AI-Powered Google Review Management Platform
🔒 Private Source Code: This repository serves as a portfolio showcase. The source code is private due to proprietary workflows and commercial licensing.
- 🚀 Live SaaS: Fully functional multi-tenant application deployed in production.
- 🤝 Human-in-the-Loop: AI generates drafts; humans approve them. Zero autonomous posting.
- 🛡️ Safety First: Strict database isolation and deterministic AI fallbacks.
- ⚡ Modern Stack: Built on Next.js 16 and Supabase for a serverless architecture.
This project was built using an AI-Assisted Development workflow. My role focused on high-level system design, product strategy, and orchestration, while leveraging AI tools for implementation.
My primary contributions were:
- Product Requirements: defining the user flows and the "draft-first" approach to solve the trust gap in automation.
- System Architecture: Designing the data model and selecting the infrastructure components to support multi-tenancy.
- AI Workflow Design: Structuring the prompt engineering strategies and fallback logic to ensure reliable outputs.
- Validation: Reviewing and refining AI-generated code to ensure it met production standards for security and correctness.
ReviewGo addresses "Review Fatigue"—the difficulty businesses face in maintaining engagement on Google Maps.
While many tools offer full automation, I prioritized a Human-in-the-Loop model. Business owners are often wary of letting a bot speak for their brand. By positioning the AI as an assistant that prepares Drafts for approval, we provide the speed of automation with the safety of human oversight.
A high-level view of how a review moves through the system:
[Google Review]
⬇
(Webhook Event)
⬇
[Ingestion Queue] ➝ (Idempotent Processing)
⬇
[AI Analysis] ➝ (Groq Llama 3 ➝ Fallback to Gemini)
⬇
[Draft Created] ➝ (Stored in Supabase)
⬇
[Human Approval] ➝ (User clicks "Publish")
⬇
(Google API) ➝ [Reply Published]
I chose an asynchronous webhook model to handle incoming reviews.
- Decision: Decouple ingestion from processing using a worker queue.
- Why: This ensures that traffic spikes (e.g., viral reviews) do not overwhelm the application or lead to data loss.
The system design acknowledges that AI providers can experience latent periods.
- Decision: Implement a cascading fallback strategy.
- Why: Prioritizing Groq (Llama 3) allows for sub-second draft generation, but having Gemini as a fallback guarantees service continuity if the primary provider fails.
I structured the database to enforce security at the lowest level.
- Decision: Use PostgreSQL Row Level Security (RLS) keyed to Organization IDs.
- Why: This provides a "fail-safe" layer. Even if an API endpoint were to have a logic error, cross-tenant data access remains blocked by the database engine itself.
- Fully Autonomous Replies: I intentionally excluded auto-posting to avoid the risk of AI hallucinations affecting brand reputation.
- Open Source Distribution: The specific prompt engineering and orchestration logic remain proprietary.
- Social Media Support: The scope was constrained to Google Reviews to ensure deep, high-quality integration before expanding.
These tools were chosen as implementation enablers, not as a claim of deep framework specialization.
The following tools were selected to balance developer velocity with production reliability:
- Framework: Next.js 16 (App Router)
- Database: Supabase (PostgreSQL + Realtime)
- Language: TypeScript
- AI Inference: Groq + Gemini
- Infrastructure: Vercel
ReviewGo is currently live and serving users.