Digital Marketing / Ad Intelligence · Dave
AfterLib — digital ad intelligence platform
Built an ads intelligence platform that aggregates, filters, and tracks competitor creatives — with hundreds of filters by performance, trend, and e-commerce platform.
- Timeline
- Jun 2024 – Feb 2025 · live
- Status
- Live
- Category
- software
- Industry
- Digital Marketing / Ad Intelligence
Live
Status
Actively onboarding users
100s
Filter depth
Performance, trend, and platform filters
Context
Marketers and e-commerce brands struggled to benchmark competitor ads across platforms and extract actionable insights from huge creative libraries.
Dave needed a centralized, automated ad intelligence product with advanced filtering and competitor page tracking.
Channels & stack
- Custom Software Development
- Svelte
- Tailwind CSS
- Figma
- Node.js
- PostgreSQL
- Redis
- ElasticSearch
- AWS
- Railway
- Stripe
- Facebook / Shopify / WooCommerce / Amazon Ads
Challenge
Tracking competitor campaigns across platforms
Real-time trend and performance analysis
Fragmented tools for creatives and page monitoring
Inefficient sorting and insight extraction at library scale
Goals
- 01
Centralize ad library indexing and search
- 02
Ship advanced filtering and competitor tracking
- 03
Deliver real-time analytics for strategy decisions
Delivery sequence
How the engagement ran.
Phase 01
Deep-dives
Discovery
Mapped needs of marketers, brands, and agencies.
Work
- Persona and workflow research
- Filter taxonomy design
Deliverables
- Product requirements
- UX for library + filters
Phase 02
Agile sprints
Data & product build
Indexing pipelines, ElasticSearch, and competitor tracking.
Work
- Ads ingestion and indexing
- Competitor follow/inspect flows
- Favorites and multi-channel notifications
Deliverables
- Ads library
- Tracking dashboard
- Notification services
Architecture
- 01
Comprehensive multi-platform ads library
- 02
Advanced filtering by performance, trend, and platform
- 03
Competitor page tracking and favorites
- 04
Alerts via Telegram, Slack, WhatsApp, and Messenger
- 05
Performance analytics and trend reports
Outcome
Clients gain deep competitive ad intelligence
Streamlined research workflows and faster time-to-insight
Scales for agencies and brands of all sizes
Lessons
- 01
High-volume ad indexing needed optimized pipelines for real-time use
- 02
Microservices for notifications matched how marketers actually work
- 03
Close stakeholder collaboration kept the filter set usable