CHALLENGE
Roman AI was preparing to launch an AI assistant designed to do more than answer questions.
Roman operates directly inside a team's workflow, connecting to external tools and executing tasks on the user's behalf. That meant the quality bar was higher than a conventional SaaS product: the assistant needed to not only respond appropriately, but reliably understand requests, execute multi-step workflows, handle unexpected inputs, and produce results users could trust.
Before launch, Roman needed an independent quality assessment of the product's readiness and the risks that could affect the early user experience. MayQore was brought in for a three-week pre-launch audit.
HOW MAYQORE HELPED
MayQore conducted a focused QA audit of Roman's AI assistant, combining exploratory testing with structured validation of the product's core user journeys.
The engagement focused on:
- AI assistant behavior — validating how Roman handled different types of user requests and interactions.
- End-to-end workflows — testing whether tasks could move from user instruction through execution to a usable final result.
- User journey validation — evaluating the experience from the initial request through completion.
- Edge cases and failure states — looking beyond the happy path to identify situations where the assistant could behave unexpectedly or fail to complete a task.
- Functional QA — validating core product functionality and workflows across the application.
- Defect identification and prioritization — surfacing issues based on their potential impact on users and launch readiness.
- Regression awareness — helping distinguish isolated defects from issues that could affect broader product behavior.
- Launch readiness — providing a clearer view of the risks that needed to be addressed before release.
Rather than simply producing a list of bugs, the audit was structured around a more important question: “Can users trust Roman to do what they asked it to do?”
THE RESULT
Over three weeks, MayQore provided Roman's team with an independent view of product quality ahead of launch.
The engagement gave the team:
- A structured assessment of the product's launch readiness
- Clear visibility into issues discovered during testing
- Prioritized feedback on areas requiring attention
- Validation of critical user journeys
- Additional confidence around the product's AI-driven workflows
- A QA perspective focused specifically on the risks of shipping an AI-powered product
For an AI product where the promise is “tell it what you need and let it do the work,” reliability is part of the product itself.
MayQore helped Roman evaluate that reliability before putting the product in front of a broader audience.
WHY THIS MATTERED
Traditional software testing asks: “Does the software work?”
AI-powered software introduces another layer: “Does the software behave reliably when users interact with it in unpredictable ways?”
That distinction was central to the Roman AI audit.
MayQore brought human-led QA and structured testing to a product where user intent, AI behavior, integrations, and execution all intersect.
