Real-Time M&A Intelligence for 18,000+ Dealerships
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
Overview
A leading automotive advisory firm that provides M&A and investment insights for the U.S. car dealership market struggled to leverage its raw data, coming from over 18,000 dealerships spanning decades. Each record had roughly 150 fields drawn from Polk, Helix, demographic and population datasets and other open sources and APIs. This had issues of inconsistent formats, missing common identifiers that prevented easy merging, and large gaps. These problems slowed extraction of actionable insights: full data refreshes took more than a week and blocked timely, strategic decisions such as dealership valuations.
To resolve the client's data challenges, Shorthills AI developed JumpIQ, an AI-powered platform that ingests and processes raw data from Polk, Helix, and other open APIs directly into Databricks. A robust data engineering pipeline was built for intelligent merging (using techniques like fuzzy matching and address normalization), cleaning, mapping, and formatting to create a unified “golden record” for each dealership. On this refined data foundation, advanced AI/ML models were deployed for predictive analytics, including revenue forecasting, sales efficiency, dealership valuation, and performance scoring—all accessible through a web-based dashboard offering detailed analytical reports and visual insights.
As a result, the client reduced data processing time from over a week to just 8 hours, gained a single clean and accurate database, and obtained significantly stronger predictive insights that enable faster, more confident strategic decisions.
Unifying OEM & dealer data for a leading vehicle marketplac extracting 400K+ models from 1,500 OEM websites and eliminating ~94% uncategorized vehicle listings.
Industry
Automotive Marketplace
Region
North America
Technology
Python
Executive Summary
A leading North American online automotive marketplaces needed to transform massive, inconsistent OEM(Original Equipment Manufacturer) and dealer data into structured, searchable inventory at scale. Shorthills implemented a multi-project program—data extraction and ETL, image/text annotation, and dealer/location verification—backed by custom tooling to monitor OEM site changes and optimize scraping. Outputs land in a unified JSON schema with stringent QA, powering better search, SEO; enhancing platform performance and empowers their future AI initiatives. Results include large-scale ingestion across 1,500 OEM websites and 400,000+ unique models, quarterly maintenance of 45,000 models, and categorized 900k vehicles with help of image annotation using vision models resulting in reduction of uncategorized models from ~30,000 to ~2,000–3,000.
Python (Scrapy, Selenium)
Manual Research & Annotation
Custom Monitoring Tools
Label Studio
Jira
Tech Stack
Executive Summary
A leading North American online automotive marketplaces needed to transform massive, inconsistent OEM(Original Equipment Manufacturer) and dealer data into structured, searchable inventory at scale. Shorthills implemented a multi-project program—data extraction and ETL, image/text annotation, and dealer/location verification—backed by custom tooling to monitor OEM site changes and optimize scraping. Outputs land in a unified JSON schema with stringent QA, powering better search, SEO; enhancing platform performance and empowers their future AI initiatives. Results include large-scale ingestion across 1,500 OEM websites and 400,000+ unique models, quarterly maintenance of 45,000 models, and categorized 900k vehicles with help of image annotation using vision models resulting in reduction of uncategorized models from ~30,000 to ~2,000–3,000.
Tech Stack
Python (Scrapy, Selenium)
Manual Research & Annotation
Custom Monitoring Tools
Jira
Label Studio
Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Our Solutions
Data Foundation: Lakehouse & Entity Resolution
We stood up a Databricks-powered lakehouse with medallion layers (bronze → silver → gold) and survivorship rules to reconcile conflicts. Fuzzy matching plus brand/state heuristics created a durable golden dealer record across renames, mergers, and closures—an analytics-ready backbone with end-to-end lineage.
Signals & Feature Engineering
On unified records, we built a reusable catalog of 150+ signals per dealership spanning performance, market, and macro indicators. Features are standardized across brands/states and versioned over time, so valuations, forecasts, and benchmarks stay fair and reproducible.
Valuation & Forecasting Engines
A model suite blends store performance with market signals to produce explainable valuations and forward-looking forecasts. Scenario/sensitivity views test brand, geography, and macro assumptions—accelerating buy/no-buy calls with consistent methodology.
Delivery Experience: Analyst App for M&A Workflows
A secure analytics app streamlines real M&A tasks: search/filter/compare, geospatial views, and exportable diligence summaries. Built on governed tables and shared definitions, it keeps every stakeholder aligned—from board decks to deep dives.
Challenges
Hundreds of thousands of models across 1500 OEM websites, each with different structures, formatting and multiple datapoints such as Specs, videos, images, options, Manufacturer's Suggested Retail Price(MSRP), PDF’s etc.
Massive volume & diversity
Unstructured, uncategorized data
Inconsistent names/ specifications/ trim; unclear vehicle types; unmapped/outdated dealer locations and inventories.
Fragile extraction & maintenance
Frequent OEM website changes, anti-scraping defences, and high effort to keep the database up-to-date.
Automotive marketplaces that span multiple verticals inherit messy, ever-changing data from OEM and dealer sources. Inconsistent structures, outdated classifications, and fragile extraction pipelines slow search, hamper SEO practices, and inflate operation costs. Without structured schemas and reliable data ingestion, information becomes hard to find and growth slows down.
What Shorthills AI Did
We pulled messy OEM and dealer data into one clean schema and made names, trims, and specs consistent across brands. Our team and AI tools mapped make–model–trim, tagged vehicle types from images, and verified dealer locations and inventories. An OEM change monitor flags site updates so we fix only what changed. QA gates catch outliers, and exceptions go to a small review queue—keeping the catalog accurate without slowing it down.
Data Foundation: ETL at Scale
We enabled automated extraction from OEM sites (model names, URLs, specs, attachments) using Python tooling (BS4, Scrapy, Selenium); transformation into a unified JSON schema and loading to production with QA for accuracy and completeness.
Annotation & Categorization
We delivered make-model-trim mapping, image annotation for vehicle types (e.g., sedan/SUV/hatchback/pickup), and labeled datasets that feed internal LLM/GenAI efforts; tools include Label Studio and Google Sheets.
Dealer & Inventory Verification
We made possible an accurate mapping of dealer locations that listed inventory matches for each dealership—improving reliability for buyers and dealers.
Resilience & Governance
We developed a custom OEM Monitor to track website changes and new model releases to avoid full re-scrapes; Jira-driven delivery with quarterly targets (maintain ~1,500 OEM websites; process 45k models/quarter).
Frequently Asked Questions
Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Tech Stack
Databricks | Python (Django) | React | AWS S3 | Gemini
Outcomes
A leading U.S. vehicle marketplace was held back by uncategorized and inconsistent OEM/dealer data that hurt search, SEO, and operations. With Shorthills AI’s program, 1,500 OEM websites and 400,000+ unique models are standardized into a governed JSON catalog that powers clean filters and reliable listings. Uncategorized models dropped from ~30,000 to ~2,000–3,000, so far fewer vehicles land in “other/unknown.” A change monitor targets maintenance to what actually shifted, enabling quarterly updates across ~1,500 OEM websites and ~45,000 models without full re-scrapes. Dealers benefit from accurate location/inventory mapping, buyers find what they want faster, and SEO improves with consistent naming. Net result: structured data at scale, better discovery, and a stable foundation for downstream analytics and LLM use cases.
Data quality & structure
Fragmented inputs standardized to governed JSON; uncategorized models cut from ~30k → ~2k.
Better search & SEO
Accurate categorization (make/ model/trim/type) and enriched listings improved findability and organic visibility.
Scalable, ongoing operations
Quarterly updates across ~1,500 OEM websites and 45k models with targeted scraping via OEM Monitor.
Also Read
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.