Amplence built a full-stack AI appeal generation platform that drafts Amazon seller reinstatement appeals using structured intake and a RAG workflow trained on successful appeal templates. The system reduced appeal preparation from hours to minutes and supported more than 2,000 generated appeals with an 87 percent success rate.
Amplence
AI Automation for Real Workflows
Amplence
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Amplence Portfolios
Amplence built a multilingual AI customer service automation workflow for CollageDepot, combining intent classification, Shopify order enrichment, automated response generation, and escalation logic. The system handles more than 5,000 monthly emails, resolves about 65 percent automatically, and keeps average response time under 60 seconds.
Challenge: Homeowners and property managers were waiting 3 to 5 days and paying $500+ for a contractor background check. There was no single place to verify a contractor: the data sat scattered across five separate government systems, each with its own format and no standard way to access it. Solution: We built My Contractor Report, a platform that queries five government databases in parallel and delivers an AI risk-scored PDF report in under 30 seconds, for $19.99. A Gemini-powered scoring engine weighs the data points and writes plain-language recommendations, and each PDF includes direct citations to government records. Results: 1,200+ reports generated; 30-second turnaround (vs 3–5 days); $19.99 per report (vs $500+). Tech: React, Supabase, Stripe, Google Gemini, PDF generation, Serverless. Timeline: 3 months. Full case study: https://amplence.com/case-studies/my-contractor-report
Challenge: Forecasting renewals across 200+ accounts on three renewal calendars and two currencies, where external signals (relegations, budget and staff changes) arrived too late to inform forecasts. Solution: An AI agent that keeps a locked contracted base and proposes renewal revisions from published sources only, with four-driver scenario modeling, a 0.70 confidence floor, named-publisher attribution and human sign-off on every change. Results: 3 renewal calendars modeled; 4 scenario drivers; 2 reporting currencies; 0 forecast changes without human sign-off. Tech: Node.js, Netlify, LLM agent. Timeline: 14 weeks. Full case study: https://amplence.com/case-studies/ai-agents-for-financial-forecasting-prosports
Challenge: Manual intake bottlenecked a Dallas boutique practice promising same- or next-day responses, with risk of missed limitation deadlines and unidentified conflicts. Solution: A custom AI intake agent that runs conflict checks before collecting facts, calculates the Texas limitation position, and enforces three hard stops (conflict, limitation, advice boundary) in deterministic code. Results: 7 matter types routed; 3 hard stops enforced in code; 100% of inquiries conflict-checked first; 0 legal advice given by the agent. Tech: Node.js, Netlify, LLM agent. Timeline: 6 weeks. Full case study: https://amplence.com/case-studies/ai-agents-for-law-firms-daniel-law-firm
Challenge: Manual order processing across 5 languages, 4 channels and 7 European markets, with inconsistent compliance decisions on restricted professional cosmetics. Solution: Six-stage business process automation: language/market resolution, intent classification, account verification, commerce enrichment, compliance gating in deterministic code, and fulfillment routing. Results: 5 languages routed; 6 process stages automated; 4 channels unified; 0 restricted items auto-shipped to consumers. Tech: Node.js, Netlify, Shopify API. Timeline: 8 weeks. Full case study: https://amplence.com/case-studies/business-process-automation-for-ecommerce-bella-luxe
Challenge: Enquiries lacked clinical context and took ~20 minutes of phone work each; urgent sight-threatening cases were indistinguishable from routine ones in a shared inbox. Solution: A six-stage administrative triage agent: intent capture, red-flag screening with hard stops that escalate emergencies, routing across seven surgical service lines, candidacy screening, fee guidance, and a structured handoff brief. Results: 7 service lines routed; 5 red-flag rules in code; 6-stage workflow; 100% of briefs marked for human review. Tech: Node.js, Netlify, LLM agent. Timeline: 6 weeks. Full case study: https://amplence.com/case-studies/custom-ai-agent-for-healthcare-warwick-eye-surgeons
Challenge: A complex architectural metal roofing catalog served architects, contractors and distributors, but generic contact forms produced incomplete inquiries and slow sales handoffs. Solution: A role-aware AI chatbot that guides each user type through tailored product discovery, captures technical requirements, surfaces documentation and prepares organized handoffs. Results: 3 customer journeys; 7+ product pathways mapped; 4 guided questions per workflow; review-ready technical handoffs. Tech: React 18, TypeScript, Vite, Netlify. Timeline: 6 weeks. Full case study: https://amplence.com/case-studies/ai-chatbot-services-for-manufacturing-companies-everlast-metals
Challenge: Unstructured technical requests made it hard for sales to extract requirements and route inquiries to the right engineers; keyword search couldn't match products. Solution: An AI agent that reads technical requests, extracts requirements, matches product paths and prepares handoff notes for engineer review. Results: 3 agent workflows; 6 product lines mapped; 85% agent success rate; 100% of outputs marked for human review. Tech: React 18, Node.js, OpenAI API. Timeline: 1 month. Full case study: https://amplence.com/case-studies/ai-agents-for-manufacturing-industry-greenfield-systems
Challenge: Medicare plan comparison overwhelms people with competing priorities (providers, cost, travel, prescriptions) before they speak to an advisor. Solution: An AI MVP with five guided preference inputs, evidence-retrieved comparisons over fictional plan documents, and an advisor-ready brief; privacy-first with no personal health data. Results: 3 plans compared; 12 evidence points retrieved; 5 guided inputs; 1 advisor-ready brief. Tech: Node.js, React 18, Supabase, OpenAI API. Timeline: 2 months. Full case study: https://amplence.com/case-studies/healthcare-mvp-development-services
Challenge: Submissions arrived through forms, email, decks and referrals, making deal preparation inconsistent and manual. Solution: A six-stage n8n workflow: founder intake validation, pitch-deck extraction, company enrichment, transparent thesis-fit scoring, opportunity routing and decision-pack preparation, with human approval gates. Results: 6-stage connected workflow; 1 centralized pipeline; 100% activity logging; 3 approval gates. Tech: n8n, Supabase, OpenAI API, Next.js 14, Node.js. Timeline: 2 months. Full case study: https://amplence.com/case-studies/finance-workflow-automation
Challenge: Commercial roofing leads arrived unstructured, missing roof condition, material, urgency and service-fit details needed for productive contractor conversations. Solution: An AI-guided assessment tool capturing building type, roof age, material, leak history, photos and urgency, producing explainable condition scores, four triage paths and contractor-ready briefs. Results: Structured, contractor-ready lead briefs with explainable condition scoring and four triage paths (coating, repair, inspection, replacement). Tech: React 18, TypeScript, Vite, Netlify. Timeline: about 7 weeks. Full case study: https://amplence.com/case-studies/ai-mvp-development-for-construction-roofintel