Atlys is an AI-native visa processing startup that simplifies cross-border travel by automating the end-to-end visa application process — from document collection and verification to submission and real-time status tracking.
This case study documents a SQL-based analysis of 8,514 real drop-off events across this funnel, focused on identifying where users abandon, how severely, and what the product team can do about it.
The funnel has multiple steps where users start an application and never finish it. Without understanding which steps cause the most abandonment — and what kind of users are being lost — it is impossible to prioritise product fixes effectively.
The specific questions this analysis set out to answer:
| Table | Description | Rows |
|---|---|---|
| funnel_dropoffs | Raw user-level drop-off events with step, device, destination, city, time spent | 8,514 |
| mom_clp_apply | Monthly CLP → Apply Now conversion | 10 |
| mom_clp_txn | Monthly CLP → Transaction conversion | 10 |
| mom_travel_dates | Monthly Travel Dates → Selfie conversion | 8 |
| mom_selfie_passport | Monthly Selfie → Passport conversion | 7 |
| mom_passport_review | Monthly Passport → Review conversion | 7 |
| mom_review_checkout | Monthly Review → Checkout conversion | 7 |
| mom_checkout_txn | Monthly Checkout → Transaction conversion | 6 |
Tool: PostgreSQL via Supabase Unique users: 4,799
CLP → Apply Now → Travel Dates → Selfie → Passport → Application Review → Checkout → Transaction