Executive Summary
A healthcare nonprofit needed to scale creation of family-friendly guides for rare genetic conditions. A fully manual process—reading 50–200 medical sources and drafting a 10-page guide—took 2–3 months per topic, limiting output and burdening PhD researchers. We built a purpose-built AI engine that ingests a client-vetted medical corpus, applies a custom Llama-3-70B prompt framework to extract and write section-wise content with sensitive, hedged language and citations, and routes drafts through a human-in-the-loop validation flow. First drafts now generate in 10–15 minutes, enabling scale with accuracy and consistent tone.
Tech Stack
Google Analytics
Google BigQuery
Elastic search
AWS
Python
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
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.
Challenges
Healthcare nonprofits producing family-friendly medical guides face long, manual research cycles across 50–200 sources, risking inconsistent tone and limited output. Expert time gets consumed by drafting instead of review, slowing impact and reach. With corpus-grounded automation and human-in-the-loop validation, teams scale trustworthy, consistent guides in minutes—not months.
Manual, time-intensive research & synthesis
(50–200 sources; 2–3 months per guide).
Limited scalability
(only 6–7 guides/year vs. thousands of conditions).
Tone, sensitivity, and consistency risks
Across long cycles and multiple authors.
What Shorthills AI Did
We turned a months-long writing process into a guided, evidence-based flow. The engine reads only a trusted medical corpus, pulls the right facts for each section, and drafts a family-friendly guide with clear hedging and citations. Experts stay in the loop to review and refine tone before publish. First drafts are ready in 10–15 minutes, so researchers spend time validating—not starting from scratch.
We shadowed subject-matter experts, manually produced two guides end-to-end, and translated their method, tone, and template rules into an AI blueprint.
Immersed to codify the gold-standard process
We ingested only client-approved medical literature and normalized it via parsing/chunking so generation is grounded in vetted evidence.
Built a trusted-corpus ingestion layer
We used Llama-3-70B with multi-layer prompts to extract facts per section, apply family-friendly hedging, and auto-generate citations/references for traceability.
Engineered a domain-specific LLM workflow
We routed each draft to external clinical reviewers, then to in-house experts for tonal refinements and publication formatting.
Implemented HITL quality gates
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 patient-education content for a healthcare nonprofit—AI engine generates citation-backed guides in 10–15 minutes (~99% faster) to scale beyond 6–7/year.
Industry
Healthcare
Region
EMEA
Technology
Gemini
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.
Real-Time M&A Intelligence for 18,000+ Dealerships
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 healthcare nonprofit was producing just a handful of rare-condition guides each year because every draft meant 50–200 sources and 2–3 months of manual work. With Shorthills AI’s purpose-built engine, a citation-backed first draft arrives in 10–15 minutes—about a ~99% speedup—grounded only in client-approved literature. Human reviewers focus on clinical accuracy and tone, not blank-page writing, so throughput scales from 6–7 per year to potentially hundreds. Consistent templates and hedged, family-friendly language improve readability and trust, while structured references make fact-checking straightforward. Net result: faster, reliable guide production at scale—freeing PhD experts to concentrate on review, outreach, and higher-value patient support.
~99% faster first drafts
From 2–3 months to 10–15 minutes.
Massive scalability
From 6–7/year to potentially hundreds of guides.
Consistency & efficiency
Experts focus on review and outreach; tone and structure standardized.
Frequently Asked Questions
Executive Summary
A healthcare nonprofit needed to scale creation of family-friendly guides for rare genetic conditions. A fully manual process—reading 50–200 medical sources and drafting a 10-page guide—took 2–3 months per topic, limiting output and burdening PhD researchers. We built a purpose-built AI engine that ingests a client-vetted medical corpus, applies a custom Llama-3-70B prompt framework to extract and write section-wise content with sensitive, hedged language and citations, and routes drafts through a human-in-the-loop validation flow. First drafts now generate in 10–15 minutes, enabling scale with accuracy and consistent tone.
Tech Stack
Llama 3.3
Gemini 2.5 Pro
RAG
BGE Reranker
MiniLM
Python
Weaviate
Docling
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