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.
Modernizing borrower acquisition for a private residential lender—human-in-the-loop entity resolution delivers ≥90% verified leads and scalable nationwide coverage.
Industry
Real Estate Lending
Region
North America
Executive Summary
A private residential real-estate lender struggled to identify and qualify the right borrowers. Unstructured SPV-level data hid true parent entities; manual, costly outreach missed niche “fix-and-flip” developers; and sales lacked borrower intelligence for risk-aware pitches. Shorthills AI delivered a human-in-the-loop research and data-enrichment service that resolves SPVs to ultimate owners, verifies contacts, and compiles borrower intelligence (history, default risk signals, competitor relationships, geo focus). The result: high-accuracy, pre-qualified leads; reduced cost per lead; faster, better-informed lending decisions; and scalable nationwide coverage.
Tech Stack
YOLO
Qwen 2.5-VL
Django (Python)
React Flow
Next.js
AWS S3
MySQL RDS
SageMaker
Executive Summary
A private residential real-estate lender struggled to identify and qualify the right borrowers. Unstructured SPV-level data hid true parent entities; manual, costly outreach missed niche “fix-and-flip” developers; and sales lacked borrower intelligence for risk-aware pitches. Shorthills AI delivered a human-in-the-loop research and data-enrichment service that resolves SPVs to ultimate owners, verifies contacts, and compiles borrower intelligence (history, default risk signals, competitor relationships, geo focus). The result: high-accuracy, pre-qualified leads; reduced cost per lead; faster, better-informed lending decisions; and scalable nationwide coverage.
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.
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
Private residential lenders often face opaque ownership hidden behind SPVs and scattered data, making outreach costly and low-yield. Manual prospecting misses niche fix-and-flip developers and weakens risk-aware pitching. With clean parent-entity resolution and verified contacts, teams cut acquisition costs, lift conversion, and scale coverage predictably.
Unstructured & fragmented borrower data
Developers used multiple SPVs, obscuring true ownership and track record.
Inefficient, costly lead generation
Traditional/digital marketing couldn’t precisely target niche developers; manual surveys didn’t scale.
Limited risk intelligence
No consolidated view of borrowing history, defaults, or competitor-lender ties.
Scalability constraints
Research, collation, and qualification were labor-intensive and hard to grow.
What Shorthills AI Did
We turn messy SPV-level records into clean borrower profiles. Our team resolves each SPV to its real owner, verifies direct contacts, and compiles decision-ready intelligence—borrowing history, risk flags, competitors, geography, and property types. Only pre-qualified, high-intent targets reach sales, with accuracy checks baked in and a steady cadence of refreshed leads and reports.
Discovery & Problem Framing
We mapped data sources, current workflows, pain points, and goals (lead gen + risk), defining a transformation path from raw SPV/property data to an intelligent borrower database.
Research & Entity Resolution
We identified ultimate parent entities/individual owners behind SPVs and clustered related entities (shared addresses/directorships, etc.) to form a holistic developer profile.
Contact Enrichment & Verification
We sourced key contacts (names, direct phones, verified emails, LinkedIn, addresses) and validated them—manual phone calls during U.S. business hours and email verification—before handing to sales.
Borrower Intelligence & Reporting
We compiled decision-ready intelligence (borrowing cadence, typical amounts, default/stalled projects, competitor lenders, geography, property types) and delivered reports on a client-defined cadence (daily/weekly/quarterly).
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.
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.
Outcomes
A private residential lender was missing the right “fix-and-flip” borrowers because SPVs hid true owners and outreach was costly and hit-or-miss. With Shorthills AI’s human-in-the-loop enrichment, SPVs are resolved to ultimate parents and contacts are verified, delivering high-quality, pre-qualified leads. Accuracy exceeds ~90% for parent identification and contact verification, so reps spend less time chasing bad data and more time converting. Intelligence on borrowing cadence, defaults, and competitor ties sharpens pitches and speeds risk-aware decisions. Lead acquisition costs fall, conversion rises, and coverage scales from regional to nationwide via a predictable delivery cadence (daily/weekly/quarterly). In short: clearer ownership, better targets, and faster, smarter lending decisions at scale.
Verified, Accurate Leads
High-quality leads with 90%+ parent-entity identification and contact verification
Lower Cost, Higher Conversion
Pre-qualified, intelligence-rich targets that reduce acquisition cost and improve win rates.
Scalable, Risk-Aware Growth
Expand from regional to nationwide outreach with faster, more informed decisions.
Frequently Asked Questions
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