AutoCar Intelligence
How we unified data from 9+ auto repair shops into one source of truth for AutoCar Intelligence
Rebuilding Multi-Location Automotive Intelligence from Broken SaaS Data
A centralized operational intelligence platform that gives owners a single, trustworthy source of truth across all shops without logging into multiple systems. Not a reporting toy—an operational control system.
Locations unified
Data trust
Industry
Automotive Repair & Maintenance
Client
9+ Shop Multi-Location Operator (US)
Engagement
Data Aggregation Platform & Operational Dashboard
Outcome
Unified, AI-ready intelligence across all locations
Tech Stack
React dashboard, Supabase (PostgreSQL) with Edge Functions, Tekmetric API, Chrome extension-based ingestion, AI-ready data model
A look inside the live platform — scroll to explore →
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The Business Problem
The client owned 9 automotive shops (growing to 10+) across multiple US states. Their Shop Management System exposed a limited and inconsistent API, forcing manual workflows and unreliable dashboards.
Data existed—but it was unusable for decision-making. The owner lacked a single, trustworthy view of performance and risk across the company.
01.
Fragmented visibility—Tekmetric required logging into each shop individually
02.
Inaccurate and incomplete API data, missing key metrics like inspections and aging WIP
03.
Operational risk blind spots: high-dollar jobs with no deposits and over-exposure on low-value vehicles
04.
No automation or intelligence—everything relied on manual oversight and spreadsheets
Our Approach
We rebuilt AutoCar Intelligence around a data-first architecture: never trust upstream aggregates, calculate everything ourselves, and store raw and derived data separately for historical analysis.
The system was designed to survive bad APIs and still produce reliable insights—laying the foundation for future AI agents without refactoring.
01.
Centralize data from all shops into a single aggregation engine
02.
Normalize inconsistent fields and structures across APIs and reports
03.
Rebuild sales, profit, and WIP logic using deterministic calculations
04.
Expose risk and performance via drill-down dashboards instead of static reports
The Solution: AutoCar Intelligence Platform
Centralized Multi-Shop Aggregation: Unified aggregation layer pulls data shop-by-shop, normalizes inconsistent fields, and stores historical snapshots for month-over-month and year-over-year comparison.
Hardened Sales & Profit Calculations: Deterministic logic for labor, parts, and sublet sales, along with authorized vs unauthorized sublets and RO averages, so owners can trust every number.
Aging Work-In-Progress Intelligence: Tracks approved repair orders across all shops, flags high-dollar jobs without deposits, and surfaces aging risk by RO age, vehicle value, and exposure.
Hybrid Inspection & Compliance Tracking: Uses both API data and Chrome extension–based extraction to capture inspection metrics that Tekmetric's API doesn't expose, then normalizes them into the central model.
AI-Ready Architecture: Clear event boundaries and deterministic rules first, AI second—so future agents can safely monitor deposits, WIP aging, and anomalies without rearchitecting the system.
Technical Architecture
React-based dashboard, Supabase with PostgreSQL and Edge Functions, Tekmetric API integration, and a Chrome extension ingestion path for non-API reports. Raw and derived data are stored separately to enable deep historical analysis, not just current-state reporting.
Business Impact
AutoCar Intelligence transformed fragmented, unreliable data into a unified control system—giving leadership confidence in their numbers and visibility into risk before it becomes loss.
0+
Locations unified
One dashboard across multiple shops
0%
Data trust
Deterministic calculations over bad APIs
0 source
Truth
Raw and derived data stored separately
0 blind spots
Visibility
Risk surfaced before loss
Why This Matters
At TechEmulsion, we specialize in turning messy, real-world data into production-grade intelligence systems. AutoCar Intelligence shows how a data-first architecture can transform broken SaaS APIs into a durable strategic asset—and lay the groundwork for safe, effective AI.