Our Methodology

For each product category, we collect 50–100 products from major retailers, primarily Amazon. We focus on products with:

• Strong sales history (bestsellers and rising products)
• 4+ star ratings with substantial review counts
• Explicit performance claims in their marketing

We intentionally cast a wide net to catch both popular choices and overlooked gems.

Custom scripts analyze each product's marketing materials, specifications, and documentation. The AI flags:

• Specification inconsistencies — when claimed specs don't match documentation
• Missing certifications — safety ratings, standards compliance
• Unverifiable claims — marketing language without supporting evidence
• Category mismatches — products that don't belong in the search results

This automated pass helps us quickly identify red flags across dozens of products.

Every AI finding is reviewed by a human. We cross-reference:

• Official manufacturer websites and spec sheets
• Product manuals and documentation (often PDFs)
• Regulatory listings where applicable
• Real customer reviews and complaints
• Community forums and discussions

This human layer catches nuances that automation misses — and filters out AI errors.

Based on verified specifications and evidence, we produce:

We explain why each product ranked where it did — with specific, measurable differences.