget and people go out, all through one governed account model that the team and its AI agents share, and that learns over time from how each team works.
The first steps are already in the product. Apps publish into HubSpot and open on the company record, and Salesforce is coming soon. From there the layer keeps widening, with more of the data a revenue team runs on, context that teams add about their own business, a library of proven apps to start from, and apps that bring the answer to the team before anyone thinks to ask.
Every step is held to the same test as the first app: does it help someone decide what to do next, or add another chart?
The fastest way in is the interactive demo on nrich.io/platform/ai, with no form to fill in. To see N.Rich AI on your own account data, book a demo.
N.Rich is an account-based go-to-market platform used by more than 250 go-to-market teams. It finds the accounts showing buying intent, puts ads in front of them and shows sales when to move. N.Rich is recognised in the 2025 Gartner® Magic Quadrant™ for ABM Platforms and has been in the market for ten years. Learn more at nrich.io.
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