From manual bottlenecks to automated scale: How Coframe validates thousands of website variants

Coframe is an AI-native growth marketing platform that autonomously generates and deploys website variants that drive conversion lift and outperform both SaaS and CRO agencies. Trusted by teams at industry-leading companies such as Dropbox and Intuit, Coframe empowers its users to scale experimentation without adding operational overhead.

Challenge

Ensuring quality across an expanding number of customer-facing website variants

Coframe was founded on the idea that teams don’t need a team of engineers, designers, and marketers to optimize their UI. By using generative models trained on high-performing web patterns, the platform enables companies to continuously and autonomously improve and personalize their UI at a velocity few internal teams or agencies could match.

However, Coframe’s promise isn’t just speed; it’s delivering high-quality, production-ready optimizations at scale. Every UI variant must integrate cleanly with customer environments and perform as intended from the moment they go live. If a single bug reaches production, it can trigger a series of failures (from broken checkouts to failed forms) that block real purchases and muddy optimization insights.

As Founding AI Engineer, Glavin Wiechert was tasked with ensuring quality across each deployment. At the time, Coframe relied heavily on backend unit tests combined with developer-led manual checks during implementations. While backend coverage was robust, manual testing was slow, fragmented, and inconsistent. Over time, the risk of issues surfacing at the interaction layer continued to climb.

Glavin first began experimenting with Playwright. While the tool helped standardize testing workflows, the technical overhead associated with maintaining suites for ephemeral variants was significant. Knowing he couldn’t simply throw headcount at the issue, Glavin began searching for an automated testing platform that could validate comprehensive frontend flows without introducing additional maintenance overhead.

Fortunately, Glavin was already using Momentic in a limited capacity to test Coframe’s internal product workflows. As the number of website variants continued to grow, it became clear that the platform could also support the broader validation needs of Coframe’s experimentation engine. That realization led him to formalize Momentic’s adoption across the team.

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Solution

Automating end-to-end testing with Momentic

With Momentic, Coframe transformed frontend testing from a manual bottleneck to its competitive advantage.

Instead of maintaining selector-heavy scripts tied to specific DOM structures, Glavin now authors tests by describing user workflows and expected outcomes. Momentic interprets those steps against the deployed interface, identifying elements contextually rather than binding to static identifiers. When multiple similar elements appear on a page, Momentic surfaces that ambiguity and prompts clarification before execution, preventing silent mis-clicks and reducing false positives.

This shift reduces maintenance overhead and enables validation to scale alongside development. When UI structure changes, tests do not require constant rewrites because they are interpreted in context rather than rigidly coupled to implementation details.

Within two weeks, Glavin had integrated Momentic into Coframe’s CI, and validation became a seamless part of the release process. Chrome extension compatibility, essential for Coframe’s product, was identified early, and the Momentic team moved quickly to enable local support, removing what could have otherwise been a blocker.

Beyond validating internal product flows, Glavin focused on a larger objective: ensuring that the growing volume of customer-facing UI changes generated by Coframe could be validated automatically.

He built a proof of concept that translated AI-generated variant descriptions into structured acceptance criteria, then executed those validations through Momentic runners. Rather than discouraging the approach, the Momentic team collaborated to explore a more formal integration path via an official SDK.

“Momentic didn’t treat our proof-of-concept as misuse. They saw it as a legitimate value add and worked with us to formalize it. That’s when you know you’re working with the right partner.”

Results

Scalable validation, expanded coverage, and a future-proof testing foundation

Partnering with Momentic enabled Coframe to replace manual checks with a reliable, behavior-driven testing foundation that scales alongside the company. By slashing time spent on validating UI variants, Glavin reclaimed engineering bandwidth that he now directs toward higher-value initiatives.

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Moving forward, Glavin is eager to expand Momentic’s influence across Coframe’s non-technical operators, empowering them to create and execute end-to-end tests that wholly align with customer behavior.

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