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 valuation research with AI—auto-transcribing expert calls and extracting multiples to cut processing to ~5 minutes (~95% faster).
Industry
Automotive
Region
North America
Technology
AWS
Executive Summary
A valuation advisory team had critical financial insight trapped in hour-long expert interviews. Analysts manually listened, transcribed, and copied valuation multiples—taking 3+ hours per call and risking transcription errors. We built an end-to-end AI pipeline that ingests recordings, produces high-fidelity transcripts, extracts valuation multiples, and generates valuation-focused summaries—creating a searchable knowledge base and cutting processing to ~5 minutes per call.
Tech Stack
OpenAI GPT-4o
ElevenLabs
Weaviate
Apache NiFi
AWS
Frequently Asked Questions
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
Executive Summary
A valuation advisory team had critical financial insight trapped in hour-long expert interviews. Analysts manually listened, transcribed, and copied valuation multiples—taking 3+ hours per call and risking transcription errors. We built an end-to-end AI pipeline that ingests recordings, produces high-fidelity transcripts, extracts valuation multiples, and generates valuation-focused summaries—creating a searchable knowledge base and cutting processing to ~5 minutes per call.
Tech Stack
OpenAI GPT-4o
ElevenLabs
AWS
Weaviate
Apache NiFi
Outcomes
A valuation advisory team was spending 3+ hours per call to find a few ratios, with errors creeping in from manual transcription. With Shorthills AI’s pipeline, each recording is processed in ~5 minutes—about a 95% time reduction—delivering accurate transcripts, extracted multiples, and a concise summary. Analysts search a growing knowledge base to compare calls, spot trends, and prep faster for deals. Because extraction is consistent and audit-friendly, rework drops and confidence rises. Net result: less time chasing numbers, more time making decisions.
~95%+ time reduction
From 3+ hours of manual effort to ~5 minutes per call.
Higher accuracy & repeatability
Reduced human error; consistent extraction and summaries at scale.
Searchable intelligence hub
Lasting, queryable archive of expert conversations for faster deal prep.
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
Valuation teams often rely on hour-long expert calls where key ratios hide in messy audio and notes. Manual transcription and extraction slow analysis, invite errors, and stall cross-call comparison—raising cost per insight. With automated transcripts, structured multiple extraction, and a searchable knowledge base, teams move from chasing numbers to making decisions.
Manual, slow extraction
Hours of analyst time per call to find a handful of ratios/multiples.
Error-prone transcription
Financial figures and jargon were easy to mishear or miskey.
Inaccessible knowledge
Insights were locked inside video files; cross-call search/compare wasn’t possible.
What Shorthills AI Did
We turn hour-long expert calls into usable, searchable insight. New recordings are picked up automatically, transcribed with finance-aware accuracy, and scanned for key valuation multiples (EV/EBITDA, P/S, price-per-vehicle-sold). The system writes a short, valuation-focused summary, saves everything to a searchable knowledge base, and emails results to analysts—so they get the numbers and context in minutes, not hours.
We auto-detected new recordings from cloud folders (e.g., Zoom/OneDrive/Drive), isolated audio, and queued files for processing.
Automated capture & preparation
We used a state-of-the-art speech-to-text engine (ElevenLabs) for accurate, speaker-aware transcripts purpose-built for finance conversations.
High-fidelity transcription
We fine-tuned an LLM (GPT-4o) to pull EV/EBITDA, P/S, price-per-vehicle-sold and generate concise, valuation-specific summaries—ignoring small talk.
Valuation intelligence extraction
We stored extracted data in a structured database, vectorized full transcripts (Weaviate) with text search (Elasticsearch), and auto-emailed results to analysts.
Structured knowledge base & delivery
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.
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.
