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
Real-Time M&A Intelligence for 18,000+ Dealerships
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Modernizing legal & tax knowledge discovery with AI at a leading professional services firm- 60% faster search results and 30% higher associate efficiency.
Industry
Professional Services
Region
APAC
Technology
Azure
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 global professional services firm, was facing significant challenges with its vast internal library of legal and tax documents. Their employees struggled with slow, inaccurate keyword searches and spent too much time manually sifting through complex information, which hindered their productivity. Shorthills AI developed an intelligent, AI-powered search solution. This platform provided our client powerful search capabilities to quickly pinpoint exact information, tools to summarize lengthy documents, and an intuitive Q&A system to get direct answers from their data. As a result, the client achieved a 40% reduction in search time and a 30% improvement in associate efficiency, allowing their highly skilled teams to focus on what matters most — their clients.
Tech Stack
GTP 4o
Langchain
RAG
Weaviate
Reranker (Cross-Encoder)
Azure
Django
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
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
Professional services—especially large tax and legal practices—run on vast, ever-changing libraries of rulings, memos, and client files. Mixed formats, inconsistent metadata, and regulatory churn make fact-finding slow and error-prone, driving up review costs and delaying filings. Without smarter discovery, teams struggle to meet deadlines and maintain margins at scale.
Searchability at Scale
Keyword search was slow, imprecise, and noisy due to massive corpus of documents.
Document Complexity & Change
Reliability of results was limited due to scanned documents, complex tables, excessive jargon, and constantly evolving regulations.
High Cost of Error
The team needed reliable, citation-based insights as misinterpretations could have serious consequences.
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.
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 legal & tax knowledge discovery at a leading professional services firm with AI-powered search, summarization & Q&A—60% faster results and ~30% higher associate efficiency.
Industry
Professional Services
Region
APAC
Technology
Azure
Challenges
Keyword search was slow, imprecise, and noisy due to massive corpus of documents.
Searchability at Scale
Document Complexity & Change
Reliability of results was limited due to scanned documents, complex tables, excessive jargon, and constantly evolving regulations.
Document Complexity & Change
Reliability of results was limited due to scanned documents, complex tables, excessive jargon, and constantly evolving regulations.
Professional services—especially large tax and legal practices—run on vast, ever-changing libraries of rulings, memos, and client files. Mixed formats, inconsistent metadata, and regulatory churn make fact-finding slow and error-prone, driving up review costs and delaying filings. Without smarter discovery, teams struggle to meet deadlines and maintain margins at scale.
Executive Summary
A leading global professional services firm, was facing significant challenges with its vast internal library of legal and tax documents. Their employees struggled with slow, inaccurate keyword searches and spent too much time manually sifting through complex information, which hindered their productivity. Shorthills AI developed an intelligent, AI-powered search solution. This platform provided our client powerful search capabilities to quickly pinpoint exact information, tools to summarize lengthy documents, and an intuitive Q&A system to get direct answers from their data. As a result, the client achieved a 40% reduction in search time and a 30% improvement in associate efficiency, allowing their highly skilled teams to focus on what matters most — their clients.
Tech Stack
GPT 4o
Langchain
RAG
Weaviate
Reranker (Cross-Encoder)
Azure
Django
What Shorthills Did
We unified the firm’s sprawling legal and tax library into a governed, search-ready index. Ingestion pipelines cleaned scans with OCR, added key metadata (jurisdiction, case number, entities), and stored embeddings with lineage so content stayed current. On top, we built hybrid search—semantic + keyword + filters—with a cross-encoder re-ranker to surface the right passages fast. Long documents are auto-summarized into clear briefs, and a RAG Q&A answers in plain English with citations, respecting permissions. Result: teams can find, skim, and trust the right material in minutes, not hours.
