Executive Summary
The largest automotive manufacturer in India struggled to manage spare parts across machine assembly lines. Disparate, unstructured data, subjective replacement decisions, and lengthy manuals drove downtime, excess inventory, and higher costs. Shorthills built PartsGenie, an AI-powered tool that standardizes parts data, identifies viable alternatives with side-by-side specifications comparisons and savings, and answers technical questions from multi-hundred-page PDFs in seconds. The dashboard tracks transactions, approvals, savings, and KPIs like alternate-parts coverage and inventory reduction. Hosted on secured internal servers with role-based access, delivering measurable savings and a path to standardized, data-driven maintenance at scale.
Tech Stack
Python
React
Elasticsearch
AWS
Gemini 2.5 Pro
Power BI
Real-Time M&A Intelligence for 18,000+ Dealerships
Optimizing spare-parts management in the largest automotive manufacturer with PartsGenie—standardizing data and alternatives to cut overall inventory by 17.5%.
Industry
Automotive
Region
APAC
Technology
AWS
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
Executive Summary
The largest automotive manufacturer in India struggled to manage spare parts across machine assembly lines. Disparate, unstructured data, subjective replacement decisions, and lengthy manuals drove downtime, excess inventory, and higher costs. Shorthills built PartsGenie, an AI-powered tool that standardizes parts data, identifies viable alternatives with side-by-side specifications comparisons and savings, and answers technical questions from multi-hundred-page PDFs in seconds. The dashboard tracks transactions, approvals, savings, and KPIs like alternate-parts coverage and inventory reduction. Hosted on secured internal servers with role-based access, delivering measurable savings and a path to standardized, data-driven maintenance at scale.
Tech Stack
Python
React
AWS
Gemini 2.5 Pro
Elasticsearch
Power BI
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.
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.
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
Unstructured & Disparate Parts Data
Non-standard names/codes and scattered PDFs slowed accurate identification.
Subjective, Trial-and-Error Replacements
Expert “tacit knowledge” led to inconsistent choices and longer downtime.
Overstocking near-identicals; critical specs buried in lengthy manuals.
Inefficient Inventory & Documentation
Automotive manufacturers run complex assembly lines that depend on fast, accurate parts decisions. Disparate nomenclature, scattered PDFs, and tribal knowledge make identification and replacements slow and inconsistent—driving downtime, excess inventory, and higher costs. Without structured parts intelligence and verified alternatives, plants struggle to reduce stock and speed repairs at scale.
What Shorthills AI Did
We pulled scattered parts data and manuals into one clean catalog, standardizing names and codes so every item is easy to find and compare. When a part is out of stock or overpriced, PartsGenie suggests verified alternatives, shows side-by-side specifications and expected savings, and records approvals. An AI chat frontend answers questions from multi-hundred-page PDFs in seconds, while a dashboard tracks usage, savings, coverage, and inventory KPIs—securely, with role-based access.
Structured Parts Knowledge
Our AI platform Parts Genie ingests Enterprise Management System (EMS) data and manuals, standardizes nomenclature, and builds a searchable catalog with detailed specs and nomenclature breakdowns.
Alternative Suggestion & Comparison
The tool recommends compatible parts when stock-outs or cheaper options exist; shows specs comparison, cost-savings, and “replacement insights” with verification notes.
AI Chat & RCA Bot
Our custom built LLM chatbot answers natural-language queries from multi-hundred-page PDFs in 10–15 seconds. The Root-Cause Analysis(RCA) bot surfaces likely causes and prior fixes from history/manuals.
Manager Dashboard & Governance
The Power BI dashboard tracks transactions, approvals, savings, coverage, and inventory-reduction KPIs; secured roles for managers and subordinates; hosted on internal servers.
We pulled scattered parts data and manuals into one clean catalog, standardizing names and codes so every item is easy to find and compare. When a part is out of stock or overpriced, PartsGenie suggests verified alternatives, shows side-by-side specifications and expected savings, and records approvals. An AI chat frontend answers questions from multi-hundred-page PDFs in seconds, while a dashboard tracks usage, savings, coverage, and inventory KPIs—securely, with role-based access.
What Shorthills AI Did
Structured Parts Knowledge Base
Our AI platform Parts Genie ingests Enterprise Management System (EMS) data and manuals, standardizes nomenclature, and builds a searchable catalog with detailed specs and nomenclature breakdowns.
Alternative Suggestion & Comparison
The tool recommends compatible parts when stock-outs or cheaper options exist; shows specs comparison, cost-savings, and “replacement insights” with verification notes.
AI Chat & RCA Bot
Our custom built LLM chatbot answers natural-language queries from multi-hundred-page PDFs in 10–15 seconds. The Root-Cause Analysis(RCA) bot surfaces likely causes and prior fixes from history/manuals.
Manager Dashboard & Governance
The Power BI dashboard tracks transactions, approvals, savings, coverage, and inventory-reduction KPIs; secured roles for managers and subordinates; hosted on internal servers.
Outcomes
India’s largest automaker was losing time and money to inconsistent part naming, trial-and-error swaps, and buried documentation. With PartsGenie, teams now can analyze the root cause of a breakdown, locate parts and verified alternates quickly, cutting identification time from ~40 minutes to ~5 minutes. Standardized data and guided comparisons lifted alternate-part matches by ~1.4×, delivering ~67.9% alternate coverage and ~9.3% projected savings. Inventory levels dropped as near-identical SKUs consolidated, supporting an overall ~17.5% reduction. Because every decision is traceable—with specs, approvals, and savings—plants reduce downtime and buy smarter, while managers track impact in real time. In short, spare-parts decisions moved from manual and fragmented to fast, standardized, and savings-driven at scale.
Inventory Reduction
Lowered inventory levels through 1.4× higher matches for alternate parts.
Savings & Coverage
Achieved 9.3% projected savings with 67.9% alternate part coverage.
Faster Diagnosis & Turnaround
Reduced alternate part identification time from 40 mins to 5 mins.
Outcomes
India’s largest automaker was losing time and money to inconsistent part naming, trial-and-error swaps, and buried documentation. With PartsGenie, teams now can analyze the root cause of a breakdown, locate parts and verified alternates quickly, cutting identification time from ~40 minutes to ~5 minutes. Standardized data and guided comparisons lifted alternate-part matches by ~1.4×, delivering ~67.9% alternate coverage and ~9.3% projected savings. Inventory levels dropped as near-identical SKUs consolidated, supporting an overall ~17.5% reduction. Because every decision is traceable—with specs, approvals, and savings—plants reduce downtime and buy smarter, while managers track impact in real time. In short, spare-parts decisions moved from manual and fragmented to fast, standardized, and savings-driven at scale.
Inventory Reduction
Lowered inventory levels through 1.4× higher matches for alternate parts.
Savings & Coverage
Achieved 9.3% projected savings with 67.9% alternate part coverage.
Faster Diagnosis & Turnaround
Reduced alternate part identification time from 40 mins to 5 mins.
Frequently Asked Questions
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.
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