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.
Outcomes
An automotive M&A team was spending 2–3 days per Impact Report, stitching PDFs and spreadsheets and limiting deal-speed comparisons. With Shorthills AI’s platform, reports generate in under 2 minutes—~99.9% faster—so analysts can evaluate multiple targets and run what-if scenarios on demand. A centralized, clean dataset covers 18,000+ dealerships, eliminating manual wrangling and standardizing results. Narrative insights and visuals are consistent across users, improving confidence with buyers and boards. The capability is productized for ~40–50 power users, creating a subscription revenue stream alongside faster, better decisions.
~99.9% faster report generation
From 2–3 days to under 2 minutes per Impact Report.
Centralized, clean data at scale
Unified intelligence for 18,000+ dealerships, eliminating manual wrangling.
Faster, more confident decisions + new revenue stream
Rapid comparisons/what-if analysis and a subscription product for high-value users.
Modernizing automotive M&A diligence with Agentic AI—unifying 18,000+ dealerships to auto-generate Impact Reports in under 2 minutes.
Industry
Automotive
Region
North America
Technology
Gemini
Executive Summary
A leading automotive M&A advisory team needed a faster way to value dealerships and generate due-diligence “Impact Reports.” Raw, scattered data across sources (e.g., Polk/Helion), manual collation, and hand-written analysis made each report take 2–3 days, blocking rapid comparisons and what-if exploration. We built an AI-powered platform that unifies data for 18,000+ dealerships, provides an interactive dashboard and portfolio builder, and auto-generates multi-page PDF reports using an agentic workflow with Gemini—cutting report time to under 2 minutes while improving scale and consistency. The platform productizes expertise as a subscription for high-value users.
Tech Stack
Agentic AI (Google Gemini)
Python (Django)
React
PostgreSQL (AWS RDS)
FastAPI
ETL Pipelines
What Shorthills AI Did
We pulled scattered dealership data into one clean source and gave analysts a simple web app to search, compare, and build portfolios. From there, an agentic AI workflow turns each request into a multi-page “Impact Report” in minutes—pulling the latest numbers, adding news context, and writing a clear narrative with charts. Teams stop copy-pasting and hand-writing analyses; they click, review, and download.
We engineered automated pipelines to ingest raw multi-source data, clean inconsistencies (e.g., normalize field names), and load a centralized 100GB+ relational store—establishing a reliable single source of truth.
Built a Golden Dataset & ETL
We built a modern app (Python/Django + React) with filters by state/brand/KPIs, map/table views, and a portfolio builder for side-by-side comparisons and trend tracking.
Delivered the Web Platform & UX
We orchestrated a Gemini-powered workflow that pulls the golden dataset, researches recent news, synthesizes narrative insights, and outputs a multi-page PDF—from request to download in <2 minutes.
Automated Impact Reports with Agentic AI
We packaged capabilities for ~40–50 power users, enabling a paid SaaS model while standardizing quality and turnaround.
Productized as a Subscription
Executive Summary
A leading automotive M&A advisory team needed a faster way to value dealerships and generate due-diligence “Impact Reports.” Raw, scattered data across sources (e.g., Polk/Helion), manual collation, and hand-written analysis made each report take 2–3 days, blocking rapid comparisons and what-if exploration. We built an AI-powered platform that unifies data for 18,000+ dealerships, provides an interactive dashboard and portfolio builder, and auto-generates multi-page PDF reports using an agentic workflow with Gemini—cutting report time to under 2 minutes while improving scale and consistency. The platform productizes expertise as a subscription for high-value users.
Tech Stack
Agentic AI (Google Gemini)
Python (Django)
React
PostgreSQL (AWS RDS)
ETL Pipelines
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.
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.
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
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.
Challenges
Automotive M&A teams wrestle with fragmented market and dealer data spread across PDFs, spreadsheets, and APIs. Manual collation and hand-written analysis slow valuations, limit side-by-side comparisons, and inflate cost per deal. Without automated discovery and standardized reporting, decisions lag and opportunities slip.
Scattered & inconsistent data
Multiple sources, formats (PDF/Excel), and naming inconsistencies; no single source of truth.
Manual, slow reporting
Analysts spent 2–3 days per Impact Report (searching, calculating, writing).
Limited scale & scenario analysis
Couldn’t compare portfolios or run rapid “what-if” scenarios at deal speed.
Outcomes
Unify all your disparate sources into a governed data lakehouse, resolve duplicates to a single “golden record,” and standardize key signals so analysts can trust the data. That’s how we built JumpIQ for a leading U.S. automotive M&A firm: we consolidated decades of data across 18,000+ dealerships, cut refresh time from 7+ days to ~8 hours, and engineered 150+ metrics per store. On top, we added explainable valuation and forecasting models so you can run what-ifs on brand, geography, and macro factors. The result: faster, defensible diligence with scenario planning directly from your historical data.
Drastic Speed Improvement
Full data ingestion and refresh cycles reduced from over a week to 8 hours.
Enhanced Predictive Accuracy
Unified, clean database for 18,000+ dealerships, each with ~150 data points.
Comprehensive & Accurate Data
More reliable forecasts for Key Performance Indicators, sales, and valuations.
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