66 M
URLs analyzed
The Language of
Performance
Large language models are embedded at the core of the RTB House Deep Learning stack, enriching every campaign with semantic intelligence derived from natural language. Sharper targeting, more relevant ads. Better results across the funnel.
Generating semantic
intelligence at scale
LLMs analyze content across the open internet and product feeds at scale, transforming complex natural language into an ever-expanding map of contextual relationships that power more precise ad targeting. Large language models are already working behind the scenes in all our campaigns.
Numbers that speak for themselves
Operating LLMs across the open internet requires robust engineering. Here are some numbers that show it in action to date.
70 M
matched offers
90 K
audiences generated
What LLMs bring to the table
Large language models understand web content the way humans do. Not by counting keywords, but by interpreting meaning. RTB House embeds this capability directly into its Deep Learning engine.
1/3
Semantic understanding at depth
LLMs embed critical contextual insight directly into our Deep Learning engine, improving every bid with semantic signals derived from natural language. Our Deep Learning system already analyzes vast quantities of data at every bidding decision. LLMs add an additional layer of contextual intelligence, boosting results across the sales funnel.
LLM-powered audiences
Our LLMs read web content the way humans do, interpreting the meaning of articles on the open internet to identify true intent. The system calculates semantic relevance between URL content, advertiser inventory, and LLM-defined audiences that place ads in front of those genuinely interested in your products.
Products matched to context
LLMs align the semantics of each page with ads for the most relevant products from the advertiser's catalog, so the right ad meets the right context. Advanced, first-of-its-kind semantic comparison increases ad relevance in matching products to context automatically across every campaign.
How LLMs work inside our engine
Four steps connect language understanding to campaign performance running automatically, at scale, inside every campaign.
URL and feed semantic processing
Our LLMs analyze web content at the individual URL level and process natural language across page content and product feeds, transforming unstructured data into structured semantic representations.
Semantic relevance and signal generation
The system calculates relevance between URL content and advertiser inventory, then generates contextual signals and audiences based on that analysis.
Integration into the Deep Learning engine
All LLM-derived signals are fed directly into the RTB House Deep Learning stack, informing real-time bidding and offer selection across every impression.
Automatic activation across campaigns
This LLM technology runs inside every campaign from day one. No setup, no configuration, immediate performance.
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