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 family-history capture for a UK healthcare provider—patient-led chatbot cuts pedigree charting time by 93% and doubles clinician throughput.
Industry
Healthcare
Region
EMEA
Technology
Gemma
Revolutionizing family-history capture for a UK healthcare provider—a patient-led chatbot cuts pedigree charting time by 93% and delivers the double clinician throughput.
Industry
Healthcare
Region
EMEA
Technology
Gemma
Executive Summary
A UK-based healthcare organization needed to free clinicians from drawing family-history pedigree charts during appointments. Manual capture took 10–15 minutes per patient, relied on rushed recall, and produced hand-drawn charts that couldn’t be reused across systems. Shorthills built a patient-facing chatbot that collects family history pre-visit through a structured, adaptive Q&A, then auto-generates an editable digital pedigree chart. The assistant can also summarize uploaded clinical documents (lab/radiology/PET) to give physicians a consolidated view. Result: the solution shifted work out of the visit, standardized data, and sped clinical workflows—delivering a 93% reduction in charting time and enabling clinicians to see 2× more patients in the same window.
Tech Stack
Gemma
FastAPI
Django (Python)
React Flow
Next.js
MySQL
Executive Summary
A UK-based healthcare organization needed to free clinicians from drawing family-history pedigree charts during appointments. Manual capture took 10–15 minutes per patient, relied on rushed recall, and produced hand-drawn charts that couldn’t be reused across systems. Shorthills built a patient-facing chatbot that collects family history pre-visit through a structured, adaptive Q&A, then auto-generates an editable digital pedigree chart. The assistant can also summarize uploaded clinical documents (lab/radiology/PET) to give physicians a consolidated view. Result: the solution shifted work out of the visit, standardized data, and sped clinical workflows—delivering a 93% reduction in charting time and enabling clinicians to see 2× more patients in the same window.
Tech Stack
Gemma
FastAPI
Next.js
MySQL
Django (Python)
React Flow
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
Healthcare clinics rely on accurate family history, but in-visit, hand-drawn pedigree charts are slow, error-prone, and hard to reuse across systems. Reliance on rushed patient recall and paper charts lengthens visits and limits throughput.
Time-consuming manual creation
Clinicians spent 10–15 minutes drawing each pedigree chart during the visit.
Inaccurate patient recall under pressure
Live appointments made it hard for patients to remember full family history.
Low data
reusability
Hand-drawn charts weren’t easily editable or shareable across systems.
Challenges
Clinicians spent 10–15 minutes drawing each pedigree chart during the visit.
Time-consuming manual creation
Inaccurate patient recall under pressure
Live appointments made it hard for patients to remember full family history.
Hand-drawn charts weren’t easily editable or shareable across systems.
Low data reusability
Healthcare clinics rely on accurate family history, but in-visit, hand-drawn pedigree charts are slow, error-prone, and hard to reuse across systems. Reliance on rushed patient recall and paper charts lengthens visits and limits throughput.
What Shorthills AI Did
We moved family-history collection to the pre-visit stage by having patients complete their family medical history before the appointment. A secure chatbot asks clear, guided questions, fills gaps with smart follow-ups, and turns responses into a clean, editable digital pedigree chart. Clinicians just review and adjust in session. Patients can also upload lab/radiology/PET reports for quick summaries—so doctors see a consolidated view without extra paperwork.
Interactive patient-led data collection
We built a secure, intuitive chatbot that gathers family history pre-appointment via structured Q&A with dynamic follow-ups for accuracy and completeness.
Automated digital chart generation
Patient inputs are instantly turned into a clean, editable digital pedigree chart that patients can download and clinicians can review in session.
AI-supported medical report summarization
We designed it to ingest and summarize uploaded clinical documents (lab, radiology, PET) to present a cohesive medical background.
What Shorthills AI Did
We moved family-history collection to the pre-visit stage by having patients complete their family medical history before the appointment. A secure chatbot asks clear, guided questions, fills gaps with smart follow-ups, and turns responses into a clean, editable digital pedigree chart. Clinicians just review and adjust in session. Patients can also upload lab/radiology/PET reports for quick summaries—so doctors see a consolidated view without extra paperwork.
Interactive patient-led data collection
We built a secure, intuitive chatbot that gathers family history pre-appointment via structured Q&A with dynamic follow-ups for accuracy and completeness.
Automated digital chart generation
Patient inputs are instantly turned into a clean, editable digital pedigree chart that patients can download and clinicians can review in session.
AI-supported medical report summarization
We designed it to ingest and summarize uploaded clinical documents (lab, radiology, PET) to present a cohesive medical background.
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 UK healthcare provider was losing appointment time to hand-drawn pedigree charts and rushed patient recall. With Shorthills AI’s patient-led assistant, history is collected pre-visit and auto-rendered as an editable digital chart for clinician review. Charting time drops by ~93%, letting clinicians see ~2× more patients in the same window. Standardized, reusable charts reduce errors, improve continuity across systems, and keep visits focused on care—not drawing.
93% less clinician charting time
Pedigree creation time drops from minutes to near-instant.
2× patient throughput
Clinicians can see twice as many patients in the same timeframe.
Also Read
Outcomes
A UK healthcare provider was losing appointment time to hand-drawn pedigree charts and rushed patient recall. With Shorthills AI’s patient-led assistant, history is collected pre-visit and auto-rendered as an editable digital chart for clinician review. Charting time drops by ~93%, letting clinicians see ~2× more patients in the same window. Standardized, reusable charts reduce errors, improve continuity across systems, and keep visits focused on care—not drawing.
93% less clinician charting time
Pedigree creation time drops from minutes to near-instant.
2× patient throughput
Clinicians can see twice as many patients in the same timeframe.