Case Study · Product / Diagnostic Engine
Conversion Lens
AI-powered business diagnosis engine that analyzes customer journeys, identifies conversion gaps and recommends operational improvements. Structured intake feeds a Claude reasoning layer that maps friction points across touchpoints — from first inquiry to booking — and outputs a prioritised, actionable remediation plan. Built as a repeatable operational framework, not a one-off audit.
Focus
Systems Architecture · AI Implementation · Customer Journey Analysis · Product Operations
Stack
React · TypeScript · Claude API · Vercel
Status
Production · Deployable
Problem
Operators feel where their funnel leaks but can't see it clearly enough to fix it. Generic audits surface symptoms; the underlying journey friction stays invisible until revenue is already lost. What's needed is a repeatable diagnostic framework, not another one-off report.
Approach
AI-powered business diagnosis engine that analyzes customer journeys, identifies conversion gaps and recommends operational improvements. Structured intake feeds a Claude reasoning layer that maps friction points across touchpoints — from first inquiry to booking — and outputs a prioritised, actionable remediation plan. Built as a repeatable operational framework, not a one-off audit.
What I built
- Customer journey friction mapping across all touchpoints
- Conversion gap identification with quantified impact scoring
- AI-powered operational improvement recommendations
- Reusable diagnostic framework deployable across industries
Outcome
A reusable diagnostic framework that turns 30 minutes of structured input into a prioritised, AI-reasoned remediation plan — deployable across any business with a customer journey to optimise.
Architecture notes
Built to run autonomously, fail loudly, and be handed off to a team — not held together by a single operator. Conversation state, business logic and integration adapters are separated so any layer can be swapped without rebuilding the system.
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