Oversee: Ops & AI Dashboard

Overview

Workshop management system where organizations track vehicle repairs, assign technicians, and monitor performance. Users create reports with registration numbers, problem descriptions, categories, and time tracking. An org-scoped AI assistant powered by Z.AI GLM-4.7 handles multi-step workflows — parsing Excel uploads into reports, querying the database, generating charts, and bulk-updating records — with human-in-the-loop approval before any destructive action. The whole interface is Swedish-first, built for field teams who may not be familiar with AI tools.

Key Features

Role-based multi-tenancy: owners, admins, members, and technicians each see different data and controls, enforced by Supabase RLS
AI assistant with 10+ tools for database queries, report CRUD, Excel parsing, chart generation, and web search — all scoped to the active organization
Human-in-the-loop approval dialogs before the AI creates, updates, or deletes reports
Report management with registration numbers, categories, technician assignment, time tracking, and bulk operations via drag-reorderable data tables
Dashboard analytics: KPI cards, daily/weekly/monthly trend charts, category distribution, technician performance bars, and top reported issues
Vehicle inspection tracking (Bilkollen) for recurring check-ups alongside ad-hoc repair reports
Invitation system with join requests, org creation flow, and onboarding for new users

Challenges

Z.AI's GLM-4.7 only supports Chat API, rejects system role messages, and doesn't handle images — so the integration needed workarounds for message formatting and graceful degradation when users attach files
Multi-step AI workflows like Excel import (fetch technician names → category names → parse → create reports) had to preserve context across 5+ tool calls without blowing up the token budget
Technicians should only see their own reports while admins see everything — getting RLS policies right for that asymmetry across all query paths took careful testing
Base64 images from screenshots can exceed Vercel's 8MB request limit, so the client compresses them iteratively before sending

Solution

System prompt gets prepended to the first user message to work around GLM's missing system role. AI tools use names_only flags and pagination to keep token usage low during multi-step operations. RLS policies enforce org + role scoping at the database level, and server actions validate membership before any mutation. Client-side Canvas API compression reduces image payloads by 60-80% with iterative quality reduction. Rate limiting (30 req/hour per user per org) prevents abuse without blocking normal usage.

Outcome

Used in production by a workshop organization to track repairs and technician workload. The AI assistant handles the bulk of data entry — field technicians snap a photo or paste an Excel export and the assistant turns it into structured reports after confirmation. What started as a KPI dashboard grew into the primary tool for day-to-day operations.

Project Information

Technologies

TypeScript

Next.js 16

React 19

Supabase

shadcn/ui

TanStack Table

Vercel AI SDK

Z.AI GLM-4.7

Zod

react-hook-form

Recharts

MDX

Framer Motion

Playwright

View Live Project View Source Code

Overview

Key Features

Role-based multi-tenancy: owners, admins, members, and technicians each see different data and controls, enforced by Supabase RLS

AI assistant with 10+ tools for database queries, report CRUD, Excel parsing, chart generation, and web search — all scoped to the active organization

Human-in-the-loop approval dialogs before the AI creates, updates, or deletes reports

Report management with registration numbers, categories, technician assignment, time tracking, and bulk operations via drag-reorderable data tables

Dashboard analytics: KPI cards, daily/weekly/monthly trend charts, category distribution, technician performance bars, and top reported issues

Vehicle inspection tracking (Bilkollen) for recurring check-ups alongside ad-hoc repair reports

Invitation system with join requests, org creation flow, and onboarding for new users

Challenges

Z.AI's GLM-4.7 only supports Chat API, rejects system role messages, and doesn't handle images — so the integration needed workarounds for message formatting and graceful degradation when users attach files

Multi-step AI workflows like Excel import (fetch technician names → category names → parse → create reports) had to preserve context across 5+ tool calls without blowing up the token budget

Technicians should only see their own reports while admins see everything — getting RLS policies right for that asymmetry across all query paths took careful testing

Base64 images from screenshots can exceed Vercel's 8MB request limit, so the client compresses them iteratively before sending

Solution

Outcome