A Locus Intelligence alternative with no location minimum.

A multi-location estate is the set of every Google Business Profile your brand operates across every city or market you serve, treated as one connected network rather than a collection of independent pins. The point at which a single brand becomes an estate is roughly five locations: at that scale, manual consistency checks stop working, because a wrong category in Pune, missing hours in Jaipur, and a duplicate in Surat cannot all be tracked by hand, and the structural relationships between listings — fragmented names, drifted categories, and proximity-based duplicate candidates — become the dominant signal that moves the brand’s overall visibility. The audit reads the estate as one graph: every listing is compared against every other on four consistency axes, and a proximity-based duplicate detector flags same-brand pins within one kilometre for human review.

INR-native pricing is the practice of quoting a per-location, per-month subscription price in Indian Rupees with a GST invoice, rather than presenting a USD figure that the customer has to convert at a moving exchange rate and reconcile against a foreign-exchange invoice. For an Indian brand, the difference is concrete: a USD-priced plan means the number on the pricing page is not the number the finance team can plan against, because the rupee cost drifts every month, and a foreign vendor may not issue a GST invoice at all, which turns a routine software expense into a reconciliation headache. INR-native with GST invoicing means the figure you see is the figure you budget, and the invoice you get is the one your accountant expects — which is the whole point of publishing a price list in rupees rather than converting a global price into local currency.

Cross-location consistency is the practice of comparing every Google Business Profile in your estate against every other one to find fragmentation on the four dimensions the audit automates: business name, primary category, website hostname, and phone-number format. Name drift — "Brand Name Baner" vs "Brand Name - Baner Pune" — is the most visible form, and it is exactly what Google’s naming guidelines discourage because extra keywords or locations that are not part of the real-world business name weaken the entity confidence Google applies to your pins. The consistency check is the one severe-category check that can only run by comparison; a single listing looks fine in isolation, but two listings with drifted names, drifted categories, or drifted websites expose the brand as multiple loosely-related businesses to Google’s matcher. The audit detects, a human fixes, and the brand maintains a locked source-of-truth sheet so future edits stay consistent.

Brand Health Score is a single 0–100 number that summarises the listing-level findings of a Google Business Profile audit into one figure a marketing lead can track month over month. It is built in two stages: every listing starts at 100 and loses points for each issue the audit raises, weighted by severity (Critical costs 15 points, Warning 7, Info 2); the per-listing scores are then averaged into a brand base, and brand-level deductions are applied for problems that only exist across the estate, such as name fragmentation, primary-category inconsistency, and duplicate-candidate clusters. The score is a PlaceOptimizer diagnostic, not a Google metric — Google does not produce, recognise, or endorse it, and it does not predict where a brand ranks in the local pack. Treat it as a directional diagnostic for tracking one brand over time, not as a league-table number for comparing unrelated brands.

Listing health is the subset of a Google Business Profile audit that focuses on the public-state signals of a single listing, as opposed to cross-location signals that only surface when you compare listings side by side. The audit’s Listing Health category checks two things automatically: a listing marked PERMANENTLY_CLOSED in the Places API is flagged Critical, and an abandonment heuristic — zero photos and almost no reviews — flags Warning. It deliberately does not attempt to detect suspension risk or edit-conflict history, because those signals are not exposed in the public data the audit reads; the engine marks them as manual verification items rather than scoring them, so a brand knows exactly where the automated pass stops and human review has to start.