Executive Summary
A global event-technology enterprise with a 1,000-person sales force struggled to find time for deep pre-meeting research, leading to inconsistent pitches and missed opportunities. We partnered to design and deploy an Agentic AI–powered research pipeline that gathers data from public and internal sources, synthesizes insights, and auto-generates concise Client Research Reports delivered straight to each seller before meetings. The result: higher sales conversions, massive productivity gains, and a standardized, professional pitch quality at scale.
Tech Stack
Perplexity APIs
Python
GPT 4 Turbo
Playwright
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
Modernizing sales prep for a global event-tech enterprise—agentic AI briefs deliver just-in-time client research to lift conversions and standardize pitch quality.
Industry
SaaS
Region
North America
Technology
Perplexity
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
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
Large enterprise sales teams juggle packed calendars and fragmented intel, leading to under-researched meetings and uneven messaging. Manual prep doesn’t scale across 1,000 sellers, so opportunities slip and cycles lengthen. With agentic research that auto-synthesizes public/internal data into concise briefs, reps walk in prepared—improving win rates, productivity, and consistency at scale.
Lack of time & preparation
Packed calendars left sellers under-researched for client meetings.
Inconsistent pitch quality
Research depth varied widely across the team, fragmenting the message.
Missed opportunities & poor scalability
Without targeted insights, positioning was generic—and the manual process couldn’t scale to 1,000 sellers.
What Shorthills AI Did
We turned pre-meeting prep into a push-button flow. An agentic AI pipeline gathers the latest public info, filings, proprietary data, and CRM history, then distills executive priorities, news, and talking points into a short Client Research Report. Reports arrive automatically before each meeting—attached to the calendar and on mobile—so every seller walks in briefed, consistent, and ready to tailor the pitch.
We mapped current prep steps and defined “perfect preparation,” iterating with frontline sellers to ensure usefulness and adoption.
Co-designed the target workflow with sales
We automated multi-source ingestion (public web, financial filings, proprietary databases, and internal CRM history) and LLM-based synthesis to extract themes, executive priorities, and client-relevant challenges.
Built an Agentic AI research pipeline
We generated a concise Client Research Report using a co-designed template, attached it to the calendar invite, and pushed it to the seller’s mobile device ahead of the meeting.
Automated report generation & just-in-time delivery
We implemented agentic orchestration with OpenAI LLMs, robust scraping via Playwright, and deployed the solution on-prem for data privacy and control, wired to CRM and databases.
Integrated securely on enterprise infrastructure
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 1,000-person sales force was walking into meetings under-prepared, leading to uneven pitches and missed openings. With Shorthills AI’s agentic research pipeline, each seller now receives a concise, client-specific brief ahead of time—grounded in public and internal data. Prep time drops dramatically, so teams focus on strategy instead of searching, and pitch quality becomes consistent across regions. Conversations are sharper and more relevant, lifting conversion rates and shortening cycles. Net result: higher win rates, major productivity gains, and a standardized, professional pitch experience at scale.
Increased sales conversions
Through highly personalized, insight-led pitches.
Massive productivity gains
By automating pre-meeting research across 1,000 sellers.
Consistent, professional pitch quality
Stronger client perception organization-wide.
Frequently Asked Questions
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.
Executive Summary
A global event-technology enterprise with a 1,000-person sales force struggled to find time for deep pre-meeting research, leading to inconsistent pitches and missed opportunities. We partnered to design and deploy an Agentic AI–powered research pipeline that gathers data from public and internal sources, synthesizes insights, and auto-generates concise Client Research Reports delivered straight to each seller before meetings. The result: higher sales conversions, massive productivity gains, and a standardized, professional pitch quality at scale.
Tech Stack
Perplexity APIs
GPT 4 Turbo
Playwright
Python
