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.
What Shorthills AI Did
We pulled messy vendor and internal data into one clean Azure pipeline, standardizing formats and keeping only the most reliable sources. Business rules de-duplicated loans (including bulk loans), fixed bad fields, and linked parent–child companies so each borrower has a single, trustworthy view. From this foundation, fast dashboards and simple apps surface portfolio risk, borrower signals, and market opportunities—ready for future ML and text-to-SQL so non-technical users can ask questions in plain English.
Unified Ingestion & ETL on Azure
We built Azure Data Factory pipelines to ingest and standardize third-party and internal data—narrowing from 5–6 providers to the two most viable—ready for downstream processing.
Domain-Driven Cleansing & Structuring on Databricks
We engineered quality checks and business-rule transforms (Databricks + Python) to filter out non-B2B/residential noise, de-duplicate loans (incl. bulk loans), and publish a governed golden dataset.
Entity Mapping at Scale
We implemented a robust entity-resolution layer to link parent–child companies, reducing ~40M raw records to ~10M unique parent entities for accurate borrower views.
Insights & Access: Dashboards and Apps
We delivered Power BI dashboards and tailored apps for sales, risk, and loan teams—plus a path to GenAI text-to-SQL for natural-language querying—cutting latency from >20s to ms/few seconds.
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 residential real-estate lending data on Azure—entity-resolved golden records cut app latency from >20s to ms and deliver 70–80% early rectification.
Industry
Real Estate Lending
Region
North America
Technology
Databricks
Executive Summary
A U.S. private lender in residential real-estate lending struggled with poor data quality, inconsistent vendor feeds, and scattered, unstructured records—slowing risk assessment, origination, and sales planning. We delivered a multi-layer data platform on Azure that standardizes vendor inputs, resolves complex entity relationships, de-duplicates loan transactions (including bulk loans), and produces a governed golden dataset for analytics. Interactive dashboards and bespoke apps now surface borrower insights, portfolio risk, and market opportunities, with application latency dropping from >20 seconds to milliseconds/few seconds. The foundation also supports future ML (forecasting, clustering) and GenAI text-to-SQL for non-technical users.
Tech Stack
Databricks
Python
Next.js
React Flow
FastAPI
Redis
Selenium
Azure Ecosystem – Synapse, Azure Functions, Storage, Vault, Serverless SQL Pool, Polar
Executive Summary
A U.S. private lender in residential real-estate lending struggled with poor data quality, inconsistent vendor feeds, and scattered, unstructured records—slowing risk assessment, origination, and sales planning. We delivered a multi-layer data platform on Azure that standardizes vendor inputs, resolves complex entity relationships, de-duplicates loan transactions (including bulk loans), and produces a governed golden dataset for analytics. Interactive dashboards and bespoke apps now surface borrower insights, portfolio risk, and market opportunities, with application latency dropping from >20 seconds to milliseconds/few seconds. The foundation also supports future ML (forecasting, clustering) and GenAI text-to-SQL for non-technical users.
Tech Stack
Azure Ecosystem – Synapse, Azure Functions, Storage, Vault, Serverless SQL Pool, Polar
Databricks
Python
Next.js
FastAPI
Redis
Selenium
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.
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 U.S. private lender was slowed by inconsistent vendor feeds, duplicate loans, and scattered records that made risk and sales planning hard. With Shorthills AI’s Azure lakehouse, data now lands in a governed “golden” dataset that teams trust, replacing noisy files with a single source of truth. Entity resolution shrank ~40M raw records to ~10M unique parent entities, while early domain cleanup delivered ~70–80% rectification. Dashboards and apps load in milliseconds to a few seconds (down from >20s), so analysts move from waiting to acting. Because the platform narrows vendors to the best two and supports future ML and GenAI text-to-SQL, the lender gets sharper targeting, clearer portfolio risk, and faster decisions—without growing headcount.
Reliable single source of truth
Trustworthy, analytics-ready data replaces inconsistent vendor deliverables.
Performance & usability
App/report latency reduced from >20s to ms/few seconds; easier data access for business users.
Quality uplift at scale
Achieved 70–80% rectification early; robust entity resolution and de-duplication.
Sharper strategy & risk
Target high-growth geographies, refine sales strategy, and improve portfolio risk visibility.
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.
Frequently Asked Questions
Challenges
Private lenders—especially in residential real-estate—battle messy vendor feeds, duplicate loans, and scattered, unstructured records that slow risk assessment and origination. Inconsistent formats and weak entity mapping inflate costs and stall decisions. With governed pipelines, de-duplication, and parent–child resolution, teams get a single source of truth, faster dashboards, and clearer portfolio signals without adding headcount.
Low data quality & inconsistency
Vendor feeds varied in format and accuracy; significant rectification remained after third-party “cleaning.”
Disparate sources & unstructured data
Multiple ETLs and formats (including irrelevant records) blocked analytics and decision making.
Limited domain-specific insight
No reliable entity mapping (parent/child), duplicate loans, and weak B2B lending signals.
Operational friction
Slow dashboards, difficult access, and limited ability to scale analysis.
Also Read
