A busy dental practice needed the enquiry captured before the visitor left the page — tagged with the treatment the patient chose, and routed somewhere the practice actually reads. The build pairs a validated intake form with a WhatsApp hand-off that arrives already knowing what the patient asked for, and a dashboard that lays out the follow-up pipeline from first enquiry to treated.
Enquiries arrived by phone during treatment hours, when nobody could answer.
A dental audience searches by treatment — implants, root canal, orthodontics — not by clinic name.
The practice serves a bilingual catchment, so the page had to set Devanagari as competently as Latin.
A structured, treatment-led intake sits in the page rather than behind a mailto link, so the enquiry is typed — and tagged with the treatment the patient chose — while intent is highest.
The form validates an Indian mobile number and requires an explicit consent tick before it will submit, so what leaves the page is reachable and permissioned.
Click-to-chat links carry an intent-specific pre-filled message, so the thread opens with the reason for contact already written.
A separate dashboard turns captured enquiries into a worked pipeline — four summary tiles, two charts, and a kanban running the six statuses from new enquiry through treated and follow-up to closed.
A Dentist entity graph publishes the practice, its hours, its coordinates and its treatment questions in a form search engines and assistants read directly.
Every row was read off the deployed build at inspection, not from a project brief; the decisions below give the date each was checked. The deployment is not linked, because the business is not named.
A booking with an unreachable number is not a booking. The field constrains input to a valid Indian mobile — ten digits opening 6–9, with an optional country code — and explains the rule in place instead of failing silently after submission.
Verify: Read from the production booking form on 2026-08-07: the phone input carries a pattern attribute, a numeric inputmode, and its own error message element.
A public clinic form attracts bot submissions, and a CAPTCHA taxes every real patient to stop them. An off-screen field that humans never see and scripts routinely fill costs the patient nothing and drops the automated traffic.
Verify: Read from the production booking form on 2026-08-07: a visually hidden, aria-hidden field with tabindex -1 and autocomplete off, labelled to be ignored.
The practice contacts patients on WhatsApp afterwards. Making the tick a submission requirement means every record it holds was volunteered on the record, rather than assumed from a pre-checked box.
Verify: Read from the production booking form on 2026-08-07: the consent checkbox is required, and the form will not submit without it.
The dashboard is reachable at a public URL with no login. Seeding it with real enquiry records would have made a demonstration surface into a data-protection exposure — names, phone numbers and treatments for identifiable people, served to anyone who guessed the path. Fictional records make the surface reviewable without putting a single real patient on the open web. The constraint the page is actually under is privacy, not fidelity.
Verify: Read from the production dashboard on 2026-08-07: invented patient names, placeholder numbers and sequential record IDs throughout.
It is an internal surface for the practice, not a page for patients, so it earns no navigation entry and no sitemap presence. Reachable by URL for whoever is given it; absent from the path a visitor takes.
Verify: Searched in the production site’s source on 2026-08-07: the public pages contain no reference to the dashboard’s path — zero matches.
Serving Hindi through a fallback renders it visibly worse than the English beside it. Noto Sans Devanagari ships alongside the Latin faces so both scripts are set deliberately.
Verify: Read from the production document on 2026-08-07: the Google Fonts request includes Noto Sans Devanagari with the Latin families.
Not publicly disclosed.
No business outcome has been measured against a stated method and confirmed with this client, so no figure is published. We would rather show you the system and let you judge it than print a number nobody verified.
This build uses the following SheetFlow services:
