We study how AI discovers, understands, and recommends products.
Research-grade investigations into the AI discovery layer. Every study states its methodology, its sources, and its verified claims. Not content marketing; measurement.
Studies
Ad Strength vs Quality Score: The Two Metrics That Decide Your Cost Per Click
The difference between Ad Strength (coverage diagnostic) and Quality Score (auction determinant), and how each impacts real CPC in Google Ads 2026.
How to Feed Google and LinkedIn Ads Algorithms with the Right Signals
Signal ingestion architecture for automated ad generation: brand intelligence, keywords, audiences, and technical constraints that Google and LinkedIn AI engines need to produce high-performing creatives.
Copywriting Strategy for Interest-Graph Algorithms: LinkedIn vs Facebook
How LinkedIn 360Brew and Facebook MSI algorithms evaluate text, which lengths work on each platform, and which copywriting frameworks maximize organic reach without external link penalties.
Google Ads 2026: AI Max, Power Pack, and the New Account Architecture
The mandatory transition to AI Max, the Power Pack framework, and how to restructure a Google Ads account to feed algorithms with high-density data in 2026.
LinkedIn Ads API 2026: AI Policies, Technical Specs, and Validation Architecture
Technical integration manual for the LinkedIn Marketing API: Rest.li protocol, versioning, permission matrix, AI policies, and a four-tier validation system for programmatic ad generation.
The Perfect LinkedIn Ads Post: Specs, 2026 Benchmarks, and B2B Strategy
Complete guide to technical specifications by format, 2026 performance benchmarks (CTR, CPC, CPM by industry), auction dynamics, and conversion strategy for LinkedIn Ads B2B.
What we investigate
How AI systems discover, understand, compare, and recommend products, and how retailers control their representation inside those systems.
How AI search systems read, structure, rank, and answer from catalogs, feeds, and structured data, and what they can and cannot see.
Structural changes in ecommerce driven by AI: catalog architecture, product entities, discovery surfaces, and the new buyer journey.
How advertising changes when discovery moves through generative systems: asset economies, AI-optimized campaigns, and brand governance.
Measuring who gets cited, recommended, and seen across AI search: query sampling, citation extraction, product matching, and scoring.
The AI Commerce Benchmark
A living measurement of how AI engines represent ecommerce catalogs: 12,500+ products, 42,000 queries, 4 engines, refreshed quarterly.