Client Stories: How We Rescued Three Broken Bubble Applications

Three Architecture Rescues, Three Different Failure Modes

The best way to understand what architecture failure looks like — and what architecture rescue looks like — is through real engagements. Details are anonymised and specific numbers are approximated, but the architecture problems and our solutions are described accurately. These three cases represent the most common failure modes we encounter.

The SaaS That Was Leaking Customer Data

The situation: A 6-month-old B2B SaaS with 45 paying customers. The founder contacted us after a customer reported seeing another company’s data in their dashboard. The app had been built by a freelancer who had no understanding of Bubble’s privacy rule system.

What we found: Zero privacy rules on 11 of 14 data types. The default ‘Everyone’ configuration was in place on the core CRM data types. Any authenticated user could search for any record belonging to any workspace via the Bubble Data API. The app had been live for six months with every customer’s data completely exposed to every other customer.

What we did: Emergency 48-hour engagement. We added workspace fields to all 11 unprotected data types, created correct privacy rules, ran the tenant isolation test, and deployed a patch. We then conducted a full security audit and found three additional vulnerabilities in workflow permission checks. Total remediation: 4 days.

The outcome: No customer was lost, but two enterprise prospects who had been close to signing had to be re-engaged with a security statement. The founder later attributed losing one of those prospects to the delay caused by the incident. Cost of not having an architect from day one: approximately £15,000 in lost revenue plus the £8,000 remediation engagement.

The App That Worked Fine at 50 Customers and Broke at 300

The situation: A project management SaaS built by the founder over 9 months. At launch with 50 customers it performed adequately. At 300 customers the dashboard was taking 12-18 seconds to load. Three enterprise prospects had tried and rejected the product specifically citing performance.

What we found: The dashboard performed 22 separate search operations on page render, including 8 count queries across large data sets. Every count query scanned the entire data type. The largest data type had 280,000 records. One count query was retrieving all 280,000 records to the browser and counting them in JavaScript.

What we did: We designed and implemented denormalised counters for all 8 dashboard metrics. We replaced every count query with a read from the Workspace record. We added 6 counters to the data model and updated 14 workflows to maintain them. We eliminated 19 of the 22 page render queries. Dashboard load time dropped from 14 seconds to 0.8 seconds.

The outcome: The three prospects who had rejected the app on performance grounds were re-engaged. Two converted. The third had already signed with a competitor. Net result: 2 enterprise customers recovered, dashboard performance maintained as the customer base continued to grow. The architecture work that would have prevented this took 4 hours at the start of the project.

The App Where Customers Who Paid Could Not Access Their Account

The situation: A 3-month-old SaaS with 30 paying customers. The founder was spending 2-3 hours every day manually activating accounts for customers who had paid but whose subscription status had not updated. The app had been built quickly by an offshore team unfamiliar with Bubble’s webhook system.

What we found: The Stripe integration relied entirely on the checkout success redirect URL to activate subscriptions. When a customer completed payment and the browser closed before the redirect fired — which happens approximately 15% of the time due to mobile networks, accidental browser closure, or redirect delays — the subscription status was never updated. 15% of every day’s checkouts required manual intervention.

What we did: Rebuilt the billing architecture from scratch over 3 days. Implemented all 6 webhook events as the sole source of billing truth. Removed the redirect-based activation entirely. Backfilled all existing customers by matching Stripe subscription IDs to workspace records. Tested every event type against the live Stripe account before deployment.

The outcome: Zero manual activations in the two months following the fix. The founder recovered 6+ hours per week that had been consumed by manual billing intervention. The fix also uncovered 3 customers who had technically cancelled but were still marked active due to the same redirect-dependent logic — representing approximately £450/month in revenue the business had not actually earned.