Jeremy Longshore

Executive Summary

DiagnosticPro represents a paradigm shift in vehicle diagnostics - transforming complex professional-grade analysis into accessible, AI-powered insights. Built in days, not months, this platform demonstrates how modern AI and cloud infrastructure can rapidly deliver enterprise-scale solutions.

Key Metrics:

The Challenge: Democratizing Professional Diagnostics

Vehicle diagnostics has long been the domain of expensive professional equipment and specialized knowledge. Mechanics invest thousands in diagnostic tools, while car owners face uncertainty about repair costs and technical accuracy.

Traditional Pain Points

Solution: AI-Powered Diagnostic Intelligence

DiagnosticPro bridges this gap by combining GPT-4’s analytical capabilities with a massive, continuously-updated knowledge base of real-world repair data.

Core Value Proposition

  1. Professional-Grade Analysis - GPT-4 provides mechanic-level diagnostic interpretation
  2. Comprehensive Context - 254+ data tables provide real-world repair scenarios
  3. Cost Transparency - Accurate repair cost estimates with regional variations
  4. Actionable Insights - Step-by-step repair guidance and part recommendations

Technical Architecture: Multi-Project Ecosystem

The platform uses a sophisticated multi-project workspace architecture designed for scalability and data integrity.

Project Structure

diagnosticpro/
├── daily-energizer-workflow/  # N8N automation for content generation
├── rss_feeds/                 # RSS feed testing & curation (226 feeds)
├── schema/                    # BigQuery schemas & validation (254 tables)
└── scraper/                   # Data collection from YouTube/Reddit/GitHub

Data Flow Architecture

The system follows a strict Export Gateway Pattern ensuring data integrity:

graph LR
    A[YouTube Scraper] --> D[Export Gateway]
    B[Reddit Collector] --> D
    C[GitHub Miner] --> D
    D --> E[Schema Validation]
    E --> F[BigQuery Import]
    F --> G[DiagnosticPro API]

Key Principles:

Implementation Deep Dive

1. Multi-Source Data Collection

YouTube Data Mining

# youtube_scraper/data_extractor.py
class YouTubeDataExtractor:
    def extract_repair_scenarios(self, channel_data):
        """Extract structured repair data from automotive channels"""
        scenarios = []
        for video in channel_data:
            if self.is_diagnostic_content(video):
                scenario = {
                    'vehicle_info': self.extract_vehicle_details(video),
                    'symptoms': self.extract_symptoms(video),
                    'diagnostic_steps': self.extract_procedures(video),
                    'resolution': self.extract_resolution(video)
                }
                scenarios.append(scenario)
        return scenarios

Reddit Mining (500K+ Posts)

# praw/massive_500k_collector.py
def collect_diagnostic_discussions():
    """Massive collection of real-world diagnostic scenarios"""
    subreddits = ['MechanicAdvice', 'Cartalk', 'autorepair', 'Justrolledintotheshop']
    for subreddit in subreddits:
        posts = reddit.subreddit(subreddit).top('all', limit=None)
        for post in posts:
            if is_diagnostic_relevant(post):
                store_diagnostic_scenario(post)

GitHub Integration

Mining open-source automotive projects for technical specifications and repair procedures.

2. RSS Feed Curation (226+ Feeds)

# rss_feeds/test_rss_feeds.py
def validate_feed_quality():
    """Ensure 226+ feeds provide quality automotive content"""
    feeds = load_automotive_feeds()
    for feed in feeds:
        quality_score = assess_content_quality(feed)
        if quality_score > QUALITY_THRESHOLD:
            include_in_pipeline(feed)

