Featured Work · AI Product Case Study

AngleScope turns ad-library research into a creative intelligence workflow.

I built AngleScope for affiliate and performance marketing teams that need to find winning ad angles, understand why they work, and turn those patterns into new testable creative concepts without pretending to have private ad-account data.

The Render demo is hosted on a free instance, so first load can take a moment if the service is asleep.

AngleScope workbench preview with ad analysis, angle clusters, and generated creative concepts.
What it demonstrates

Marketing judgment, AI implementation, and data realism in one product.

AngleScope is deliberately not a fake spend dashboard. As an outside builder, I did not have private Meta, Google, TikTok, Taboola, landing-page, or lead-quality data. So I focused on the part of the buyer workflow that can work honestly today: public ad examples, user-supplied creative, structured AI deconstruction, and evidence-backed concept generation.

Problem

Media buyers already do this manually.

Creative angle discovery is often one of the highest-leverage parts of affiliate media buying. Buyers scroll ad libraries, save examples, look for ads that appear to keep running, infer which hooks and proof patterns are working, and translate those patterns into the next batch of tests.

That work is valuable, but it is slow, uneven, and easy to lose in screenshots and scattered notes.

Product thesis

Turn research into an operating loop.

AngleScope collects examples, classifies what is happening, clusters recurring angles, ranks them with evidence, and generates offer-specific concepts that a buyer can actually test. The goal is not to replace the operator. The goal is to give the operator a sharper starting point.

Workflow

From public examples to creative concepts.

Search

Enter a vertical, competitor, or keyword, or provide a manual ad media URL for the analysis run.

Ingest

Load validated seed examples and best-effort public examples from TikTok Creative Center when the server app is running.

Deconstruct

Classify hooks, emotional angles, formats, offer mechanics, CTAs, claims, and compliance risk.

Cluster

Group recurring winning angles and rank them with evidence from source ads and strength scoring.

Generate

Turn selected angles and offer details into new creative concepts, copy, briefs, and image direction.

Export

Make the output usable outside the demo with JSON and CSV export paths.

Architecture

Built as a real product surface, not a one-off prompt demo.

  • Next.js App Router + TypeScript
  • Tailwind UI workbench
  • Zod schemas for API and model output
  • Source adapters for seed, manual, and TikTok data
  • Prisma + Postgres persistence scaffold
Current live build bar
Demo
Full server-side Next.js app deployed on Render.
Sources
Seed examples, manual ad URL input, and best-effort TikTok Creative Center ingestion.
AI path
OpenAI-backed visual and metadata deconstruction plus structured generation when configured.
Fallbacks
Deterministic, evidence-grounded concepts and a static GitHub Pages fallback.
Output
Ranked angle clusters, creative concepts, briefs, copy, image direction, JSON, and CSV.
What I would build next

Close the loop between creative intelligence and business outcomes.

The next version would connect internal performance data so AngleScope can learn from actual winners and losers, not only public examples. That means joining ad creative attributes to ROAS, CPL, lead quality, approval risk, and funnel drop-off.