Data Foundation: Continuous Ingestion & Enrichment
We built and deployed automated pipelines to ingest internal, third-party, and client files; normalized scanned documents with OCR; extracted jurisdiction, case-number, and entity metadata; and stored embeddings with end-to-end lineage in a governed vector store to keep content retrieval-ready.
Search & Retrieval: Hybrid with Re-Ranking
We engineered semantic search to blend with keyword and metadata filters. This ensured query parsing and re-ranking to surface the most relevant passages, improving precision/recall without sacrificing speed or compliance requirements.
Summarization & Review: Fast, Consistent Briefs
Our system condenses lengthy documents or sections into concise, structured summaries on demand, that highlight arguments, facts, and precedents—standardizing first-pass review across teams.
Our RAG retrieves evidence and uses GPT-4 (Azure OpenAI) to draft direct, citation-backed answers whose responses preserve context, include sources, and respect access controls and governance policies.
Conversational Q&A with Guardrails
What Shorthills AI Did
We unified the firm’s sprawling legal and tax library into a governed, search-ready index. Ingestion pipelines cleaned scans with OCR, added key metadata (jurisdiction, case number, entities), and stored embeddings with lineage so content stayed current. On top, we built hybrid search—semantic + keyword + filters—with a cross-encoder re-ranker to surface the right passages fast. Long documents are auto-summarized into clear briefs, and a RAG Q&A answers in plain English with citations, respecting permissions. Result: teams can find, skim, and trust the right material in minutes, not hours.
Data Foundation: Continuous Ingestion & Enrichment
We built and deployed automated pipelines to ingest internal, third-party, and client files; normalized scanned documents with OCR; extracted jurisdiction, case-number, and entity metadata; and stored embeddings with end-to-end lineage in a governed vector store to keep content retrieval-ready.
Search & Retrieval: Hybrid with Re-Ranking
We engineered semantic search to blend with keyword and metadata filters. This ensured query parsing and re-ranking to surface the most relevant passages, improving precision/recall without sacrificing speed or compliance requirements.
Summarization & Review: Fast, Consistent Briefs
Our system condenses lengthy documents or sections into concise, structured summaries on demand, that highlight arguments, facts, and precedents—standardizing first-pass review across teams.
Conversational Q&A with Guardrails
Our RAG retrieves evidence and uses GPT-4 (Azure OpenAI) to draft direct, citation-backed answers whose responses preserve context, include sources, and respect access controls and governance policies.
60% faster search
Precise, relevant results in fewer queries.
~30% higher associate efficiency
Less time spent on first-pass review and extraction.
Lower risk coupled with higher confidence
Citation-backed answers and consistent summaries.
Outcomes
A global professional services firm was losing time to keyword searches and manual review across a massive, mixed-format corpus of legal and tax content. With Shorthills AI’s AI-powered discovery, associates now get precise passages in fewer queries and jump straight to citation-backed answers. Search cycles are 60% faster, and standardized summaries cut first-pass review time, lifting associate efficiency by ~30%. Because every answer links to sources and honors access controls, risk drops while confidence rises. Teams file sooner, spend less time on repetitive retrieval, and focus more on client work and analysis. In short, the firm moved from slow, scattershot searching to fast, trustworthy knowledge—at scale.
Outcomes
A global professional services firm was losing time to keyword searches and manual review across a massive, mixed-format corpus of legal and tax content. With Shorthills AI’s AI-powered discovery, associates now get precise passages in fewer queries and jump straight to citation-backed answers. Search cycles are 60% faster, and standardized summaries cut first-pass review time, lifting associate efficiency by ~30%. Because every answer links to sources and honors access controls, risk drops while confidence rises. Teams file sooner, spend less time on repetitive retrieval, and focus more on client work and analysis. In short, the firm moved from slow, scattershot searching to fast, trustworthy knowledge—at scale.
60% faster search
Precise, relevant results in fewer queries.
~30% higher associate efficiency
Less time spent on first-pass review and extraction.
Lower risk coupled with higher confidence
Citation-backed answers and consistent summaries.
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