Measured 5,475 LLM executions and 709 ground-truth field comparisons.

The Review Agent ran the deepest cohort the platform has executed on a single merchant: 5,475 LLM queries across providers, surfacing exactly when AI engines mention reviews, ratings, fit guidance and sizing for Erobold SKUs. 709 ground-truth checks confirmed which AI claims matched the live PDPs and which drifted.

Simulated the trust-signal stack: review schema, sentiment match, social proof.

For each PDP the OS modelled three things in parallel: is review schema present and valid? Are AI engines actually citing the reviews when asked? Does the sentiment AI infers match what shoppers actually wrote? In intimates, where most buying decisions happen on trust, every gap matters more.

• Review schema validated per PDP, surfacing missing review counts, ratings, and dates
• AI-cited sentiment cross-checked against the actual review corpus
• Fit-note coverage flagged separately: sizing matters most in this category

Shipped 3,100 action items, with the Review Agent leading the trust-signal queue.

P-priority CONTENT_FIX items lead the queue: every PDP that lacks review schema, every product where AI is citing weak or no social proof, every gap between the sentiment AI infers and what the live reviews actually say. 15 SKUs, 3,100 line items, every one ranked.

Compounded: the trust-signal pass is now a default specialist on every audit in trust-heavy categories.

Erobold proved that visibility alone isn’t enough in categories where the buying decision rides on social proof. The Review Agent’s trust-signal pass (schema + sentiment match + fit-note coverage) is now a default for intimates, beauty, supplements, and any DTC category where reviews carry the cart.