Case StudiesThree industries. Three production platforms. Real metrics from real projects, not hypothetical projections or demo screenshots.
Card 1: Amazon Appeal Wizard, Legal Services A full-stack AI platform of Business Process Automation that generates professional legal appeal documents in under 3 minutes. Built with Next.js 14, OpenAI GPT-4o-mini, and a Gemini-powered RAG pipeline searching 46 real successful appeals. Includes a complete admin panel for prompt engineering, template management, version control, and A/B testing.
appeals generated
A contractor verification platform that queries five government databases in parallel, applies an AI-powered risk scoring algorithm, and generates professional PDF reports. Built with React 18, Supabase, Stripe, and Google Gemini. Includes customer and admin dashboards, subscription management, and automated email delivery.
reports generated
A complete AI-powered customer service pipeline that handles the full email lifecycle, from capture and AI classification to Shopify order enrichment and auto-response across four languages. Built with n8n, OpenAI GPT-4o, and direct Shopify API integration. Includes automated ticket routing, sentiment detection, and a real-time monitoring dashboard.
emails handled monthly
for Your Business?Have in CommonEvery project on this page shares three things:
Real users, not demos. These production AI platforms serve paying customers and process live data daily. The metrics above come from production usage, not test environments.
AI that’s tuned to the domain. None of these systems use generic prompts or off-the-shelf templates. Each one is built around the client’s specific documents, data sources, and decision logic, so the output is specific enough to be useful.
Infrastructure that runs without us. Each platform operates on its own infrastructure with its own admin controls. The client’s team manages day-to-day operations, prompt tuning, template updates, configuration changes, without filing a support ticket.

Challenge?Every project above started with a conversation about a manual process that was too slow, too expensive, or too fragile to scale. If that sounds familiar, the next step is a call.