What it unlocks
Saves four to six hours a week per CSM on Monday triage. CS Ops gets another eight to twelve hours back on cohort analysis.
Catches at-risk onboardings before day 7 instead of day 30. Day-30 churn typically drops by 15 to 20%.
Replaces $15-30k a year of onboarding tooling like Pendo Onboarding, Userpilot, and Appcues, plus a quarter of in-app guidance engineering work.
What we mean by an AI agent
An AI agent is software that runs continuously in the background, reads across the tools where your customer data lives, and brings you the picture you’d otherwise have to assemble yourself. Legacy SaaS works the other way around: someone logs in, clicks through dashboards, and pulls the picture together by hand. That difference changes the math on what a small team can cover. Onboarding is one of the first places it shows up, because the reasons a new account stalls are scattered across product, CRM, support, and billing.
A Monday morning that ends at 9:15
The CS Manager opens her week at 9am. Forty new customers started using the product this month, and they’re all in their first 30 days. By the old math, she’d have time to review maybe eight before her 10am meeting.
To review one account properly, she’d open eight tabs. Mixpanel for activation, Salesforce for the account owner, Intercom for support, billing for plan tier. Five accounts an hour on a good day. The other 32 either self-activated or stalled silently. The team usually didn’t find out which until day 30.
She doesn’t do that anymore. The agent ran across all forty accounts overnight. Thirty-one are tracking the activation pattern that historically converts at her company. Six are showing day-7 drop-off. Three are sitting in a state nobody has seen before, flagged with a note: “new pattern, suggest reviewing.”
By 9:15 she’s on the six pre-churn accounts with the context already laid out. The 10am meeting moves from “what’s happening across the book” to “what are we doing about it.”
What you stop paying for
For most B2B SaaS companies between 50 and 500 employees, a few stacks of legacy spend become redundant once the agent is running.
Onboarding tools (Pendo Onboarding, Appcues, Userpilot, Userflow) run $15-30k a year. They guide users in-app, which is useful, but they don’t read across CRM, support, or billing. They tell you what someone is clicking on, not who needs help. Custom in-app guidance engineering, the kind teams build when they outgrow the off-the-shelf tools, costs about a quarter of platform-team time, $40-100k loaded.
Then there’s the labor. Onboarding triage runs four to six hours a week per CSM, plus another eight to twelve hours from CS Ops on cohort review. On a five-person team, that’s about one full FTE of effort, every week, on work that’s mostly mechanical. And the day-30 save campaigns most teams run? Most of that spend exists because the risk wasn’t caught at day 7. Catch it earlier and the campaigns shrink.
Net replaced cost lands at $20-40k a year direct, plus the engineering quarter, plus an FTE of triage labor. Real money you can put toward new product work or expansion.
The math that changes
Take a CSM onboarding 40 new accounts a month. The old way meant 30 minutes of triage per account, with maybe eight of those 40 reviewed deeply each week. The other 32 self-activated or stalled silently. The team often only found out which when day 30 rolled around.
With the agent running on all 40, the CSM spends about five minutes per flagged account, on the six to ten flags that come up in a day. That’s four to six hours back per CSM per week. On a five-person team, 20 to 30 hours back. Roughly a meaningful slice of an extra CSM, no new hire.
For the Head of CS, day-30 churn typically drops by 15 to 20% because at-risk customers get caught at day 7 instead of day 30. Activation rate climbs by 10 to 15% because the intervention window widens by three weeks. And whichever in-app onboarding tool is running today (Pendo Onboarding, Userpilot, Appcues) usually gets dropped at the next renewal. The cross-stack reasoning makes the in-app guidance feel half-blind.
How the agent actually works
What the agent reads on Monday morning isn’t complicated.
Activation events from product analytics, compared against the events that historically correlated with conversion at your company. Champion engagement signals from CRM, calendar, and email. Support ticket volume, sentiment, and topic. Configuration completeness against the steps your fastest-converting accounts had finished by this stage.
The cross-signal reasoning is the part siloed onboarding tools can’t do. Same activation numbers can mean different things. An account with normal activation but support friction and an absent champion needs a different recommendation than an account with low activation but a present champion working through configuration. Same surface signal, different recommendation.
Underneath, the agent reads from the agentic customer layer. The layer connects to CRM (Salesforce, HubSpot), product analytics (Mixpanel, Amplitude, Heap), support (Intercom, Zendesk), and billing (Stripe, Chargebee), and resolves them into one canonical record per account. Provenance on every signal, with continuous updates instead of yesterday’s snapshot. If you’ve already built onboarding agents in Claude or OpenAI, point them at the same layer over MCP. They read the same canonical customer the pre-built agent reads.
Example output
A real account, anonymized. The agent’s day-14 summary on Account A:
Account A. Flag: pre-churn pattern (day 14)
Activation: 60% of feature usage benchmark for converting accounts at day 14. Trajectory below threshold.
Champion: Head of Operations last logged in 6 days ago. Calendar shows new team standup she’s no longer attending.
Support: Three tickets in 7 days. Topics: Stripe integration, SSO, user permissions. Volume up 200% vs first week.
Configuration: SSO setup incomplete. Two of four integrations not connected. Expected complete by day 10.
Recommendation: Outreach this week. Confirm Head of Ops involvement, offer SSO session, escalate Stripe friction. Probability of conversion at current trajectory: low.
Sources: [Mixpanel events], [HubSpot record], [Intercom conversations], [internal config tracking].
The CSM acts on the recommendation in 15 minutes. Before the agent was running, this account would have been one of the 32 she didn’t have time to look at this week.
Who this is for
This is built for the Head of CS, Head of Revenue, or RevOps leader at a B2B SaaS company in the 50 to 500 employee range, running three or more customer-facing tools. If your CSMs cover 30 or more accounts each and Monday triage has started to break down, you’ve felt this. If you’ve tried single-tool onboarding software and noticed it doesn’t read across the rest of the stack, you’ve felt the limit too.
If you’re a 10-person team, manual triage is faster. If you’re a 5,000-person enterprise, the conversation is the enterprise plan.
Frequently asked questions
How is this different from Pendo Onboarding, Userpilot, or Appcues?
Those tools guide users inside your product. They don’t read across CRM, support, billing, or product analytics to flag who’s actually stalled. Most teams keep one of those tools for in-app guidance and use Sento for the cross-stack reasoning, then drop the duplicate analytics tier.
How long does setup take?
Sixty to ninety minutes to connect sources. The agent runs on every new account from the moment sources are connected. First useful output the same day.
Will this replace my CSM team?
No. The watching becomes free; the deciding stays with the team. Most CS teams find their first month with the agent is the most productive month they’ve had, because their meetings shift from “what’s happening” to “what are we doing about it.”
Can I bring my own agent instead?
Yes. The same layer exposes over MCP. Build the agent in Claude, OpenAI, LangChain, or your own framework, and point it at the layer.
Ready to see the agent on your data?
Join the waitlist. Sento is in early access with B2B SaaS companies between 20 and 500 employees. Free during early access.
