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 purchase decisions through product research with AI—analyzing 18.6M+ reviews across 1,500+ categories to deliver feature-specific product insights.
Industry
E-commerce
Region
North America
Technology
AWS
Elevating purchase decisions through product research with AI—analyzing 18.6M+ reviews across 1,500+ categories to deliver granular, feature-specific product insights.
Industry
E-commerce
Region
North America
Technology
AWS
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 product rating platform was built to simplify product research by converting millions of scattered, subjective reviews into transparent, feature-specific insights. The in-house solution—Best Views Reviews (BVR)—uses AI/NLP to ingest and analyze massive sets of reviews, extract helpful snippets, compute feature-level and overall scores, and present results through an intuitive web experience with fast search and comparisons. Revenue comes via affiliate links, while the system employs large-scale data extraction, sentiment analysis, and model training. With 18.6M+ reviews analyzed across 1,500+ categories, BVR demonstrates scalable AI, iterative model refinement, and a roadmap for new verticals (e.g., hotels), all designed for trust, transparency, and growth.
Tech Stack
Python
Langchain
React
Elasticsearch
Custom LLM (Llama 3)
AWS
Django
Apache Airflow DAGS
SpaCy
Flan-t5
What Shorthills Did
We pull reviews from major sites into one place and let AI read them like a smart analyst. It groups comments by product features (battery, build, comfort, etc.), rates each feature, and rolls them up into a fair overall score that balances recency and volume. Short, clear snippets show why a product scored the way it did. The website then lets shoppers search, compare, and browse by features with transparent counts—so decisions are quick and confident.
AI Review Scoring
Our agentic AI sentiment analysis classifies opinions; feature-specific scoring aggregates what users say about aspects like battery or build and overall scores balance recency and volume to avoid bias. Helpful snippets are rewritten for clarity.
Data Extraction & Processing
Our large-scale scraping pipelines collect reviews from major web sources; in-house, fine-tuned models (incl. Llama) and then process text at scale, supported by Elastic Search for fast retrieval.
User Experience for Fast Decisions
Features such as Category pages, top recommendations, feature-based browsing, comparisons, and transparent counts (e.g., “51,975 reviews…”) reduce research time and cognitive load.
Operate & Grow: D2C + Affiliate
The platform drives users to retailers via affiliate links, aligning D2C growth with revenue while enabling continuous AI/model and UX improvements.
Executive Summary
A product rating platform was built to simplify product research by converting millions of scattered, subjective reviews into transparent, feature-specific insights. The in-house solution—Best Views Reviews (BVR)—uses AI/NLP to ingest and analyze massive sets of reviews, extract helpful snippets, compute feature-level and overall scores, and present results through an intuitive web experience with fast search and comparisons. Revenue comes via affiliate links, while the system employs large-scale data extraction, sentiment analysis, and model training. With 18.6M+ reviews analyzed across 1,500+ categories, BVR demonstrates scalable AI, iterative model refinement, and a roadmap for new verticals (e.g., hotels), all designed for trust, transparency, and growth.
Tech Stack
Python
Custom LLM (Llama 3)
Apache Airflow DAGS
React
Elasticsearch
AWS
Bart-Large-MNLI
SpaCy
Flan-t5
Challenges
E-commerce platforms flood shoppers with scattered, subjective reviews across multiple sites. Inconsistent formats, bias, and surplus of content make comparisons slow and unreliable, driving up research time and eroding trust. Without structured, feature-level signals, buyers hesitate, conversions dip, and return/support costs rise. At scale, major rating platforms and marketplaces also struggle with manipulated or incentive-driven reviews, which makes aggregate ratings less trustworthy.
Information Overload & Bias
Consumers face time-consuming, subjective, and unstructured reviews across multiple sites.
Scale, Quality & Anti-Scraping
Handling large data volumes, changing formats, and scraping restrictions makes reliable data capture challenging.
