Inspiration
Traditional analytics tools like Google Analytics and Hotjar show store owners what users are doing, but not what to do about it. Small businesses don't have time to decode heatmaps or hire CRO specialists. Blip Ship closes this gap: an AI agent that detects UX problems and deploys the fixes automatically.
What it does
Blip Ship is an autonomous CRO agent for e-commerce: Tracks user behavior - Rage clicks, dead clicks, form struggles, scroll patterns Detects friction - AI identifies when users are frustrated (e.g., clicking non-interactive images) Generates fixes - Uses Gemini to produce React/TypeScript code patches that match your design system Deploys safely - Shows preview, validates against design guardrails, applies after approval Example flow: User rage-clicks product image → AI detects pattern → Generates onClick handler → Owner approves → Code deploys → Next user can click to open modal
How we built it
Stack: Next.js 14, React 19, Tailwind, Gemini 2.0 Flash, JSON storage EventTracker: Captures 20+ signals (rage clicks, dead clicks, hover intent, exit intent) LLM Pipeline: Loads specialized agent prompts, reads actual source code, enforces theme guardrails, outputs structured patches Guardrails: Theme protection (colors, fonts, spacing), click action rules (preserve existing handlers), statistical thresholds Key innovation: We feed the LLM your actual source code and enforce exact pattern matching—no hallucinations
Challenges we ran into
Code accuracy: LLM guessed wrong code structure → Fixed by feeding actual file content Event noise: 1000s of irrelevant clicks → Added statistical thresholds (5+ rapid clicks, 3+ sessions) Breaking existing features: onClick on image broke the Add to Cart button → Guardrails enforce stopPropagation() Design drift: LLM wanted random colors/fonts → Theme guardrails validate every CSS property
Accomplishments that we're proud of
End-to-end automation: frustration → deployed fix, zero manual coding 20+ behavioral signals with user intent inference Gemini-powered code generation with validation guardrails 95% patch success rate (up from 40% without source code context) Real-time dashboard with one-click approval
What we learned
AI needs constraints - Guardrails turn creative LLMs into reliable coworkers Context is everything - Feeding actual source code vs. assumptions made all the difference Behavioral analytics > raw metrics - "User rage-clicked after failing to compare products" beats "10 clicks recorded" Small businesses need outcomes, not dashboards - Show the fix, not the heatmap
What's next for Blip Ship
A/B testing engine to measure impact before full deployment More fix types: checkout recovery, accessibility, mobile optimizations Multi-store learning to predict issues before they happen Self-evolving stores that get smarter with every customer interaction
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