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 research for a financial-services lender—automation blueprint targets ~60% time savings with AI validation and API-first uploads.
Industry
Real Estate Lending
Region
North America
Executive Summary
A financial-services lender relied on a fully manual borrower-research workflow inside its portal: a list of entities was assigned to a 10-person research team (guided by 2 project managers) who gathered contact and company details from public sources, then manually uploaded results. This model was slow, error-prone, costly to scale, and inconsistent across researchers. We documented the process, stabilized day-to-day execution, and proposed an automation blueprint: web data aggregation, AI/NLP-based extraction and validation, a researcher dashboard for exceptions, and API integration to the portal. The roadmap targets ~60% time savings, better data quality, and scalable throughput without proportional headcount.
Tech Stack
YOLO
Qwen 2.5-VL
Django (Python)
React Flow
Next.js
AWS S3
MySQL RDS
SageMaker
Executive Summary
A financial-services lender relied on a fully manual borrower-research workflow inside its portal: a list of entities was assigned to a 10-person research team (guided by 2 project managers) who gathered contact and company details from public sources, then manually uploaded results. This model was slow, error-prone, costly to scale, and inconsistent across researchers. We documented the process, stabilized day-to-day execution, and proposed an automation blueprint: web data aggregation, AI/NLP-based extraction and validation, a researcher dashboard for exceptions, and API integration to the portal. The roadmap targets ~60% time savings, better data quality, and scalable throughput without proportional headcount.
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
Financial services—especially underwriting and portfolio teams—depend on fast, accurate borrower research across scattered public sources. Manual lookups, re-keying, and uneven methods slow turnarounds, raise costs, and introduce quality risk—especially during volume spikes. Without automated aggregation, AI validation, and exception routing, throughput stalls and scaling requires proportional headcount.
Time-intensive, manual research
Researchers navigate many sources, cross-reference, verify, and re-key results.
Hard to scale
A fixed team struggles with volume spikes and tight deadlines.
Inconsistent quality & data entry risk
Different researcher methods and manual uploads lead to variability and errors.
Limited automation & higher cost
No tooling for scraping/validation; operations are expensive for repetitive work.
What Shorthills AI Did
We mapped the end-to-end borrower research process and stabilized daily operations. Then we designed an automation layer that pulls company and contact details from public sources, uses AI to extract and cross-check fields, and sends only exceptions to a researcher dashboard. Clean results flow straight into the portal via APIs—reducing manual lookups, re-keying, and inconsistencies while keeping humans in control for edge cases.
Mapped and Stabilized Operations
We documented the end-to-end research process, aligned stakeholders, and put two project managers over a 10-member team to standardize SOPs and reduce variability while the automation plan was finalized.
Designed Automated Data Aggregation
We drafted a web aggregation service to pull public company/contact data (sites, directories, LinkedIn) and normalize it for portal ingestion.
Built AI Validation with Exception Handling
We specified NLP/ML checks to cross-verify fields across sources, flag discrepancies, and route edge cases to a researcher dashboard for quick human review.
Enabled Scale with API Integration
We outlined batch processing for large borrower lists and API integrations to auto-upload results to the portal—cutting re-keying and preparing the team for ~60% time savings at scale.
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 financial-services lender relied on a 10-person team (with two PMs) to manually research borrowers—slow, error-prone, and costly to scale. After Shorthills standardized SOPs and oversight, delivery became reliable and quality more consistent. With the proposed automation, web data is aggregated, AI validates it, and only exceptions need review—cutting routine work dramatically. Time to complete lists is projected to drop by ~60%, while API uploads eliminate re-keying and reduce data-entry errors. The team can handle volume spikes without proportional headcount, lowering operating cost and turnaround time. In short: steadier day-to-day today, and a clear path to faster, cheaper, higher-quality research tomorrow.
Current (manual, with our management)
Reliable delivery, standardized workflow, and stronger client trust in the data.
Projected (with automation blueprint)
Up to ~60% time savings, lower operating cost, improved data quality, and easier scale—positioning the team to handle higher volumes with agility.
Frequently Asked Questions
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