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 broker onboarding for a fintech AI platform—reusable integration framework turns months-long builds into weeks/days for faster revenue.
Industry
Financial Services
Region
APAC
Technology
Python
What Shorthills AI Did
We turned one-off broker builds into a reusable integration framework. Core APIs are wrapped once, standard connectors handle common patterns, and thin broker-specific adapters plug in as needed—so most work is reused. A clear checklist, automated tests, and compliance guardrails keep each integration consistent. Result: months-long builds shrink to weeks—or days for standard platforms—without growing the team.
We built intelligent API wrappers for the provider’s endpoints, with clear I/O contracts and standardized connectors that talk to varied broker platforms—minimizing net-new code per onboarding.
Engineered a reusable integration framework
We designed a plug-in adapter architecture so broker-specific logic can be added without touching the core; comprehensive unit tests safeguard data transforms and reliability.
Made it modular & extensible
We used AI-assisted coding, iterative sprints (1–2 weeks/monthly for larger integrations), and close co-working sessions with the client team to shorten build cycles.
Accelerated delivery with AI-assisted dev & agile
We aligned framework behavior to the provider’s security/data-handling protocols and integration checklists to support their compliance posture across brokers.
Embedded compliance-aware practices
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 fintech provider offering AI investment tools needed to accelerate broker onboarding across a fragmented ecosystem of legacy and custom trading platforms. One-off, bespoke integrations strained a small internal team, slowed time-to-market, and raised costs—while compliance needs added complexity. We built an AI-driven integration framework (Python/Django) that standardizes connectors, wraps core APIs, and plugs in broker-specific adapters with robust testing—turning months-long builds into weeks or even days for standard platforms. Result: higher onboarding throughput, lower integration effort, and faster revenue realization.
Tech Stack
Python
Django
Microsoft Teams
My SQL
Executive Summary
A fintech provider offering AI investment tools needed to accelerate broker onboarding across a fragmented ecosystem of legacy and custom trading platforms. One-off, bespoke integrations strained a small internal team, slowed time-to-market, and raised costs—while compliance needs added complexity. We built an AI-driven integration framework (Python/Django) that standardizes connectors, wraps core APIs, and plugs in broker-specific adapters with robust testing—turning months-long builds into weeks or even days for standard platforms. Result: higher onboarding throughput, lower integration effort, and faster revenue realization.
Tech Stack
Python
Django
Microsoft Teams
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.
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.
Frequently Asked Questions
Challenges
Capital-markets integrations are messy: every broker has different, often under-documented platforms, so one-off builds strain small teams and slow launches. Manual discovery, bespoke code, and heavy compliance checks inflate cost and risk. With a reusable framework and standardized connectors, providers accelerate onboarding, improve reliability, and scale without proportional headcount.
Diverse & poorly documented broker platforms
Every onboarding required custom discovery and code; many lacked standard APIs or docs.
Limited internal capacity
A small integrations team became a bottleneck as demand grew.
Compliance & reliability needs
Financial-grade integrations demanded consistency, testing, and adherence to security protocols.
Outcomes
A fintech provider was stuck onboarding brokers one at a time, with bespoke code, scarce engineers, and heavy compliance checks slowing launches. With Shorthills AI’s reusable framework, standard platforms now go live in weeks or even days instead of months, accelerating time-to-market and revenue recognition. Throughput rises because teams can run multiple onboardings in parallel using shared connectors and a repeatable checklist. Reliability improves via unit tests across wrappers/adapters, reducing defects and rework, while embedded security and data-handling practices strengthen compliance. Engineering effort and cost drop as more of the stack is reused per broker, and the framework keeps improving as new adapters are added. In short: faster integrations, higher scale, and dependable, compliant launches.
Onboarding time slashed
from months to weeks/days on standard platforms; faster time-to-market and revenue.
Higher throughput & scalability
onboard more brokers in parallel without proportional headcount growth.
Lower cost & better reliability
via reusable components, standardized connectors, and
robust testing.
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