Overview
DOVENI is a SaaS product that helps teams evaluate digital-product readiness before release. It reviews signals across UX, accessibility, visual consistency, content, localization, and RTL behavior, then turns the results into clear product and release guidance.
Context
Near release, product-quality information is often distributed across design reviews, QA notes, accessibility checks, stakeholder feedback, and engineering conversations. Teams may understand individual issues without having one clear view of overall readiness.
Problem
Many testing tools generate isolated findings. Teams must still interpret the results, decide what matters most, and determine whether the product is ready to launch.
Goals
Build an evaluation product that connects evidence to practical release decisions.
- Make the first evaluation quick and understandable.
- Prioritize findings by severity and confidence.
- Connect individual issues to release risk.
- Present useful evidence to specialists and decision-makers.
- Treat regional readiness as part of product quality.
My Role
I founded and built DOVENI end to end. My work covered product strategy, information architecture, UX, interaction design, visual design, design systems, front-end development, backend integration, evaluation workflows, testing, reliability, and launch preparation.
Process
I started by mapping the questions teams face before release: What could block launch? Which findings matter most? Are issues repeated? Does the experience work in Arabic and RTL? What does a decision-maker need to know before approving release?
These questions shaped a product centered on evidence, priority, context, and action rather than a long, unstructured list of findings.
Key Decisions
The product separates detailed evaluation evidence from the broader release picture. Quality score, release risk, findings, confidence, regional readiness, and release decisions each communicate a different part of product readiness.
- Lead with readiness rather than raw issue counts.
- Separate quality score from release risk.
- Display severity and confidence together.
- Include Arabic, RTL, and localization in the core evaluation model.
- Provide executive summaries without hiding detailed evidence.
Design System
I created a reusable product language for dashboards, findings, risk levels, quality scores, evaluation progress, navigation, forms, loading states, failure states, and executive information. DOVENI’s teal identity replaces generic success-green treatments unless a separate semantic color is necessary.
Accessibility
Accessibility shaped both the product interface and the evaluation model. The interface uses semantic structure, visible keyboard focus, readable contrast, clear labels, responsive layouts, and states that do not rely on color alone.
Bilingual & RTL Considerations
Arabic, RTL, and localization are evaluated as product-quality dimensions. The review goes beyond text direction to consider layout behavior, alignment, content expansion, mixed-language interfaces, regional context, and whether the localized experience remains coherent and usable.
Development
DOVENI was built with Next.js, TypeScript, Tailwind CSS, Supabase, Playwright, axe-core, OpenAI, Docker, and a production worker hosted on Render.
The system supports authentication, workspaces, applications, evaluations, findings, release decisions, reports, AI-assisted analysis, and long-running browser-based evaluations. Before launch, I also completed a controlled beta and reliability pass covering URL handling, mobile evaluation, failure states, email flows, memory monitoring, and the complete signup-to-result journey.
Outcome
DOVENI progressed from an early product concept to a working production SaaS product at doveni.app. It demonstrates my ability to connect product strategy, UX, visual systems, engineering, AI integration, evaluation infrastructure, reliability, and launch execution within one coherent product.
Learnings
The strongest evaluation experience is not the one that produces the most findings. It is the one that connects evidence, priority, context, and the next action. Building DOVENI end to end also reinforced the value of designing the product, system, and implementation together.