Two orthodontic offices, one connected patient journey

The practice

An orthodontic practice with two locations across Illinois, each drawing from its own local market. Bilingual in English and Spanish, with same-day braces and in-house financing among the things patients repeat about it.

This is one of more than fifty projects, an example of the work rather than the whole book.

The starting condition

A site that had grown a page at a time, two Google Business Profiles managed separately, no shared picture of where the practice showed up across the areas each office serves, and no clean count of which inquiries came from where.

What was built, in the order the diagnosis set

Market and competitive audit. We audited how the practice shows up across Google, the local map and the AI assistants, ChatGPT, Gemini, Claude and Perplexity, against the practices patients see instead. We ran a 169-point visibility map for each office, and ran it again after the profile work.

Offer, from the practice’s own data. The practice’s anonymized transaction history was used to pin down who its best patients are, and a brand voice, tone and messaging direction was built around them. No patient information left the practice’s own systems.

The site. We migrated it to a new platform on the same domain, with a one-to-one map of 56 redirects so the addresses worth keeping still resolve, page titles and descriptions swept, and structured data rebuilt for a multi-location practice: one organization, one entry per office. The content is built on real patient needs, an orthodontic issues hub and a life-with-braces guide, written to rank locally and to be picked up by AI search. An objection-based Smile Quiz that answers the reason a patient has been putting it off.

Profiles. Both Google Business Profiles were rebuilt: primary category, a consolidated services list with descriptions, the language attribute, financing handled in the description rather than as a service, and the booking link pointed at a tracked consultation page.

Measurement. We wired tag management, analytics and session recording with named events for inquiries, calls, bookings and quiz starts, so the practice counts a lead once and can see which source produced it.

Now in build. We are building an intake and patient-communication system on the practice’s CRM, integrated with its practice management software, so an inquiry is answered in minutes, followed up until it books, and reminded until it shows.

What this changed

The 169-point visibility map was run for each location before the work and again after it, on the practice’s main keywords. The measure is coverage: the share of the 169 sampled points at which the practice appeared in the local pack top three. That share rose by about 30 percent between the two scans. The AI Visibility scan moved the same way: the AI assistants name the practice across the core patient questions, where before they did not.

The map result is a measured figure and the AI result is a qualitative read of recorded answers; both come from instruments the practice can inspect, run before and after. No revenue or patient-count figure is published here, because the tracking that would carry one went live partway through the engagement.

The pillars this touched

Market Visibility came first, because the audit showed where the practice was and was not being found. Offer Design came second, from the practice’s own patient data. Insights came third, because the practice could not say which inquiries came from where. Systems is not claimed here yet: the intake and patient-communication build is the part still in progress.