Stop discounting the trial users who would have paid anyway.
KaQuill predicts which trial users will convert in real time. It triggers in-app nudges, sales alerts, and email interventions only for the fence-sitters — so you keep margin on inevitable conversions and save the winnable ones.
Margin you burned discounting 23 trials who would have paid full price.
Time-based drip campaigns are flat. Your trial users aren't.
You discount the wrong users
Day-7 trial email fires at everyone equally. The users who were going to pay full price get the discount too — you train them to wait for it.
You lose the fence-sitters
The trials who would have converted with one well-timed nudge churn silently. No one knows they were close. They just don't show up on Day 14.
Your sales team chases ghosts
Reps spend hours on trials that were never going to pay. The intent signal is in the product usage data — no one is reading it.
Three steps. No model fine-tuning. No data team required.
Drop in the SDK
One JS snippet or a server-side API call. Send trial signups, feature events, plan views — anything you already track.
KaQuill watches and learns
Behavioral fingerprinting + reinforcement learning. Per trial, per moment, it predicts conversion probability with calibrated confidence.
Intervene precisely
In-app nudge, Slack alert, contextual email. Triggered only for fence-sitters, only when intervention has measurable lift.
See exactly how much margin your drip campaign burns each month.
For every discounted conversion, KaQuill computes the counterfactual: would this user have paid full price anyway? When the answer is yes, that's Ghost MRR — revenue your blanket discount strategy is destroying.
It's the number you didn't have. The one that makes the case for intent-based intervention impossible to argue with.
New to the metric? Read: What is Ghost MRR?
Not another rules engine wearing AI lipstick.
Agent, not rules
Userpilot and Appcues make you write the if/then logic by hand. KaQuill learns it — it predicts each trial's intent, adapts as it sees more outcomes, and decides whether to act at all instead of firing the same rule at everyone.
Counterfactual aware
Knows the difference between conversions it caused and conversions that would have happened anyway. No other PLG tool does this.
Action-layer agnostic
Trigger anything: in-app nudges, Slack messages, sales-team alerts, email, webhooks. The decisioner doesn't care what the action is.
Two lines of code. Ten minutes to live.
Server-side SDK for Node, Python, Ruby, and Go. Client-side pixel for browser events. Or just POST JSON to our universal endpoint.
import { Kaquill } from '@kaquill/client';
const kq = new Kaquill({ apiKey: process.env.KAQUILL_KEY });
// On trial signup
kq.identify(user.id, {
plan: 'trial',
trial_ends_at: '2026-06-23',
});
// On any meaningful product event
kq.track('feature_used', {
user_id: user.id,
feature: 'export_csv',
});Free to see the leak. Paid to plug it.
See your Ghost MRR. Read-only insights, no interventions.
- Up to 100 nudges per month
- Ghost MRR dashboard
- Conversion probability per trial
- Community support
Unlimited nudges. Cancel any time.
- Unlimited tracked trials
- In-app nudges + email triggers
- Slack alerts for high-intent trials
- All Free features included
- Email support
Save ~$200 vs monthly. Same unlimited nudges.
- Everything in Monthly
- Two months free vs monthly billing
- Priority support
- Locked-in pricing for the year
Need higher volume? Contact us for custom enterprise pricing.
Questions, answered.
How is this different from Userpilot, Appcues, or Pendo?+
Those are rules-based: you write if/then logic for tooltips. KaQuill is a continuously-learning agent that predicts conversion probability per user, per moment — and decides whether to intervene at all. The Ghost MRR metric also doesn't exist anywhere else.
What events do I need to send?+
At minimum: trial signup, plan change, and any 'meaningful' product action (feature use, document creation, anything that signals engagement). The richer your event schema, the better the model. We provide example schemas for common SaaS verticals.
How long until KaQuill starts being useful?+
Insights (Ghost MRR, conversion probability) appear after roughly 200 trial sessions. Live interventions activate after we've seen enough data to be confident — typically 2–3 weeks for a typical PLG SaaS. We're transparent about model confidence; you'll always see it.
Where does the intervention actually fire?+
Three places: (1) in your app, via a tiny client SDK that renders the nudge component you control; (2) in Slack, as a high-intent alert for your sales team; (3) via email, by triggering your existing transactional provider with the right payload at the right moment.
What's the data story? GDPR, etc.?+
Events are stored on EU infrastructure if you're EU-based. We never sell or share data. You own everything. Full DPA available before signup. Right to delete, right to export, hashed user identifiers — all standard.
Will this work for my specific SaaS?+
If you have a free trial or freemium plan and care about conversion, yes. We've designed KaQuill to be vertical-agnostic — same engine for project management tools, developer tools, AI tools, design tools. The model learns your specific buyer's behavior.
See what you're leaving on the table.
Free forever to see Ghost MRR. Upgrade only when you're ready to act on it.
Start free