SaaS ~10 hours to build

AI-Powered Brake Inspection Analyser

Upload brake photos, get instant AI diagnostics with repair recommendations for mechanics.

The Problem

Auto repair shops face mounting pressure to deliver fast, accurate diagnostics while operating on razor-thin margins. With over 174,000 repair shops in the US and the average vehicle age now surpassing 12 years, brake maintenance has become a critical—and growing—service category. Yet most mechanics still rely on visual inspection alone, which can miss subtle wear patterns like hairline scoring, uneven pad wear, or early glazing that indicate deeper issues.

The result? Inconsistent diagnoses, customer disputes about whether repairs were truly necessary, and expensive callbacks when problems resurface. With the industry's average profit margin hovering around 6.3%, independent shops can't afford diagnostic errors—but professional tools like JPRO Professional ($1,699/year) or enterprise solutions like Ravin AI are priced for fleets and dealerships, not the typical shop with just 2.2 employees.

The Solution

An AI-powered brake inspection tool that uses computer vision to analyze photos of brake components. Mechanics simply snap photos of pads, rotors, and calipers with their smartphone, and the system provides instant wear analysis, damage detection, and repair recommendations—turning every technician into a brake diagnostic expert. The reports can be shared with customers to build trust and justify repair recommendations with visual evidence.

How it works:

1

Capture

Take photos of brake pads, rotors, and calipers with your smartphone

2

Analyze

AI detects wear levels, scoring, glazing, and damage patterns

3

Report

Get detailed findings with severity ratings and visual annotations

4

Recommend

Receive repair or replacement recommendations with parts estimates

Market Research

The automotive diagnostic and inspection market is experiencing strong growth, driven by aging vehicle fleets and the adoption of AI-powered tools. Key validation signals from recent market research:

$40.27 billion automotive diagnostic tools market in 2024, projected to reach $63.93 billion by 2034 (4.73% CAGR)
$6.9 billion AI vehicle inspection market expected by 2033, growing at 15.8% CAGR—one of the fastest-growing segments
174,200+ auto repair shops in the US as of 2023, representing a highly fragmented market with the top 5 companies holding only 10-15% share
42% adoption rate for AI-powered damage detection in 2024, improving inspection accuracy by 29%—signaling strong market readiness
12+ year average vehicle age in the US, driving increased demand for brake maintenance and repair services

Competitive Landscape

Current solutions in the automotive diagnostic space are either enterprise-priced, fleet-focused, or require expensive hardware—leaving independent mechanics underserved:

JPRO Professional (Noregon)

Comprehensive diagnostic software for heavy-duty trucks with ABS diagnostics, OEM codes, and repair guides.

$1,699/year subscription + hardware

Fleetio

Fleet management platform with inspection checklists, maintenance tracking, and mobile app for technicians.

$4-10/vehicle/month, 5 vehicle minimum

Ravin AI

AI-powered vehicle inspection for insurance and fleet companies. Uses computer vision for damage detection.

Enterprise pricing (contact sales)

AutoVitals

Digital vehicle inspection (DVI) platform with workflow management and customer communication tools.

Custom pricing, ~$29.99/mo tablet subscription

Your Opportunity

No affordable, brake-specific AI diagnostic tool exists for independent mechanics. Current solutions are either full-shop management suites (overkill for brake specialists) or enterprise-grade inspection platforms (out of reach for small shops). A focused, mobile-first brake analyzer at $49-99/month fills a clear gap.

Business Model

A tiered subscription model positions the tool as affordable for independents while capturing more value from multi-location shops. Pricing is informed by competitor analysis showing $4-10/vehicle for fleet tools and $29-99/month for shop software.

Free

$0

10 inspections/month, basic reports, single user

Pro

$49/mo

Unlimited inspections, detailed reports, parts pricing, history

Shop

$99/mo

Multi-user, branded reports, customer portal, API access

Unit Economics

Target CAC

$50

Avg. Revenue/User

$65/mo

Est. Churn

5%/mo

LTV

$1,300

Recommended Tech Stack

This stack balances rapid development with the AI/ML capabilities needed for image analysis. The separation of frontend and AI backend allows you to iterate on the model without touching the user-facing code.

Next.js + TypeScript

Frontend framework with built-in API routes. Perfect for the upload interface and customer-facing reports.

Python + FastAPI

AI backend for image processing. FastAPI handles async requests well for the analysis pipeline.

TensorFlow / PyTorch

Image classification models for detecting brake wear patterns, scoring, and damage.

AWS S3 + PostgreSQL

S3 for image storage, PostgreSQL for inspection history and user data.

Clerk

Authentication with built-in user management. Handles team accounts for the Shop tier.

Vercel + Railway

Vercel for the Next.js frontend, Railway for the Python AI backend. Simple deployment.

AI Prompts to Build This

Copy and paste these into Claude, Cursor, or your favorite AI tool.

1. Project Setup

Create a new Next.js 14 project with TypeScript for an AI Brake Inspection Analyser. Set up: Project structure with app router, a Python FastAPI backend folder for AI processing, database schema for users, inspections, and reports (use Prisma with PostgreSQL), authentication with Clerk, image upload to AWS S3, and API routes for submitting inspections and retrieving results. Include proper TypeScript types, error handling, and environment variables for API keys.

2. Core Feature

Build the brake inspection analysis feature. Requirements: (1) Image upload component that accepts multiple photos of brake components (pads, rotors, calipers), (2) FastAPI endpoint that receives images and runs them through a TensorFlow image classification model, (3) Model should detect: pad thickness/wear percentage, rotor scoring severity (none/light/moderate/severe), glazing presence, visible damage or cracks, (4) Return structured JSON with findings, severity ratings (green/yellow/red), and confidence scores, (5) Generate a report with visual annotations highlighting problem areas. The user flow: mechanic uploads 3-4 photos → sees processing indicator → receives detailed report with repair recommendations.

3. Landing Page

Create a landing page for BrakeCheck AI using Next.js and Tailwind CSS. Include: Hero section with headline "AI-Powered Brake Diagnostics for Mechanics" and subheadline about instant analysis, a demo section showing the upload-to-report flow with sample images, problem/solution sections highlighting inconsistent diagnoses and customer trust issues, feature highlights with icons (instant analysis, shareable reports, parts pricing), pricing section with Free/Pro/Shop tiers, testimonial placeholders, and email capture form. Use a clean, professional design with a dark blue and orange color scheme that appeals to automotive professionals.

4. Branding Package

Create a branding package for BrakeCheck AI, an AI-powered brake inspection tool for auto mechanics. Include: Logo concept (simple, modern mark incorporating a brake disc or checkmark that works at small sizes), color palette (primary: professional dark blue #1E3A5F, secondary: safety orange #F5A623, accent: success green #22C55E), typography pairing (heading: Inter or similar sans-serif, body: system fonts for performance), and usage guidelines. Provide hex codes, font recommendations, and examples of the branding applied to the app interface and marketing materials.