Challenge
Amazon seller reinstatement work involved repetitive legal-style appeal drafting. Teams had to review suspension reasons, compare them against prior successful appeals, and write a precise Plan of Action manually. Each appeal took hours, and inconsistent drafting made it hard to scale a high quality reinstatement workflow.
Amazon seller reinstatement work involved repetitive legal-style appeal drafting. Teams had to review suspension reasons, compare them against prior successful appeals, and write a precise Plan of Action manually. Each appeal took hours, and inconsistent drafting made it hard to scale a high quality reinstatement workflow.
Solution
Amplence built a full-stack AI appeal generation platform with structured intake, Retrieval-Augmented Generation over 46 successful appeal templates, and prompt workflows using Next.js 14, OpenAI GPT-4o-mini, and a Gemini-powered RAG pipeline. The system maps the suspension reason to similar precedents and drafts a tailored appeal in minutes.
Amplence built a full-stack AI appeal generation platform with structured intake, Retrieval-Augmented Generation over 46 successful appeal templates, and prompt workflows using Next.js 14, OpenAI GPT-4o-mini, and a Gemini-powered RAG pipeline. The system maps the suspension reason to similar precedents and drafts a tailored appeal in minutes.
Results
The platform generated more than 2,000 appeals with an 87 percent success rate, reducing appeal preparation from several hours to under three minutes. It also reduced the effective cost to about $350 per appeal compared with roughly $3,500 for manual drafting while keeping the human review step intact.
The platform generated more than 2,000 appeals with an 87 percent success rate, reducing appeal preparation from several hours to under three minutes. It also reduced the effective cost to about $350 per appeal compared with roughly $3,500 for manual drafting while keeping the human review step intact.