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.
Frequently Asked Questions
Digitizing archive of hand-drawn pedigree charts for a healthcare organization—using a custom ML solution to deliver 93% faster processing and 97% quicker retrieval.
Industry
Healthcare
Region
EMEA
Technology
AWS
Executive Summary
A healthcare organization held decades of hand-drawn pedigree charts in paper files—impossible to search, update, or analyze at scale. Manual digitization took ~15 minutes per chart and didn’t scale to thousands, leaving high-value genetic data underused. Shorthills built a custom ML pipeline that detects nodes/relationships/symbols and extracts handwritten text, then renders clean, editable digital charts in a web app (DigiTree). Trained on 2,500+ synthetic charts to protect privacy, the solution cuts digitization time by 93% and reduces retrieval time by 97%.
Tech Stack
YOLO
Qwen 2.5-VL
Django (Python)
React Flow
MySQL RDS
Next.js
AWS S3
SageMaker
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.
Executive Summary
A healthcare organization held decades of hand-drawn pedigree charts in paper files—impossible to search, update, or analyze at scale. Manual digitization took ~15 minutes per chart and didn’t scale to thousands, leaving high-value genetic data underused. Shorthills built a custom ML pipeline that detects nodes/relationships/symbols and extracts handwritten text, then renders clean, editable digital charts in a web app (DigiTree). Trained on 2,500+ synthetic charts to protect privacy, the solution cuts digitization time by 93% and reduces retrieval time by 97%.
Tech Stack
YOLO
Qwen 2.5-VL
Django (Python)
React Flow
Next.js
AWS S3
MySQL RDS
SageMaker
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.
Challenges
Thousands of paper charts couldn’t be searched or analyzed.
Inaccessible & unusable archives
Unscalable manual digitization
~15 minutes per chart made large-scale processing impractical.
Lost research potential
Under utilized familial/genetic data limited modern analytics.
Healthcare genetics teams often sit on decades of paper pedigrees that are hard to search, update, or analyze. Manual digitization is slow and costly, leaving high-value familial data underused and delaying research and care decisions.
What Shorthills AI Did
We turned stacks of hand-drawn pedigree charts into clean, editable digital trees. The system reads symbols and handwriting from scans, rebuilds the family tree, and renders it in a web app where clinicians can search, edit, and export. To protect privacy, it’s trained on synthetic charts and flags any sensitive details for redaction—so teams get speed without risking compliance.
Custom computer-vision + handwriting pipeline
We built a dual-model approach: YOLO for objects (nodes, relationships, symbols) and Qwen 2.5-VL for handwritten text; fine-tuned for accurate extraction.
Privacy-preserving synthetic data
We trained the models on 2500+ synthetically generated charts; pipeline detects/redacts PII to maintain compliance.
Interactive frontend (DigiTree)
Extracted data is rendered as editable pedigree charts using a React Flow library—bringing digitization from ~15 minutes to under 1 minute per chart.
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.
Outcomes
A healthcare organization had decades of paper pedigrees that took ~15 minutes each to digitize and were impossible to search at scale. With Shorthills AI’s DigiTree, charts are converted to editable digital versions in under 1 minute—about a 93% reduction in effort—so backlogs clear fast and new charts are processed on the fly. Structured records make lookups and cross-patient searches nearly instant, cutting retrieval time by ~97% and bringing hidden patterns to light. Because the pipeline standardizes symbols, text, and relationships, updates stay consistent and audit-ready, while synthetic training data and redaction safeguards protect privacy. Clinicians spend less time redrawing and more time interpreting family risk, and researchers can finally analyze cohorts across thousands of charts. In short: searchable pedigrees, faster workflows, and better use of genetic insight at scale.
93% reduction in digitization time
From ~15 minutes to under 1 minute per chart.
97% reduction in search/retrieval time
Enabling instant lookup across structured digital charts.
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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