.svg)
for Amazon SellersHow we built a full-stack AI platform that generates professional Amazon reinstatement appeal documents in under 3 minutes, replacing a $3,500 manual legal process with an 87% success rate.
See how Amplence builds AI systems that replace expensive manual processes with speed and precision.
We built an AI-powered Amazon appeal generation system using Retrieval-Augmented Generation (RAG) trained on 46 real, successful appeal templates. The system analyzes the specific suspension reason, maps it to the closest winning precedents, and drafts a tailored Plan of Action in minutes, not days. Deployed across 2,000+ appeals with an 87% reinstatement success rate.
We indexed 46 real successful Amazon appeals into a vector database, building a proprietary precedent library that no off-the-shelf tool could replicate. When a seller submits their case details, Gemini semantically searches this database and retrieves the most relevant successful precedents. These are injected directly into the generation prompt, grounding every appeal in proven, real-world language that Amazon's review teams have already accepted. This single layer is the primary driver behind the platform's 87% success rate.
The core generation layer uses OpenAI GPT-4o-mini with a custom-engineered legal prompt system built specifically for Amazon's reinstatement framework. It structures every appeal according to Amazon's own preferred format, Plan of Action, Root Cause, Corrective Steps, and dynamically adapts tone and emphasis based on the specific violation type, whether that's an inauthentic complaint, a policy breach, or a performance-related suspension. The result is a fully formatted, professional appeal document delivered in under 3 minutes.
Behind the seller-facing product sits a full internal admin dashboard that gives the team complete control over the AI's behaviour. Prompts can be iterated and versioned, legal templates managed and updated, and A/B tests run between document variants to compare approval rates across different writing approaches. Every change is tracked and logged, turning what is usually a guesswork-driven process into a measurable, data-backed discipline.
Sellers access the platform through a clean, guided onboarding flow that walks them through their case details step by step. Stripe handles all payment and subscription logic, while the portal provides a full dashboard to track every appeal ever generated, including current status, submission history, and outcome records. Nothing gets lost, and sellers always know exactly where their case stands.
The platform runs on a serverless Next.js 14 architecture, with API routes handling all AI orchestration between the RAG pipeline and the generation engine. The two AI layers, Gemini for retrieval and GPT-4o-mini for generation, are fully decoupled, meaning each can be scaled, swapped, or improved independently without touching the other. Supabase handles all persistent data: user accounts, appeal records, prompt versions, and A/B test results. The entire system is designed to deliver a complete, submission-ready appeal document in under 3 minutes at any volume.
Retrieval-Augmented Generation sits at the core of what makes the output legally credible rather than generically plausible. Gemini doesn't just assist the prompt, it anchors every generated appeal in the language and structure of real decisions that have already worked.
The version-controlled prompt system ensures that improvements compound over time. Every prompt iteration is stored, compared against historical approval rate data, and only promoted to production when it demonstrably outperforms its predecessor. This turns the platform into a system that gets measurably better with every case it processes.
Empowering businesses with innovative solutions. Let’s create something amazing together.
