Data intelligence / Performance marketing
ARIE
An AI reporting intelligence engine for turning ad-platform exports into growth recommendations.
A full ad-performance analytics product that ingests Meta, Google, and TikTok Ads CSVs, scores efficiency, generates AI recommendations, and packages the output into dashboard and report workflows.
Status
Prototype
Timeline
Built as an ad reach intelligence product
Domain
Performance marketing
Why
Data intelligence
Stack
Languages, services, data sources, and operating pieces behind the build.
Code Proof
What The Build Actually Contains
LOC
20k+
Source files
125
Input
CSV exports
AI
Claude
Product proof
Implementation
Code Behind The Surface
Turning exports into recommendations
tsThe core move behind the product surface.
const report = await generateAdReport({
sources: ["meta", "google", "tiktok"],
metrics: normalizePlatformExports(files),
objective,
});
return rankRecommendations(report.insights);Product Model
tsThe useful answer has to be modeled before the UI can make it obvious.
type ProductSurface = {
input: "Performance marketing";
signal: "How do you make cross-channel ad performance easier to diagnose when the raw data li";
proof: string[];
};
const surface: ProductSurface = {
input: "Performance marketing",
signal: "Spend, reach, efficiency, and next-action recommendations in one workflow.",
proof: [
"Meta, Google, and TikTok CSV upload",
"Metrics dashboard",
"Claude-powered recommendations",
"Efficiency scoring"
],
};Hard Part
tsEvery build has a constraint: data quality, workflow shape, trust, speed, or operational risk.
const constraint = {
project: "ARIE",
status: "Prototype",
category: "Data intelligence",
hardPart: "This connects my growth background to product execution: a shorter path from paid-media data to action, priori...",
};
shipSurface(constraint);Project Logic
Why This Exists
The point is not to show another screen. It is to show the gap, the build constraint, and the proof of work.
Mission
How do you make cross-channel ad performance easier to diagnose when the raw data lives in separate exports?
Performance marketers often spend too much time normalizing exports and too little time deciding what to change. Meta, Google, and TikTok each expose useful signals, but the workflow still leaves teams stitching together spreadsheets before they can act.
Build
What Had To Work
I built an AI reporting system that uploads CSV exports, maps platform columns, calculates performance metrics, grades efficiency, and generates recommendation-oriented reports.
Why It Matters
Cross-channel report
Turns scattered ad-platform exports into a faster optimization recommendation loop.
Hard Parts
Make The Signal Useful
Turn fragmented ad-platform exports into a faster optimization loop for marketers who need to know what to do next, not just what happened.
Turn The Work Into A System
I built an AI reporting system that uploads CSV exports, maps platform columns, calculates performance metrics, grades efficiency, and generates recommendation-oriented reports.
Prove The Wedge
This connects my growth background to product execution: a shorter path from paid-media data to action, prioritization, and narrative for the next optimization cycle.
Decisions
Next Move
I would add account-level benchmarks, anomaly alerts, budget-shift suggestions, and activation flows that turn the first uploaded CSV into an immediate audit moment.
Tell Me About Your Project
Bring Me The Bottleneck.
I’ll Build The Answer.
Tell me what people are trying to do, where the current path breaks, and what kind of useful answer should exist.
Market Gap
Demand exists, but the answer is missing.
Workflow Drag
The work is still too manual, slow, or scattered.
Product Wedge
A small surface could prove the larger opportunity.