Programmatic advertising has a signal-to-noise problem. Real-time bidding systems bid on millions of impressions per second, but the models making bid decisions are often trained on stale data, optimized for click-through rather than downstream conversion, and unable to incorporate the context that makes an impression valuable.
Ad Nexus rebuilds the programmatic stack with AI throughout — from audience modeling to bid strategy to creative optimization.
What Ad Nexus Does
RTB bidding engine. Processes bid requests from SSPs in real time, with sub-10ms latency. Bid prices are determined by ML models trained on conversion data from your attribution pipeline — not industry averages.
Audience segmentation. Build audience segments from first-party behavioral data, enriched with contextual signals. Lookalike modeling using embedding-based similarity rather than demographic proxies.
Campaign management. Budget pacing, frequency capping, and daypart optimization managed automatically. Campaigns optimize toward your actual business metrics — revenue, registrations, LTV — not proxy metrics.
Creative optimization. Multi-armed bandit testing across creative variants with automatic budget reallocation toward winners. AI-generated creative variants for testing.
First-Party Data in an AI World
The deprecation of third-party cookies has accelerated what Hanzo customers already knew: the only durable signal is first-party data. Ad Nexus is built around this reality — every audience, every bid model, every optimization starts from data you own.
For AI applications — products that already collect rich behavioral data about how users interact with AI — this is an advantage. The model that understands what a user is trying to accomplish is better positioned to deliver relevant advertising than any third-party segment.
Available at github.com/hanzoai/adnexus.
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