If your company sells more than one product, buyers ask AI about each one differently : different questions, different rivals, different winning claims. KnitKnot models this with subjects: your brand is one subject, and each product is its own subject underneath it. Adding a product gives it a dedicated question library, competitor set, and slice of the report, while everything still rolls up under the brand.

Add a product

Open Benchmarks → Benchmark model → Products in the console, then add a product by name. KnitKnot starts a research pass to profile its capabilities and positioning in the same way it profiles your brand.

Adding a product sets up the profile; it doesn’t build the questions yet. Once the product is in place, generate its questions so future benchmarks start measuring it. You may also want to add competitors specific to that product : the field it competes in often differs from your brand’s.

What “separate” means

Once a product exists, everything that hangs off it is scoped to it:

• Questions : the product has its own library; editing it doesn’t touch your brand’s questions.
• Competitors : its competitive field is tracked independently.
• Scoring : every response is scored against the product it belongs to, so a claim about one product never counts for or against another.

How products roll up

A product recommendation is a brand win. When AI picks one of your products over a competitor : or picks one of your products over another of your products : that’s a win for your company, not an internal loss. KnitKnot treats products that share a parent brand as one family for the purpose of who won.

In the report, this shows up as a company roll-up with a chip per product. Switch chips to drill into a single product’s numbers; the brand itself is the roll-up, never its own chip. The report guide covers reading the subject filter.

Next: run a benchmark to measure your new product across all four engines.