CatalogSignal provides a Commerce Eligibility Index that measures and improves how product catalogs are discovered and trusted by AI search and recommendation systems. It diagnoses catalog health across 16 signals, delivers remediation assets like JSON‑LD schema scripts and PIM‑ready CSVs, and continuously validates new product data to maintain AI visibility and accuracy.
Funding
Funding not disclosed
Founders
Product
Problem
AI-powered search and recommendation engines can only surface products that are structurally parseable and trustworthy. Inconsistent attributes, missing schema, duplicate descriptions, or unverifiable claims cause catalog items to be invisible or misrepresented, leading to lost traffic and sales.
Solution
CatalogSignal offers a Commerce Eligibility Index (CEI) that quantifies how discoverable and reliable a product catalog is to AI agents such as chatbots and recommendation models. The platform first diagnoses catalog health across 16 signals and five pillars, delivering an evidence‑backed score and SKU‑level findings. Based on the diagnosis, CEI Activate provides install‑ready remediation assets—including JSON‑LD schema scripts, PIM‑compatible CSVs, terminology normalization kits, and prioritized copy briefs—to align product data with AI requirements. CEI Protect then acts as a quality gate, validating new and updated products before publishing with pass/warn/fail checks against a brand baseline, preventing regressions over time. Together, these steps enable commerce teams to measure AI visibility, fix data gaps, and maintain consistent eligibility across releases.
Target Audience
CatalogSignal serves enterprise retailers, DTC brands, and internal commerce teams responsible for product data, merchandising, and AI‑driven traffic acquisition.
Features
- Diagnostic assessment (CEI Diagnose) delivering a CEI score, signal‑by‑signal breakdown, and department‑specific remediation priorities within 10–14 business days
- Install‑ready remediation outputs (CEI Activate) such as JSON‑LD schema scripts, PIM‑importable CSV files, terminology normalization kits, and SKU‑ranked copy briefs
- Ongoing quality gate (CEI Protect) that validates incoming product updates with automated pass, warn, or fail flags based on the established brand baseline
- Live testing against major AI models (Claude, ChatGPT, Gemini) using 100+ product‑discovery queries per model to measure inclusion, prominence, accuracy, and hallucination rates
- Machine‑learning‑driven scoring engine built on 16 signals covering discoverability, trust, and recommendation readiness
- Secure data handling with encrypted transmission and storage; no API keys or code changes required for assessment