AI testing services for next-gen no-code app platform
The way we helped Rocket improve the quality of its AI-generated applications, stabilize their platform, and release their iOS mobile product.
Client
Rocket
Team
Dedicated QA engineers
Engagement
Ongoing
Platforms
Web, iOS
Technologies and tools
BrowserStack
Linear
Supabase
GitHubResults by the numbers
2×
Test coverage increase
40+
Connectors validated
iOS v1
Mobile app released
100%
Guardrail pass rate
5+
UI bug patterns identified
Daily
Agile QA engagement
Here's a bit about Rocket
Industry
AI / No-Code
Engagement
Ongoing
Platforms
Web, iOS
Integrations
40+ connectors
Rocket is an AI-driven no-code platform that empowers users to generate fully functional web and mobile applications using natural language prompts. Its platform competes in the rapidly growing AI application builder market, alongside tools like Loveable and Emergent Mind, giving non-developers the ability to create production-grade apps without writing a single line of code.
Rocket offers a distinct value proposition: users describe what they want to build, and the AI generates the application, complete with UI, data layer, and third-party integrations, in real time. As the platform matured, the team recognized that AI-generated applications introduced a new class of quality challenges that traditional QA methods were not equipped to handle.
Before and after QAble
Before QAble
With QAble
Weeks of manual regression testing required
AI-generated UI inconsistent across templates
No tests executed after developer pull requests
LLM only supported short, simple prompts
No special testing environment for AI output
No third-party connector testing
No mobile QA process, iOS app unreleased
Unstructured approach to validating AI output
Supabase data-layer issues undetected in generated apps
Our engagement
Our QA experts joined the Rocket team to implement comprehensive AI-aware testing for their platform. The primary goal was to enhance product quality and minimize the risk of issues creeping into production. Leveraging deep expertise in LLM behavior, our team built a testing framework from scratch covering functional, UI, mobile, integration, and prompt validation.
Additionally, our QA engineers introduced structured regression testing cycles within the Agile sprint process. Daily standups, sprint-aligned test planning, and real-time defect reporting ensured that every release was validated thoroughly. One major outcome was reducing regression cycle time from weeks to a single overnight run.
Another key objective was to stabilize the AI generation engine. We discovered that identical prompts produced inconsistent results across different templates, a checkbox component worked correctly in one template but failed in another. By building cross-template coverage, we helped stabilize the full template library. The team's expertise not only ensured efficient testing but also enabled Rocket to ship their iOS mobile application for the first time.
Engagement highlights
What QAble delivered
Services
UI and functional testing
Our team designed a comprehensive UI regression suite spanning all component types generated by the Rocket AI engine. We established cross-template parity testing protocols and a living defect taxonomy for AI-generated UI issues. Systematic exploratory testing surfaced bugs that manual testing alone would have missed, particularly Supabase-related data display issues and form component rendering defects.
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AI products need QA that understands how LLMs behave. Schedule a call to see how QAble can build the testing framework your platform needs.
QA built for AI-first products
Traditional QA cannot validate LLM outputs, cross-template consistency, or AI guardrails. QAble builds the frameworks that AI products actually need.
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