Booking Platform & AI Experience
July 2026 — Present
A booking platform and AI-assisted website built for a working car and motorcycle detailing studio in Lahore, covering the customer journey from service discovery and appointment requests to live business support through an AI assistant.
Services involved
01 / The situation
The Buff operates a real automotive detailing studio where appointment length depends heavily on the service being booked. A premium wash may take a fraction of the time required for a complete detailing or paint-correction job.
Before the platform was introduced, the business had no dedicated website and no structured booking workflow. Appointments were coordinated manually, which meant customers had no reliable way to see available times and the business had no system for automatically accounting for different service durations.
The absence of a centralized system also meant service information, pricing, operating hours, and booking rules could not be managed from one place. Any future digital experience needed to reflect how the studio actually operates rather than forcing the business into a generic appointment model.
02 / The challenge
The core challenge was not simply creating an appointment form. The booking system had to understand service duration, working hours, scheduling conflicts, and owner approval while remaining simple enough for customers to use without knowing how the scheduling logic works.
Different detailing services require significantly different amounts of time, so fixed appointment slots were not sufficient.
Customers can select multiple services, which means total appointment duration must be calculated dynamically.
Available times must fit completely within configured business hours rather than only checking whether the appointment can start.
Existing bookings must be considered so new requests cannot overlap with appointments already occupying part of the requested time range.
The owner needed control over prices, service duration, availability, business hours, and booking rules without requiring a code deployment.
The AI assistant needed to answer questions using current business information rather than relying on hardcoded responses that could become outdated.
03 / The approach
We treated the website, booking system, admin controls, and AI assistant as parts of one operational system rather than separate features. The shared database became the source of truth for the information used by both customers and the AI experience.
The first priority was understanding how services, durations, business hours, availability rules, and booking requests relate to each other. This allowed the interface to follow the studio's real scheduling constraints instead of using generic fixed slots.
Service pricing, duration, active status, business hours, and booking configuration are stored as manageable data. This allows the owner to change day-to-day business information without waiting for a development update.
When customers select services, the system calculates the total appointment duration and checks candidate start times against business hours, existing bookings, buffer rules, minimum notice, and slot intervals.
Instead of turning every customer selection into an instantly confirmed appointment, the system uses a booking-request model so the business can review and confirm requests before committing the schedule.
The assistant was designed around tools that query live business data so answers about services, pricing, availability, and operating hours remain aligned with the information used elsewhere in the platform.
04 / What we built
The final product combines a customer-facing website, duration-aware scheduling logic, administrative business controls, and an AI assistant into one connected experience.
Customers can select one or more services, with total appointment duration calculated automatically from the selected service configuration.
Available appointment times are generated based on total service duration, configured operating hours, booking rules, and existing appointments.
The business can manage service names, pricing, duration, and active or inactive status without changing the application code.
Weekly business hours, booking buffers, minimum notice requirements, and slot intervals are represented as configurable operational data.
Customer submissions remain requests until reviewed by the business, giving the owner control over final scheduling decisions.
A conversational assistant built with Google ADK can answer questions about the business by querying live data rather than depending on static prompts.
The assistant can guide a customer through the information needed to begin a booking request directly through the conversation.
The website uses a tailored visual direction rather than relying on the familiar visual patterns of generic car wash and detailing templates.
05 / The outcome
The Buff now has a connected digital system for presenting its services, handling appointment requests, managing operational information, and assisting customers through both the website and an AI-powered conversation.
Instead of treating the website as a static marketing layer, the project connects customer-facing content with the studio's real scheduling and service data. The same information can be managed by the business, used by the booking engine, and accessed by the AI assistant, reducing the gap between what customers see and how the studio actually operates.
Technology behind the work
Client perspective
A project is not only the system that gets delivered. The working relationship matters too.
Client feedback
Ahmad Tahir
Owner · The Buff Detailing
The Buff Detailing
“Codemaven understood how our business actually handles bookings and built the website around that process. The booking system, service management, and AI assistant have made the platform much more useful than a standard business website.”
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