I built a churn prediction API because my friend couldn't afford the $40K alternative
Last year, a friend who runs a 12-person SaaS company told me he'd lost $14K in monthly revenue in one quarter. He didn't know why. His team of two customer success people were manually combing through 400 accounts in a spreadsheet trying to figure out who was unhappy.
I asked: "Why not use a churn prediction tool?"
"Gainsight quoted us $40K per year," he said. "We can't afford that."
So I built him a script, a Python notebook that scored his customers based on login frequency, support tickets, payment history, and NPS scores. It flagged 34 accounts as high risk. His CS team called them that week. They saved 9 of them. Net retention went up 4% the following month.
That script became RevAI.
I put it on RapidAPI so any SaaS founder can call one endpoint and get the same thing. Every prediction explains exactly WHY that customer is at risk — "No login in 18 days (+25), Payment failure in last 90 days (+12), Month-to-month contract (+10)." Not a black-box score. An actionable list.
It also benchmarks your churn against anonymized data from other companies using the tool, so you know whether a 6% monthly churn rate is normal for your segment or a red flag.
It's free for 100 predictions. No credit card required. I built this because churn prediction shouldn't cost $40K.
Pricing a $40K problem at accessible levels is a real wedge. Is your accuracy comparable to the enterprise tools or is there a meaningful gap you're still closing?
The strongest part of this story is buried: the CS team saved 9 of 34 accounts. The value was the phone calls, not the score. A raw API hands the hard part, turning a prediction into action, back to the customer, and most small SaaS teams never build that layer. They check the number once and move on.
So I would sell the outcome, not the model. A Monday email that says here are your 12 at-risk accounts this week, why each one, and what to do, is something a 12-person SaaS will pay for, because it replaces the exact spreadsheet your friend's team was drowning in. Keep the API underneath for technical buyers, but lead with the done-for-you version.
One honest flag: the benchmarking feature only gets useful at scale, and at zero revenue you do not have the network data yet to make those comparisons real. I would hold it back until you do. What is your free-to-paid conversion on the 100-prediction tier so far?
This is exactly the right problem to solve. The gap between "spreadsheet CS team" and "Gainsight enterprise contract" is where most SaaS companies actually live, and nobody serves them well.
One thing I'd think about early: the API positioning might slow adoption. Most SaaS founders who can't afford Gainsight also can't integrate an API themselves. They want a dashboard, not an endpoint. Have you considered a simple UI layer on top so non-technical founders can paste in a CSV and get results immediately?
The benchmark feature is genuinely interesting as a growth mechanic too. If people share their churn benchmark publicly it could drive organic distribution.
I've scaled SaaS acquisition funnels before and the "explain why" angle is your strongest hook. Lead with that everywhere.
smart move starting from a real problem with a known price anchor ($40K Gainsight quote). that's basically your entire sales pitch already written for you.
the 34 accounts flagged → 9 saved is a strong proof point. I'd lean hard on that specific case study in your outreach. "we saved one team 9 accounts in a week" is infinitely more compelling than any feature description.
one thing to watch: the benchmarking against anonymized data is powerful but it also means your product gets more valuable as more companies use it. that's a real network effect if you can get enough users on it early.
This is a much stronger starting point than a generic “AI churn tool” idea.
The useful part is that the problem already had a clear anchor: a small SaaS team losing revenue, manually reviewing accounts, and an existing solution priced at $40K/year.
That makes the pain feel concrete instead of theoretical.
I also like that the output explains the risk factors instead of just giving a black-box score. For small teams, “why this account is at risk” is probably more useful than the prediction itself.
Curious how you’re thinking about distribution from here. Are you expecting RapidAPI search to drive users, or are you planning to reach small SaaS founders directly?
The explainability piece is what makes this actually useful. A black-box churn score is hard to act on — "No login in 18 days" gives the CS team something to say on the call.
Curious how it handles businesses where "login frequency" isn't a meaningful signal — service businesses, for example, where the owner logs in once a week but the business is perfectly healthy.
This is a strong wedge because the pain is not really “local businesses need another website builder.” It is that the whole customer journey is split across too many disconnected tools, so owners end up managing operations manually even after buying software.
I would probably keep testing “merchant OS,” but make the first promise more concrete: website, booking, CRM, invoices, reviews, and follow-up in one connected operating layer for service businesses. That makes the category feel bigger without becoming vague.
The naming is worth thinking about early too. Helm is clean, but it may pull toward navigation/control rather than local-business workflow ownership. If this becomes the operating system for service merchants, Xevoa .com would carry the product more naturally as a broader workflow/platform brand before customers, docs, and assets lock around the current name.