A credit score is trusted for one reason: everyone can see what goes into it. Sigbase Retrieval Score (SRS) works the same way. The method is public. What moves the number is public. That transparency isn't a risk to the standard — it's what makes it one.
Every scan runs an identical, standardized set of real-world queries across three major AI engines — the questions actual customers ask in your client's niche and location. Answers are scored by consensus: one engine mentioning your client is noise; multiple engines agreeing is signal. The result is a 0–100 score across five dimensions of AI visibility.
Run a client's scan today, run it again next month — under the same methodology version, you land on the same score. That stability is what makes it a standard, not a snapshot.
AI engines are third-party systems that shift over time; Sigbase Retrieval Score (SRS) holds the measurement method fixed and dated so the comparison stays fair, and publishes the methodology version on every report.
The scoring method, the query set, and the math are fixed and versioned. Neither your agency, nor Pandobix, nor any optimization provider can influence how a business is scored. You can improve a client's inputs — that's the fix layer's entire job — but you cannot move the scoring itself. That's the same reason a credit score means something: you can pay down debt to raise it, but you can't reach into the formula.
Open, no login, the same for every business scored. Pandobix methodology is based on the Sigbase.AI neutral scoring mechanism.