Advertising · IP Safety
One article URL in, five structured signals out: IAB category, entities with salience scores, sentiment, brand safety, keywords.
- Five, from a single pass Signals per URL
Level IntermediateRead 6 min readView use case
Use Cases
Real-world applications of multimodal AI, each with the templates and code to build it.
44 use cases
- E-commerce
Understand what products can do, not just what they look like
- +35% improvement Search relevance (NDCG)
Level IntermediateRead 8 minView use case - sports · Entertainment
Auto-generate highlight reels from full-length sports footage
- 24x faster Highlight generation time
Level AdvancedRead 7 minView use case - Education · Learning
Make every lecture moment searchable and actionable
- 80% reduction Content discovery time
Level BeginnerRead 6 minView use case - creative-dna · Advertising
Understand what makes ad creatives perform before they run
- Scored at upload Creative approval
Level AdvancedRead 9 minView use case Search and analyze thousands of declassified legal documents
- 100% of corpus indexed Document searchability
Level IntermediateRead 7 minView use caseMultimodal search and analysis for government document repositories
- 100% unified index Cross-department search coverage
Level AdvancedRead 7 minView use case- Advertising · E-commerce
Enrich leads with visual and behavioral signals from their content
- +30% improvement Lead scoring accuracy
Level IntermediateRead 8 minView use case - DAM Intelligence · Advertising
Auto-tag and organize digital assets with multimodal AI
- 95% reduction Manual tagging effort
Level BeginnerRead 7 minView use case - Advertising · Entertainment
Match talent to roles using multimodal portfolio analysis
- 75% reduction Casting search time
Level IntermediateRead 7 minView use case - Advertising
Track creative evolution from concept to final cut
- 85% concept retention Brief-to-final alignment
Level AdvancedRead 8 minView use case - Advertising
Analyze and optimize social content performance with multimodal AI
- +35% average improvement Content engagement rate
Level IntermediateRead 7 minView use case
None of these are quite your problem?
Every use case above is the same pipeline pointed at different content: extract features from your files, index them in one namespace, then compose retrieval stages for the question you actually ask. Point it at your own storage and see what comes back. Build starts at $25/mo for up to 1M vectors.
Or read how any of them are built
Every use case above composes these same primitives.
RetrieversThe saved, named pipeline every use case on this page is built as.Feature extractorsWhat gets pulled out of a file before any of this is searchable.Multi-stage retrieval deep diveHow filter, search, rerank, and enrich stages compose.NamespacesWhere your vectors live and how to scope them per tenant or per app.