Monitor Each Product Individually

Why product-level monitoring matters

Brand monitoring answers “Is my company recommended?” Product monitoring answers a different question: “Is my specific product recommended?” The distinction matters because different products face different competitors and produce different results.

A dental clinic might have strong overall brand visibility — AI recommends them for general dental care. But when someone asks about teeth whitening specifically, the clinic doesn’t appear at all. A different set of competitors dominates that niche. Without product-level monitoring, you’d never know.

The same pattern applies everywhere. A software company is highly visible for its main CRM product, but its newly launched analytics feature gets zero mentions. A hotel chain is recommended in Paris but invisible for its ski resort property. Each product lives in its own competitive landscape inside AI engines.

Same depth, product-level focus

Product monitoring uses the same methodology as brand monitoring — 15 queries across 8 buying intents, sent to all 8 AI engines simultaneously. The difference: every query is tailored to the specific product or service, not the company as a whole.

Instead of “best dental clinics in Berlin,” the system asks “best teeth whitening in Berlin.” Instead of “top CRM software,” it queries “best sales pipeline tool for startups.” The queries target how real customers search for that particular offering.

You get the same five core metrics — Market SOV, Brand SOV, Visibility, Citation Rate, and Sentiment — each scoped to a single product. A key changes banner highlights movements between monitoring runs: which metrics improved, which dropped, and by how much.

Product competitors — not brand competitors

When AI recommends alternatives to your product, it doesn’t list companies — it lists competing products. The competitor table reflects this. Each entry shows the competing product, not just the brand behind it.

• Product name and brand — the specific offering AI recommends, with the parent company
• Price — if AI mentions pricing, it’s captured and displayed
• Verification status — hallucination filter confirms the product actually exists
• Product URL — direct link to the competing product page
• Mention count — how many times this product appeared across all queries and engines
• Reason — why AI recommends this specific product over others

This is fundamentally different from brand-level competitor analysis. Your brand competitors and your product competitors are often entirely different lists.

Sentiment and position by engine

Each of the 8 AI engines gets its own card showing how it treats your product. Beyond raw visibility numbers, you see two dimensions that aggregate metrics miss.

Sentiment bar shows whether AI describes your product positively, neutrally, or negatively for each specific engine. A product can have 80% visibility but mostly negative sentiment — AI mentions it frequently but recommends against it. Without sentiment tracking, you’d see high numbers and think everything is fine.

Position bar shows where your product appears in AI’s ranked list — top, middle, or bottom. Being mentioned is good. Being mentioned first is significantly more valuable — users act on the top recommendation. Position tracking reveals whether you’re the first choice or an afterthought.

Each engine card displays Market SOV, Brand SOV, Visibility, Citation Rate, and Sentiment independently. One engine might champion your product while another ignores it — and now you can see exactly where the gaps are.

Product-level recommendations

Generic advice doesn’t help when each product faces unique challenges. Product monitoring generates recommendations specific to that offering — not your company as a whole. Each recommendation uses the Fact → Action → Expected Result format:

• Fact — what AI currently says about this product, verified against real responses
• Action — a concrete step to improve this product’s AI visibility
• Expected Result — which metric should change and by how much

Each recommendation has an effort badge — Quick Win, Medium Effort, or Major Initiative. The system tracks progress across monitoring cycles: recurring issues are flagged, resolved issues are marked. Any recommendation can be converted to a testable hypothesis with one click.

Custom queries for product-specific questions

Auto-generated queries cover standard buying scenarios for your product. But your market has questions that only you know matter. A SaaS company might need to track “best [product type] with Salesforce integration.” A clinic might want to monitor “painless teeth whitening options near [city].”

Add your own queries to any product sub-monitor. Each custom query goes to all 8 engines and results appear alongside the standard queries with the same metrics and hallucination filtering.

Custom queries cost $0.70 each, added on top of the base monitoring price. The formula: base cost + (number of custom queries × $0.70).

How to set it up

Product monitoring lives inside your brand monitor. You don’t create a separate project — you add products to an existing one.

• Open any brand monitor and scroll to the Products section
• Click “Add Product” and fill in the details: product name, price, city, product URL, and a short description
• Click “Create sub-monitor” — the system generates product-specific queries automatically
• Custom queries — optionally add your own questions for this product ($0.70 each)
• Independent schedule — each product can run on its own monitoring schedule

Add as many products as you need. Each one gets its own full set of metrics, competitors, and recommendations — completely independent from the parent brand monitor and from other products.