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 contact center support for a leading consumer brand—answering agent queries for ~30% faster resolution times and achieving ~40% higher CSAT scores.
Industry
Customer Experience
Region
North America
Technology
AWS
Executive Summary
A fast-growing consumer services brand needed to help support agents retrieve accurate answers faster from scattered data sources (internal databases and the public website). Shorthills built a GenAI Agent Assist platform that routes each agent query to the right backend: RAG for unstructured content, or NLP-to-SQL for structured account/billing data—coordinated by an intelligent router. The system presents legally-vetted, templated responses. Results: ~30% faster query resolution, ~40% CSAT lift, higher agent productivity, and more consistent, compliant responses.
Tech Stack
Llama 3
Agentic RAG
AWS
MySQL
Amazon BedRock
MongoDB
Docker
Python
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 fast-growing consumer services brand needed to help support agents retrieve accurate answers faster from scattered data sources (internal databases and the public website). Shorthills built a GenAI Agent Assist platform that routes each agent query to the right backend: RAG for unstructured content, or NLP-to-SQL for structured account/billing data—coordinated by an intelligent router. The system presents legally-vetted, templated responses. Results: ~30% faster query resolution, ~40% CSAT lift, higher agent productivity, and more consistent, compliant responses.
Tech Stack
Llama 3
Agentic RAG
Amazon BedRock
AWS
MySQL
MongoDB
Docker
Python
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.
Challenges
Agents had to manually search for information across multiple disconnected systems.
Information silos
Slow Query Resolution
The manual search process led to long handling times per query.
Inconsistent Customer Experience
Varying search methods caused inconsistent responses and lower CSAT.
Contact centres juggle scattered knowledge bases, public-site content, and siloed billing/account systems. Manual lookup and uneven access slow handle time, drive inconsistent answers, and raise compliance risk. With grounded retrieval and vetted templates, agents resolve faster, train quicker, and deliver more consistent CX at scale.
What Shorthills AI Did
We implemented an Agent Assist tool designed to empower agents by providing them with instant, accurate, and relevant information by leveraging a hybrid approach, integrating internal knowledge bases with cutting-edge GenAI capabilities. We connected all the places agents look—website content, internal docs, and account/billing systems—into one assist panel. The agent types the customer's query in natural language into the Agent Assist interface. An AI router decides where to fetch answers: RAG for policy/plan questions, or NLP-to-SQL for account-specific data. The tool then returns a ready-to-send reply using pre-approved templates, so agents respond quickly and stay compliant.
RAG for Retrieving Information
We built a Retrieval Augmented Generation (RAG) system on top of the client's content, allowing agents to ask natural language questions about plans, offerings, etc. and get instant, accurate answers.
Intelligent Query Routing
The core AI router analyzes each agent query in real-time and determines the most efficient path for retrieval—either the website for general queries or the database for specific account data.
Quick Response Generation
We built a platform that fetches the required information and then formulates responses using pre-approved, legally vetted templates to ensure compliance.
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 fast-growing consumer brand needed agents to find accurate answers without hopping across systems. With Shorthills AI’s Agent Assist, queries are routed to the right source automatically and replies come back in vetted templates, so handle time drops and quality rises. Resolution is ~30% faster, CSAT lifts ~40%, and new agents learn quicker because they search less and follow consistent guidance. Fewer escalations and rework lower operating strain, while compliant phrasing reduces risk. Net result: faster responses, higher satisfaction, and a steadier customer experience at scale.
~30% faster resolution
Agents find and relay answers significantly quicker.
~40% CSAT improvement
More accurate, consistent replies raise customer satisfaction.
Higher productivity & confidence
Less manual search; lower training time; standardized, compliant guidance.
Frequently Asked Questions
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