Renewal Risk Intelligence Engine
A three-agent n8n pipeline that ingests an at-risk customer account, retrieves context from a Pinecone vector database, and produces a fully styled HTML case file with risk assessment, intervention plan, and CSM-team recommendation — in under 30 seconds.
In the meantime, the architecture and case-file output are documented below. To see the build in action live, book a 15-minute walkthrough with Sean.
CS teams spot risk too late.
By the time a CSM realizes a renewal is in trouble, the champion has often already left, the economic buyer has gone dark, and the paid-but-unused modules have quietly become the de facto reason the customer is shopping competitors. Risk signals are everywhere — in support tickets, usage data, billing records, sentiment, who's not showing up to QBRs — but they're scattered across systems and read by no one in aggregate. Most "early warning" tools surface lagging indicators (NPS drops, support ticket spikes) rather than the *contradictions* and *silences* that actually predict churn.
Three agents. One case file.
The pipeline runs in sequence. Each agent has a single job, a single output schema, and a single Pinecone namespace to pull from. The final node assembles every agent's output into a styled HTML case file the CS leader can open in any browser.
{account_name, account_id} via POST.accounts, historical-data, and team namespaces. Returns concatenated text payloads for each downstream agent.What the CS leader sees.
The case file is a single self-contained HTML report. Below: live screenshots of the executed pipeline output.
The case file includes a risk score (1-10), urgency tier, hidden-relationship alerts, top risks with cited evidence, detected contradictions (e.g., positive NPS from IT while the economic buyer goes dark), silence signals, unadopted-module ARR at risk, historical pattern match with save-probability estimates, the intervention plan, and the recommended CSM team configuration.
Hidden relationships beat skill scores.
The single most important design decision in this build: Agent 3 is instructed to scan every CSM profile for prior relationships with the at-risk contacts before evaluating any skill or bandwidth score. A CSM who previously worked at a company where the now-unreachable economic buyer was a customer can re-open the door in a way no amount of "skills match" can.
This reflects how senior CS leaders actually triage red accounts in practice — they ask, "who do we know inside?" first, then optimize for skill and bandwidth second. Most automated CSM-matching tools invert this and produce mathematically correct but practically useless assignments.
Book 15 minutes.
The build is a working demo. The fastest way to evaluate it is to watch it execute end-to-end on a real account scenario. Pick a 30-minute Calendly slot — first 15 minutes is the demo, second 15 is whatever you want to discuss about it.