Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
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.
Accelerating deep legal–tax research at a leading professional services firm with agentic AI—for ~80% faster turnaround, 5× productivity, and near-perfect automation.
Industry
Professional Services
Region
APAC
Technology
Open AI
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 global professional services firm needed faster, more reliable research and drafting for legal and tax work. Manual searches, fragmented sources, and compliance demands slowed delivery and increased risk. Shorthills built a multi-agent system that refines queries, retrieves data from internal and web sources, validates and consolidates content, and auto-drafts domain-specific reports. The solution achieved up to 99.99% automation, an ~80% reduction in turnaround time, and 5× productivity gains, while enhancing accuracy, coverage, and user experience. Designed for sensitive data and specialized language, it scales research and drafting without adding headcount.
Tech Stack
Open AI (o3 model)
Google ADK
Firecrawl
Django
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—handle research and drafting across fragmented, fast-changing sources with high compliance stakes. Manual workflows slow delivery, consume senior time, and create gaps in coverage.
Time-Intensive Research
Manual searches across laws, case records, internal systems, and the web were slow and repetitive.
Fragmented & Evolving Information
Data was available in multiple systems and public sources; regulations and precedents change frequently.
High-Stakes, Domain-Specific Drafting
Outputs require precise legal/tax language, correct citations, and strong confidentiality.
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.
Our Solutions
Agentic Workflow: From Query to Final Draft
A coordinated set of agents handles query enrichment, parallel research (web, internal, user uploads), validation/consolidation, and report generation—streaming a polished, compliant draft at the end.
Parallelized Information Gathering Across Sources
Dedicated agents search the public web, internal repositories, and user-provided files simultaneously, broadening coverage and reducing blind spots before consolidation.
Content Validation & Consolidation with User Control
A validation agent filters for relevance/quality and presents a curated source list for user confirmation, enabling hyper-personalization and tighter control of evidence.
Domain-Tailored Drafting & Compliance
The drafting agent applies domain-specific language and structure for legal/tax contexts, producing accurate, compliant reports and improving overall user experience at scale.
~80% faster turnaround
Complex reports delivered much sooner than manual processes.
~5× productivity
Substantial throughput gains across research and drafting tasks.
Up to 99.99% automation
End-to-end generation with minimal manual effort where applicable.
Outcomes
A global professional services firm was slowed by manual, fragmented research across laws, rulings, and internal files. With Shorthills AI’s agentic co-pilot, teams now move from query to a citation-backed draft in one flow: queries are refined automatically, sources are gathered and vetted in parallel, and the final write-up uses domain-appropriate legal/tax language. Turnaround times drop by ~80%, and productivity rises ~5× as associates focus on judgment calls instead of hunting and stitching content. For routine, well-scoped tasks, automation reaches up to 99.99%, clearing backlogs during peak periods. Because each claim links to approved sources and respects access rules, accuracy improves, review effort falls, and client-ready drafts land faster.
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 tax-notice response at a leading professional services firm—LLM co-pilot cuts first drafts from ~3 days to 10–15 minutes at ~90% initial accuracy.
Industry
Professional Services
Region
APAC
Technology
Open AI
Challenges
Manual searches across laws, case records, internal systems, and the web were slow and repetitive.
Time-Intensive Research
Fragmented & Evolving Information
Data was available in multiple systems and public sources; regulations and precedents change frequently.
High-Stakes, Domain-Specific Drafting
Outputs require precise legal/tax language, correct citations, and strong confidentiality.
Professional services—especially large tax and legal practices—handle research and drafting across fragmented, fast-changing sources with high compliance stakes. Manual workflows slow delivery, consume senior time, and create gaps in coverage.
Real-Time M&A Intelligence for 18,000+ Dealerships
What Shorthills Did
We turned deep research and drafting into a guided, end-to-end flow through Agentic AI. Agents expand your query, pull evidence from the web and internal sources in parallel, filter and de-duplicate what matters, then draft a report in clear legal/tax language with citations. You stay in control—approve sources, tweak tone, and add notes—while the system learns from feedback and follows firm policies on access and confidentiality.
Notice Ingestion & Issue Extraction
We enable a coordinated set of agents to handle query enrichment, parallel research (web, internal, user uploads), validation/consolidation, and report generation—streaming a polished, compliant draft at the end.
Parallelized Information Gathering Across Sources
Data Foundation: Continuous Ingestion & Enrichment
Content Validation & Consolidation with User Control
Our AI validation agent filters for relevance/quality and presents a curated source list for user confirmation, enabling hyper-personalization and tighter control of evidence.
Domain-Tailored Drafting & Compliance
The drafting agent applies domain-specific language and structure for legal/tax contexts, producing accurate, compliant reports and improving overall user experience at scale.
What Shorthills AI Did
We turned deep research and drafting into a guided, end-to-end flow through Agentic AI. Agents expand your query, pull evidence from the web and internal sources in parallel, filter and de-duplicate what matters, then draft a report in clear legal/tax language with citations. You stay in control—approve sources, tweak tone, and add notes—while the system learns from feedback and follows firm policies on access and confidentiality.
Agentic Workflow: From Query to Final Draft
We enable a coordinated set of agents to handle query enrichment, parallel research (web, internal, user uploads), validation/consolidation, and report generation—streaming a polished, compliant draft at the end.
Parallelized Information Gathering Across Sources
Our dedicated AI agents search the public web, internal repositories, and user-provided files simultaneously, broadening coverage and reducing blind spots before consolidation.
Content Validation & Consolidation with User Control
Our AI validation agent filters for relevance/quality and presents a curated source list for user confirmation, enabling hyper-personalization and tighter control of evidence.
Domain-Tailored Drafting & Compliance
The drafting agent applies domain-specific language and structure for legal/tax contexts, producing accurate, compliant reports and improving overall user experience at scale.
Executive Summary
A leading global professional services firm needed faster, more reliable research and drafting for legal and tax work. Manual searches, fragmented sources, and compliance demands slowed delivery and increased risk. Shorthills built a multi-agent system that refines queries, retrieves data from internal and web sources, validates and consolidates content, and auto-drafts domain-specific reports. The solution achieved up to 99.99% automation, an ~80% reduction in turnaround time, and 5× productivity gains, while enhancing accuracy, coverage, and user experience. Designed for sensitive data and specialized language, it scales research and drafting without adding headcount.
Tech Stack
Open AI (o3 model)
Google ADK
Firecrawl
Django
Outcomes
A global professional services firm was slowed by manual, fragmented research across laws, rulings, and internal files. With Shorthills AI’s agentic co-pilot, teams now move from query to a citation-backed draft in one flow: queries are refined automatically, sources are gathered and vetted in parallel, and the final write-up uses domain-appropriate legal/tax language. Turnaround times drop by ~80%, and productivity rises ~5× as associates focus on judgment calls instead of hunting and stitching content. For routine, well-scoped tasks, automation reaches up to 99.99%, clearing backlogs during peak periods. Because each claim links to approved sources and respects access rules, accuracy improves, review effort falls, and client-ready drafts land faster.
~80% faster turnaround
Complex reports delivered much sooner than manual processes.
~5× productivity
Substantial throughput gains across research and drafting tasks.
Up to 99.99% automation
End-to-end generation with minimal manual effort where applicable.
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
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