Trust, Transparency & Security
Scores must reflect genuine sentiment, show review counts, and protect platform integrity.
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.
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.
Challenges
Consumers face time-consuming, subjective, and unstructured reviews across multiple sites.
Information Overload & Bias
Scale, Quality & Anti-Scraping
Handling large data volumes, changing formats, and scraping restrictions makes reliable data capture challenging.
Trust, Transparency & Security
Scores must reflect genuine sentiment, show review counts, and protect platform integrity.
E-commerce platforms flood shoppers with scattered, subjective reviews across multiple sites. Inconsistent formats, bias, and surplus of content make comparisons slow and unreliable, driving up research time and eroding trust. Without structured, feature-level signals, buyers hesitate, conversions dip, and return/support costs rise. At scale, major rating platforms and marketplaces also struggle with manipulated or incentive-driven reviews, which makes aggregate ratings less trustworthy.
What Shorthills AI Did
We pull reviews from major sites into one place and let AI read them like a smart analyst. It groups comments by product features (battery, build, comfort, etc.), rates each feature, and rolls them up into a fair overall score that balances recency and volume. Short, clear snippets show why a product scored the way it did. The website then lets shoppers search, compare, and browse by features with transparent counts—so decisions are quick and confident.
AI Review Scoring
Our agentic AI sentiment analysis classifies opinions; feature-specific scoring aggregates what users say about aspects like battery or build and overall scores balance recency and volume to avoid bias. Helpful snippets are rewritten for clarity.
Data Extraction & Processing
Our large-scale scraping pipelines collect reviews from major web sources; in-house, fine-tuned models (incl. Llama) and then process text at scale, supported by Elastic Search for fast retrieval.
User Experience for Fast Decisions
Features such as Category pages, top recommendations, feature-based browsing, comparisons, and transparent counts (e.g., “51,975 reviews…”) reduce research time and cognitive load.
Operate & Grow: D2C + Affiliate
The platform drives users to retailers via affiliate links, aligning D2C growth with revenue while enabling continuous AI/model and UX improvements.
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
Outcomes
An e-commerce ratings platform needed to turn scattered, subjective reviews into clear product guidance. With Shorthills AI’s BVR, 18.6M+ reviews across 1,500+ categories are analyzed into feature-level insights and a single, trustworthy score. Ingestion cycles that once took a week now complete in 1–2 days, keeping rankings fresh. Shoppers see exactly how many reviews power each score and can compare by the features they care about, cutting research time and second-guessing. Clear, evidence-backed snippets improve trust and reduce returns, while affiliate links convert informed choices into revenue. Net result: faster research, transparent scoring, and higher confidence at scale.
Scale & Coverage
Analyzed 18.6M+ reviews across 1,500+ categories; optimized ingestion from a week to 1–2 days.
Revenue & Reach
Affiliate model monetization; targeting 7–10 lakh monthly active users (MAU).
Trust & Usability
Transparent metrics, feature-level insight, and concise snippets speed confident purchase decisions.
Outcomes
An e-commerce ratings platform needed to turn scattered, subjective reviews into clear product guidance. With Shorthills AI’s BVR, 18.6M+ reviews across 1,500+ categories are analyzed into feature-level insights and a single, trustworthy score. Ingestion cycles that once took a week now complete in 1–2 days, keeping rankings fresh. Shoppers see exactly how many reviews power each score and can compare by the features they care about, cutting research time and second-guessing. Clear, evidence-backed snippets improve trust and reduce returns, while affiliate links convert informed choices into revenue. Net result: faster research, transparent scoring, and higher confidence at scale.
Scale & Coverage
Analyzed 18.6M+ reviews across 1,500+ categories; optimized ingestion from a week to 1–2 days.
Revenue & Reach
Affiliate model monetization; targeting 7–10 lakh monthly active users (MAU).
Trust & Usability
Transparent metrics, feature-level insight, and concise snippets speed confident purchase decisions.
Also Read
