Portfolio build · modeled outcomes where noted

Home Services AI Booking Agent

A practical WhatsApp + web lead qualification and booking workflow for a Dubai home-maintenance operator.

74% modeled auto-qualification<60 sec modeled first response11 hrs/week modeled admin saved
ServiceAI Automation & Agents
Timeline4–5 weeks
Project typeShowcase brief: Home Services
LocationDubai, UAE
Home Services AI Booking Agent portfolio artifact preview
Portfolio artifact previewOriginal Qintora showcase work created to demonstrate the implementation approach.
This is an original Qintora portfolio project. It is designed to show the level of strategy, implementation detail and deliverables a client can expect. Where percentages are shown, they are explicitly modeled targets rather than claimed client results.
WhatsApp Business APIn8n / MakeCRM pipelineGoogle CalendarGA4 events
Lead sourcesWhatsApp · web forms · paid traffic
Primary handoffBooked slot or human quote review
GuardrailsNo pricing promises or technical diagnosis

The brief

This portfolio build uses a realistic Dubai home-services scenario: a maintenance operator receiving AC, plumbing and electrical inquiries through WhatsApp, website forms and paid campaigns. The operational problem is fragmented intake — agents repeatedly ask for service type, location, urgency and preferred time before a booking can even be created.

Signals we designed around

The workflow assumes a mixed lead pool with urgent requests, price shoppers, out-of-area inquiries and repeat customers. The agent first captures the service category and community, then checks service-area rules, asks the minimum qualification questions and routes the conversation to either a booking path, a quote-review path or a human specialist.

What the portfolio implementation includes

The build is deliberately specific enough to show how Qintora would execute a live automation engagement, rather than stopping at a chatbot mockup.

  • Conversation map with service-area and urgency rules
  • Lead object + CRM field mapping
  • Booking / quote-review routing logic
  • Human escalation and failure paths
  • Operations dashboard event specification

Measurement model

The dashboard tracks first-response time, qualification completion, human escalations, bookings created, no-slot cases and drop-off by conversation step. A live deployment would compare these metrics against a pre-launch baseline and keep business outcomes separate from automation activity metrics.

Modeled outcome

Using a representative lead-volume model, the workflow is designed toward roughly 74% automated qualification, first response in under one minute and approximately 11 staff hours per week returned from repetitive intake. These are modeled targets for this showcase, not results from a claimed client account.

How this becomes a live client project

For a real business we would replace all assumptions with actual service areas, calendars, technician capacity, pricing rules, CRM fields, escalation policies and approved bilingual copy. The automation would launch behind a controlled pilot, then expand after error-rate and booking-quality checks.

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