Think of it as a super-fast reader that scans millions of web pages and figures out what each page is really about – not just the words on it, but the meaning and mood – so your ads show up in places that actually fit your brand and audience.
Traditional contextual targeting in advertising has been superficial (keyword-based, brittle, and poor at handling nuance), leading to wasted ad spend, mismatched placements, and brand-safety issues. AI-driven contextual targeting promises deeper semantic understanding of content so ads can be placed in more relevant, safer, and higher-performing environments without relying on personal identifiers or third-party cookies.
Access to high-quality, large-scale content data; proprietary models and taxonomies tuned for advertising semantics and brand suitability; tight integrations with publishers, SSPs, and DSPs that make the targeting option easy to activate and hard to rip out.
Hybrid
Vector Search
Medium (Integration logic)
Context-window cost and latency for processing large volumes of pages and ad requests in real time; maintaining low-latency vector search at exchange scale.
Early Majority
Positions AI-driven contextual targeting as a more genuinely contextual, semantics-aware alternative to legacy keyword and category systems, with better alignment to privacy regulations and the post-cookie landscape.
107 use cases in this application