• by Laurie Sullivan , February 13, 2026

Index Exchange executives believe the company's integration with xpln.ai -- which helps advertisers measure ads using attention-based metrics and artificial intelligence (AI) -- has turned attention data into a real-time filtering tool.

The companies integrated attention measurement into its supply-side platform (SSP) through a partnership with xpln.ai, which helps advertisers measure ads using attention-based metrics and AI.

It addresses what marketers call the "viewability" gap, where an ad can be technically viewed on a screen, but ignored by humans because of contextual clutter.

The AI, theoretically, filers out the noise preventing humans from seeing the ad.

Fabien Magalon, co-founder and CEO at xpln.al, who launched Rubicon’s office and Advertising.com’s offices in France, believes “attention sits at the intersection of media quality and creative.”

Magalon says the technology can determine the quality of the creative and how much attention it needs to become successful to ensure that each piece of the creative has the correct amount of attention to deliver an optimal outcome.

AI-based attention metrics influence the way impressions are bought.

Predictive attention signals adjusted against large-scale eye-tracking data gets embedded directly into the SSP, allowing buyers to automatically prioritize or exclude inventory pre-bid measures and optimize how attention drives performance.

Index Exchange’s integration with xpln.al moves attention metrics from post-campaign reporting into real-time bidding, and enables buyers of programmatic media to use or exclude certain types of supply before the campaign begins based on real-time data.

Xpln.al’s technology makes it possible because it embeds predictive attention signals with eye-tracking data in the SSP, allowing automated inventory to filter through the system based on attention patterns.

The company estimates its platform can process between 20 and 25 signals per impression for each individual impression.

These signals such as share of screen, time fully in view, contextual clutter, content alignment, and additional environmental factors influence whether advertisements capture genuine human attention, according to the company.

Machine-learning models, trained on eye-tracking data, predict viewer engagement by generating one key performance indicator (KPI) for use in programmatic buying workflows.

AI turns these insights into automated buying rules within SSP infrastructure, whereas traditional eye-tracking methods served mainly as research tools that revealed attention patterns but were unable to make real-time decisions.

"In the past 18 to 24 months, the branch of the AI getting a lot of visibility is the large language models, but we do AI through computer vision, natural language processing -- those are really at the core of the product," he said. 

This integration builds up Index Exchange's partnership with Gracenote, which embedded brand safety segments and content controls into programmatic.

Index Exchange also works with The Trade Desk using xpln.al’s methods.

When connected to The Trade Desk, these attention segments in the company’s marketplaces are packaged into Deal IDs.

These Deal IDs are then targeted within The Trade Desk platform.