3. BigQuery Schema Design (254+ Tables)

Core Schema Structure

-- diagnosticpro_prod dataset structure
CREATE TABLE diagnostic_scenarios (
    scenario_id STRING,
    vehicle_make STRING,
    vehicle_model STRING,
    year INT64,
    error_codes ARRAY<STRING>,
    symptoms ARRAY<STRING>,
    diagnostic_steps ARRAY<STRUCT<
        step_number INT64,
        procedure STRING,
        expected_result STRING
    >>,
    resolution STRING,
    cost_estimate STRUCT<
        parts_cost FLOAT64,
        labor_hours FLOAT64,
        total_estimate FLOAT64
    >,
    confidence_score FLOAT64,
    data_source STRING,
    created_timestamp TIMESTAMP
);

Performance Optimization

4. Export Gateway Pattern

# Data flow through export gateway
scraper/export_gateway/
├── raw/              # Raw collected data
├── validated/        # Schema-validated data
├── cloud_ready/      # BigQuery-ready imports
└── archived/         # Historical data

5. AI Integration with GPT-4

class DiagnosticAnalyzer:
    def __init__(self):
        self.openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
        self.bigquery_client = bigquery.Client()
    
    def analyze_diagnostic_request(self, error_codes, symptoms, vehicle_info):
        """Combine AI analysis with real-world data"""
        
        # Query BigQuery for similar scenarios
        similar_scenarios = self.query_similar_scenarios(
            error_codes, vehicle_info
        )
        
        # Prepare context for GPT-4
        context = self.build_diagnostic_context(
            similar_scenarios, error_codes, symptoms
        )
        
        # GPT-4 analysis
        response = self.openai_client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": DIAGNOSTIC_SYSTEM_PROMPT},
                {"role": "user", "content": context}
            ]
        )
        
        return self.parse_diagnostic_response(response)

Results and Impact

Performance Metrics

User Impact

Traditional Diagnostic Process:
1. Visit mechanic → 2-3 hours, $150+ diagnostic fee
2. Get estimate → Often inflated, limited explanation
3. Second opinion → Additional time and cost
Total: 4-6 hours, $200-400 before any work

DiagnosticPro Process:
1. Input error codes → 30 seconds
2. Receive comprehensive analysis → $29.99
3. Get multiple repair options → Instant
Total: 2 minutes, $29.99 with actionable insights

Business Results

Technology Stack

Core Infrastructure

Data Pipeline

Development Tools

Rapid Development Methodology

Day 1: Architecture and Data Collection

Day 2: Data Pipeline and Validation

Day 3: AI Integration and Analysis

Day 4: Frontend and Launch

Key Success Factors

  1. Pre-built Components: Leveraged existing scraping and data tools
  2. Cloud-Native Design: BigQuery handled scale without infrastructure overhead
  3. AI-First Approach: GPT-4 eliminated months of rule-based programming
  4. Parallel Development: Multi-project workspace enabled concurrent development

Lessons Learned

What Worked

Challenges Overcome

Future Roadmap

Phase 2: Enhanced Features

Phase 3: Professional Tools

Conclusion: Redefining Development Speed

DiagnosticPro demonstrates that modern AI and cloud infrastructure can compress traditional development timelines from months to days. The key insights:

  1. AI as a Force Multiplier: GPT-4 eliminated the need for complex rule engines
  2. Cloud-Native Scale: BigQuery handled enterprise-scale data from day one
  3. Clean Architecture: Export gateway pattern enabled rapid iteration
  4. Multi-Source Intelligence: Diverse data sources created comprehensive insights

The platform processes thousands of diagnostic scenarios, provides professional-grade analysis, and delivers measurable value to users - all built in under a week.

This case study proves that with the right architecture, tools, and approach, revolutionary products can be built at unprecedented speed without sacrificing quality or scale.

Ready to revolutionize your development process?

Contact: jeremy@intentsolutions.io
DiagnosticPro: Transforming Vehicle Diagnostics with AI

Built with: Python, GCP, BigQuery, OpenAI GPT-4, and relentless focus on delivery speed

#Case-Study #Ai #Bigquery #Gcp #Python #Rapid-Development