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 course creation for a global business school with a hyper-personalized AI Tutor—auto-building slides, quizzes, and avatar lectures in 10–15 minutes.
Industry
Ed-tech
Region
APAC
Technology
Gemini 2.5 Flash
Modernizing course creation for a global business school with a hyper-personalized AI Tutor—auto-building slides, quizzes, and avatar lectures in 10–15 minutes.
Industry
Ed-tech
Region
APAC
Technology
Gemini 2.5 Flash
Executive Summary
A global business school needed to reduce the cost and complexity of course creation and delivery while improving student experience. Shorthills built a hyper-personalized AI Tutor platform that automates the end-to-end lifecycle—admin setup, content generation, lecture delivery, interactive Q&A, and assessment—using LLMs (Gemini), deep search, generative video, and voice. Faculty upload a simple unit outline; within 10–15 minutes the system generates slides, quizzes, and an avatar-led lecture tailored to each student’s background, all hosted on AWS. Outcomes: lower operational cost, faculty time freed for mentoring, and consistent, scalable delivery across programs and campuses.
Tech Stack
Gemini 2.5 Flash
SlideSpeak
Tavily
HeyGen
Deepgram
Apache NiFi
Cursor
AWS S3
Executive Summary
A global business school needed to reduce the cost and complexity of course creation and delivery while improving student experience. Shorthills built a hyper-personalized AI Tutor platform that automates the end-to-end lifecycle—admin setup, content generation, lecture delivery, interactive Q&A, and assessment—using LLMs (Gemini), deep search, generative video, and voice. Faculty upload a simple unit outline; within 10–15 minutes the system generates slides, quizzes, and an avatar-led lecture tailored to each student’s background, all hosted on AWS. Outcomes: lower operational cost, faculty time freed for mentoring, and consistent, scalable delivery across programs and campuses.
Tech Stack
Tavily
HeyGen
Apache Nifi
Cursor
Gemini 2.5 Flash
AWS S3
Deepgram
SlideSpeak
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.
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
Higher education—especially multi-campus programs—leans on faculty-heavy content creation and one-size-fits-all delivery. Fragmented authoring, stale materials, and limited real-time support drive up cost and slow rollout, hurting engagement. Without automation and personalization, institutions struggle to deliver consistent, current learning at scale.
High operational cost & inefficiency
Manual, fragmented workflows (content authoring, delivery, assessment) consumed costly faculty hours.
One-size-fits-all
learning
Same lectures for diverse backgrounds led to disengagement or overload; limited real-time support.
Scale & freshness limits
Multi-campus growth made consistency hard; content grew stale in fast-moving domains.
Challenges
Manual, fragmented workflows (content authoring, delivery, assessment) consumed costly faculty hours.
High operational cost & inefficiency
Same lectures for diverse backgrounds led to disengagement or overload; limited real-time support.
One-size-fits-all learning
Multi-campus growth made consistency hard; content grew stale in fast-moving domains.
Scale & freshness limits
Higher education—especially multi-campus programs—leans on faculty-heavy content creation and one-size-fits-all delivery. Fragmented authoring, stale materials, and limited real-time support drive up cost and slow rollout, hurting engagement. Without automation and personalization, institutions struggle to deliver consistent, current learning at scale.
What Shorthills AI Did
We turn a simple unit outline into a full session in minutes. Faculty upload the outline of the course; the AI Tutor builds slides, a quiz bank, and an avatar-led lecture in 10–15 minutes. Lessons adapt to each student’s background, and an in-session chat answers students’ queries with grounded references. Everything runs in one portal, content stays current, and delivery is consistent across programs and campuses.
Unified portal & minimal manual touch
We built a single platform for admins (programs/cohorts/courses), faculty (unit outline upload), and students; human input limited to high-level intent and scheduling.
Automated content generation pipeline
Deep web research → Gemini summarization/structuring → SlideSpeak slide creation → quiz bank (~50 Q/session) → avatar video via HeyGen + Deepgram voice—produced per session in 10–15 minutes.
Personalized delivery & interactive support
We made sure that avatar lectures adapt language/examples to the learner’s academic background; in-session “raise hand” opens contextual chat that searches the web as well, to minimize hallucinations.
Scalable AWS architecture & data layer
We built the platform on AWS with content in S3 and metadata in MongoDB; NiFi orchestrates flows; designed for rapid rollout across programs and campuses.
What Shorthills AI Did
We turn a simple unit outline into a full session in minutes. Faculty upload the outline of the course; the AI Tutor builds slides, a quiz bank, and an avatar-led lecture in 10–15 minutes. Lessons adapt to each student’s background, and an in-session chat answers students’ queries with grounded references. Everything runs in one portal, content stays current, and delivery is consistent across programs and campuses.
Unified portal & minimal manual touch
We built a single platform for admins (programs/cohorts/courses), faculty (unit outline upload), and students; human input limited to high-level intent and scheduling.
Automated content generation pipeline
Deep web research → Gemini summarization/structuring → SlideSpeak slide creation → quiz bank (~50 Q/session) → avatar video via HeyGen + Deepgram voice—produced per session in 10–15 minutes.
Personalized delivery & interactive support
We made sure that avatar lectures adapt language/examples to the learner’s academic background; in-session “raise hand” opens contextual chat that searches the web as well, to minimize hallucinations.
Scalable AWS architecture & data layer
We built the platform on AWS with content in S3 and metadata in MongoDB; NiFi orchestrates flows; designed for rapid rollout across programs and campuses.
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 global business school was spending weeks creating materials and delivering the same lectures to students with diverse academic backgrouds. With our AI Tutor, each session is auto-built in 10–15 minutes—freeing faculty to focus on mentoring and feedback. Personalized lectures and quizzes meet learners where they are, while a live Q&A keeps students engaged and supported. Because each course is generated from a single source of truth, delivery stays consistent across campuses and is easy to keep up to date. The result is lower content-production cost, faster rollout of new units, and a more tailored learning experience at scale.
Significant cost & time reduction
Weeks of content prep compressed to an automated 10–15-minute build per session; faculty focus shifts to high-impact mentoring.
Personalized, consistent learning at scale
Tailored lectures, quizzes, and interactive Q&A delivered consistently across cohorts/campuses.
Always-current curriculum
Deep search keeps materials up-to-date in fast-changing subjects.
Also Read
Outcomes
A global business school was spending weeks creating materials and delivering the same lectures to students with diverse academic backgrouds. With our AI Tutor, each session is auto-built in 10–15 minutes—freeing faculty to focus on mentoring and feedback. Personalized lectures and quizzes meet learners where they are, while a live Q&A keeps students engaged and supported. Because each course is generated from a single source of truth, delivery stays consistent across campuses and is easy to keep up to date. The result is lower content-production cost, faster rollout of new units, and a more tailored learning experience at scale.
Significant cost & time reduction
Weeks of content prep compressed to an automated 10–15-minute build per session; faculty focus shifts to high-impact mentoring.
Personalized, consistent learning at scale
Tailored lectures, quizzes, and interactive Q&A delivered consistently across cohorts/campuses.
Always-current curriculum
Deep search keeps materials up-to-date in fast-changing subjects.