Introduction
Revora AI started with a practical problem: local businesses lose reviews because collection is slow and easy for staff to forget. Go Wind built a MERN stack SaaS to make review collection fast for customers and manageable for owners.
This article complements the [Revora AI portfolio case study](/portfolio/revora-ai) with architecture, stack decisions, and product lessons.
The Problem
Local businesses struggle with inconsistent Google reviews. Manual follow-ups are slow, customers abandon flows, and owners lack sentiment insight.
Revora AI targets:
- QR-based collection for in-store and post-service moments
- AI-assisted drafts when customers do not know what to write
- Sentiment and reputation analytics owners can act on
- AI-generated review suggestions
- QR code review collection
- Customer feedback funnel
- Google review redirection
- Business analytics dashboard
- Review sentiment analysis
- Reputation score tracking
- Marketing content generator
- QR poster customization studio
Architecture
Built with React.js, Node.js, MongoDB, OpenAI, Tailwind CSS, and JWT authentication.
Core modules: QR review funnel, AI draft generation, business dashboard, sentiment analysis, QR poster studio, and marketing content reuse.
Features Shipped
Key Decisions
Mobile-first funnel — review moments happen on phones; every extra step costs completions.
AI as assistance — drafts help customers write; the product does not auto-publish synthetic reviews.
Dashboard clarity — built for busy owners, not enterprise power users on day one.
Business Impact
Designed to help businesses collect up to 3× more Google reviews with flows targeting under 30 seconds on mobile.
Revora AI is in active development as a Go Wind internal product. [Contact Go Wind](/contact) to discuss similar AI SaaS builds.