Not an analytics dashboard. A detection and recovery system that finds what Shopify and Stripe operators are losing at checkout, ranked by actual recovery potential.
Product identifier
CheckoutLeak
Revenue Leak Detection Platform
Scans Shopify and Stripe for hidden revenue losses
Ranks findings by monthly recovery potential
Built for operators running at scale
Client
CheckoutLeak
Engagement
Full product design and build
Category
Revenue intelligence
Scope
Website, backend, and core detection engine
Overview
CheckoutLeak is a revenue leak detection platform built for Shopify and Stripe operators. It scans checkout and billing environments to surface hidden losses, identifies the root cause of each issue, and ranks findings by monthly recovery potential.
Standard reports show what happened. CheckoutLeak shows what is being lost and why, across checkout friction, payment method gaps, and failed billing recovery.
What it detects
Checkout exit points by step and device
Payment method coverage gaps
Failed billing and retry opportunities
Configuration issues affecting conversion
Multi-store consolidated revenue gaps
Recovery potential ranked by impact
What we built
Marketing and positioning layer
CheckoutLeak needed a website that could communicate a technically precise product to a non-technical buyer. We designed and built the full marketing surface: positioning, copy, conversion flow, and pricing structure. The goal was clarity without oversimplification.
Detection pipeline and data layer
We built the backend infrastructure that powers the platform: store connection and authentication, Shopify and Stripe data ingestion, detection pipeline architecture, automated scanning cadences, and multi-store reporting consolidation.
Revenue leak detection engine
The intelligence layer is the core of the product. It runs detection logic across checkout friction, payment method coverage, and failed billing recovery. Each finding is structured with a root cause, recovery estimate, and fix path. Findings are ranked by monthly recovery potential so operators know exactly where to focus.
Approach
The detection logic had to be precise before the interface had any value. We prioritized the accuracy and structure of each finding over the surface layer.
Operators do not need more data. They need prioritized action. Every finding surface is structured around recovery potential, not volume of signals.
The website had to earn trust with a technical audience without becoming a feature list. We wrote and designed it to communicate operator-grade positioning with precision.
Website, backend, and detection engine were designed as one product. Decisions in the UX informed the data model. Positioning informed the detection categories.
Outcome
Operators connect their Shopify and Stripe stores and get a structured view of where revenue is leaking.
Each finding includes a root cause, a recovery estimate, and a fix path. Nothing to interpret manually.
Findings are ranked by monthly recovery potential. Operators know what to fix first.
Multi-store operators get consolidated reporting across all stores in one view.
CheckoutLeak runs on invite-only operator access. The product is live. The detection engine runs automatically. Operators see recovery opportunities within hours of connecting their stores.
Case clarity
It proves we can design and build a complete product from scratch: website, backend infrastructure, and the core intelligence engine in one engagement.
Build laneThis case maps directly to Build and Systems. It demonstrates product design, frontend execution, backend infrastructure, and detection system architecture.
Systems laneFounders and operators who need a full product built, not just a website. Teams who need backend infrastructure, a core software layer, and a market-ready surface delivered together.
Start a projectBuild with Inovense
We design and build complete systems for founders and operators who need the website, the backend, and the core software delivered as one product.
