Build a Customer Portal for a Service Business with AI — Auth, Requests, and Status
A customer portal — login, service requests, status tracking — is the feature that separates a real service business from a brochure. Here is how to build one with AI: the architecture, the auth pattern, the data model, and what to verify before launch.
A customer portal changes what a service business is: instead of phone tag and paper invoices, customers log in, submit requests, see status, and find their history. It is also the build that separates serious AI platforms from toy ones — a portal needs real authentication, per-customer data isolation, and persistent state. Here is how to build one with Multos, what the architecture looks like, and what to verify before you put customers on it.
What the Portal Needs to Do
The core loop for a service business: customer signs up and logs in; submits a service request (type, description, photos, preferred date); sees the status of each request (received, scheduled, in progress, completed); views their history and invoices; and gets confirmations. Each of these is a standard pattern — the build is in wiring them together correctly, not inventing anything novel.
The Architecture, Plainly
Frontend: the generated React app with a login screen, a request form, a request list, and a detail view. Backend: an API layer with endpoints for auth, requests, and status updates. Database: one customers table, one requests table, one status-history table — relational by default (PostgreSQL), because 'which requests belong to which customer' is exactly what relational databases do. Hosting: your own cloud account, so customer data lives in infrastructure you control — which is also the compliance answer if the data includes addresses, phone numbers, or access instructions to homes.
Authentication: the Part to Get Right
The Data Model
Keep it boring and correct: customers (id, name, email, phone, address), requests (id, customer_id, service_type, description, photos, preferred_date, status, created_at), status_history (id, request_id, status, note, changed_at, changed_by). The AI generates this from a description like 'customers submit service requests and track their status; staff update status with notes.' Boring schemas are maintainable schemas.
What the AI Builds in One Session
A working portal — auth flow, request submission with photo upload, status timeline, customer history, and a minimal staff view for updating statuses — is a one-session build on Multos. The honest scope note: payment collection inside the portal, SMS notifications, and staff scheduling optimization are follow-up prompts, not first-session features. Build the core loop first, put it in front of five customers, then extend.
Verify Before Launch — the Checklist
One: the access-control test above. Two: photo uploads land in your storage with correct permissions. Three: email confirmations actually send (configure your provider; test from a real inbox). Four: the portal works on a phone — your customers will use it from a job site. Five: backups exist and you have restored from one. Six: the whole thing runs on your cloud account, so the data-residency answer to 'where is my customers' information?' is one you control.
What This Costs
Build: included in your token plan — the free tier can build it, Lite ($20/month) runs it comfortably. Running: your cloud account at cost — a small portal with modest traffic runs single-digit dollars per month on managed services. Compare that to the $15,000–$50,000 a custom portal quote typically starts at, and the maintenance retainer that follows it.
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