Source-backed
A useful score should be explainable. We prioritize signals that can be traced to a source or a documented data partner.
TrustDots scores are designed to summarize the best available evidence about a product, brand, or seller. They are decision aids, not guarantees, endorsements, or replacements for reading the underlying sources.
The exact scoring system evolves as coverage improves, but the principle is stable: use evidence that can be inspected, normalized, and updated. Signals may include:
A useful score should be explainable. We prioritize signals that can be traced to a source or a documented data partner.
Products are compared within relevant categories where possible, because a signal can mean different things for food, cosmetics, electronics, or baby products.
Recent recalls, updated disclosures, and changing evidence should be able to move a score as new information becomes available.
Scoring is not a sponsored ranking system. Brands cannot buy a better score or pay to hide negative evidence.
TrustDots separates the evidence we find from the values a shopper brings to a purchase. One person may care most about environmental impact, another about labor practices, transparency, company ownership, faith-based standards, or civic alignment.
The product is designed to let shoppers choose the key factors they care about, then show a performance score that reflects how a product, brand, or seller lines up with those priorities. The facts stay inspectable; the weighting can become personal.
The point is not to tell everyone to avoid the same companies. It is to make the tradeoffs visible, so many individual choices can collectively reward better products, clearer disclosures, and stronger accountability.
TrustDots turns many signals into a simpler trust score and supporting explanations. We account for source reliability, recency, category context, and the strength of the available evidence before presenting the result.
We avoid publishing exact proprietary formulas because they change as models, data coverage, and abuse prevention improve. We do aim to show the major factors that influenced a score so users can understand why it moved.
If evidence is missing, stale, disputed, or still being processed, the product experience should make that uncertainty visible instead of pretending the data is complete.