heybooster is active in the analytical layer of the AI agent ecosystem, specifically focusing on the transition from passive data visualization to active monitoring agents. While it is not a fully autonomous media-buying agent that executes trades independently, it performs the cognitive heavy lifting required for such agents: cross-platform data synthesis, performance anomaly detection, and strategic recommendation generation.
For builders in the agent space, heybooster represents a specialized domain model for eCommerce. It demonstrates how AI can be used to bridge disparate data silos (Google vs. Meta) to provide the "reasoning" layer that a future autonomous marketing agent would require to make budget decisions. It effectively serves as a diagnostic agent that monitors state changes across a marketing stack and triggers human-in-the-loop actions.
heybooster originated from a consulting practice where the team performed repetitive manual audits for eCommerce clients. Instead of scaling their service by hiring more analysts, they chose to encode their auditing logic into software. This origin defines the product's utility: it is designed to replace the routine cognitive work of a marketing analyst with automated, cross-platform insights.
The core problem heybooster addresses is the siloing of marketing metrics. While Google Ads and Meta Ads provide internal data, these platforms do not communicate with one another. Marketers often find themselves with high-performing ads on one platform that drive traffic to non-converting pages, or they might spend budget on SKUs that appear popular but actually carry a high cost of sales. heybooster is the layer that sits above these silos. It pulls data from Meta, Google Search Console, Google Ads, and Google Analytics to identify these discrepancies.
The platform is an automated auditor. It identifies underperforming products that are consuming budget without yielding conversions. Conversely, it highlights "hidden drivers"—products that have high organic visibility or conversion rates on the site but are being overlooked in paid campaigns. This SKU-level granularity is the primary differentiator. For a store managing hundreds of products, manually tracking which specific items are wasting spend is a labor-intensive task. heybooster automates this by flagging critical changes in product performance and alerting the team before the budget is fully depleted.
Beyond simple reporting, the tool provides specific budget reallocation recommendations. For example, if a campaign has a high Return on Ad Spend (ROAS) but is limited by its impression share, heybooster flags the opportunity to scale. If a creative asset is performing below the account average, it suggests a replacement. This shifts the marketing workflow from a passive dashboard-viewing exercise to an active, alert-based system. Case studies from the company suggest these adjustments can lead to significant outcomes, such as a 60% budget saving for brands like Wrangler through the identification of keyword targeting errors.
Based in London and Tallinn, the company was founded in 2019 and secured seed funding in 2024 to further its AI capabilities. Its primary users are eCommerce marketing teams and growth agencies who need to maintain oversight across multiple channels without hiring a full-scale data department. The competitive positioning is distinct from general-purpose business intelligence tools like Tableau or Looker. Those platforms require data teams to build and maintain custom models. heybooster arrives with pre-built models specifically tuned for eCommerce sales funnels and ad platform logic. It focuses on the interpretation gap—the space between seeing a data point and knowing what action to take.
An AI-powered analysis platform that helps eCommerce teams detect marketing issues and optimize budget allocation.
Heybooster is hiring.