kipt

Onboarding that senses churn and reacts.

September 17, 2026 Kipt - Onboarding that reacts in real-time when SaaS users are about to give up

For the first five years of my first SaaS, my onboarding strategy was practically nonexistent. The dashboard was an empty state, and the only guidance a new user received was a generic welcome email pointing to our documentation.

Eventually, I built a standard checklist anchored to the top of the screen. Churn improved a little bit, but I was still flying completely blind. By the time a user showed up in my metrics as churned, they were already long gone.

After answering 15 years of support tickets, a painful truth clicked for me: the users who get stuck during onboarding don’t ask for help. They just leave.

Timing is everything in activation. If you aren't there to help a user the exact moment they stumble, you might lose them forever.

That is why I am building Kipt.

It will be a visual onboarding flow builder with a critical difference: it will give your onboarding a pulse. In addition to displaying static elements, Kipt will monitor real-time user behavior—like sudden inactivity, abandoned input fields, or exit intent. When a stall signal triggers, Kipt will automatically step in with an intelligent, context-aware nudge or re-engagement email to rescue the signup right when it matters most.

I have officially opened up the early-access waitlist, and I’m going to be building this entirely in public with a new update every week.

I just published the backstory of my first SaaS, and how 15 years of support shaped this philosophy. You can read the full first post on my blog here:

👉 kipt.ai/blog

I'd love to know: How do you currently track if someone stalls out during your app's signup flow? Let's chat in the comments!

Matthew B.

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The checklist already moved churn, which makes the pulse thesis testable. Have you identified which early behavior most reliably predicts a user will abandon onboarding?

Aryan Sinh

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2 days ago
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  Hi Aryan: No. I don't have customers yet, so I don't have the data. Anything I said here would be a guess dressed up as an answer. What I did do was record everything needed to answer it later. Every signal that fires, every nudge that runs, every one the guard rails hold back and why, and how it ended — finished, dismissed, gave up. So the question is answerable the moment there's real traffic behind it. My guess, as a guess: hitting a wall beats going quiet. Quiet is ambiguous — reading, another tab, someone's kid walked in. I already ignore time when the tab isn't visible. Clicking a button that can't work yet isn't ambiguous. That's someone trying and failing. One thing I'd flag before anyone leans on it: the held-back nudges look like a control group and they're not a clean one. That's what the A/B side is for. First real customer settles it. I'll post what comes back, even if I'm wrong.
  
  Matthew B.
  
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  2 days ago
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    The instrumentation makes the hypothesis genuinely testable once real traffic arrives. What’s the best email to reach you on?
    
    Aryan Sinh
    
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    2 days ago
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      matthew@kipt.ai
      
      Matthew B.
      
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      2 days ago
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        Thanks! I’ve just sent it over. Looking forward to hearing your thoughts whenever you have a chance.
        
        Aryan Sinh
        
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        2 days ago
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As co-founder of a privacy-first personal-data app, I see a tension here. The moment when someone hesitates is exactly when help would be useful—but closely monitoring that hesitation can undermine the trust the onboarding is meant to create. We do not use tracking, so we have to learn through voluntary feedback, support questions and the steps people consciously choose to share with us. Can Kipt provide useful help without sending detailed behavioural information outside the user’s product or at least make that monitoring completely visible and optional?

sveta_syndiary

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2 days ago
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  Thanks for the reply. That's a fair question. Detection runs in the browser. Idle timers, pointer movement, the "typed then stopped" detector — none of that gets sent anywhere. What hits my server is the conclusion: stalled 92s on "Connect a data source" (median 41s). What never leaves the page: keystrokes or field contents (I record that someone typed and stopped, never what), pointer positions, page content, query strings. No cookies. I don't store IP or user agent. What does leave: which step they're on, how long it took, which checklist items they ticked, which nudges fired or got held back, route changes as paths, plus anything you send with track(). Raw events are deleted after 90 days — what's left is per-step counts, not per-person trails. Making it visible is the easy part. Every nudge carries a plain-English reason for why it fired, and kipt('on', fn) hands your page every event as it's recorded — log it, show it to the user, or drop it. If it helps, there's a live example at https://kipt.ai/demo — the panel beside it shows every event Kipt records as it happens, which is the transparency question answered. What I don't have (yet): self-hosting, or anything end-user-facing for consent. So right now, if you can't send step-level progress to a third party, Kipt isn't for you. A local-only mode is doable — static manifest, progress in localStorage, nothing posted. You'd lose the adaptive timing and A/B, since both need pooled data. But say the word and you'd be the reason I build it and put it into the product. :)
  
  Matthew B.
  
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  2 days ago
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