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 global tax operations at a leading professional services firm with an AI-driven Transaction Analyzer—classifying millions of transactions to cut unit costs and speed filings.
Industry
Professional Services
Region
APAC
Technology
Azure
Transforming global tax operations with an AI-driven analyzer at a leading professional services firm—classifying transactions to cut costs and expedite tax filings.
Industry
Professional Services
Region
APAC
Technology
Azure
Executive Summary
A leading global professional services firm needed to classify millions of transactions for indirect and direct tax obligations. Manual review couldn’t keep pace with volume, ambiguous descriptions, and changing regulations—leading to delays, cost, and risk. Shorthills built a GenAI-powered Transaction Analyser that ingests ledger and invoice data, applies OCR(Optical Character Recognition) and enrichment, and uses LLMs with rule libraries to classify expenses, assign tax slabs, assess input-credit eligibility, and determine withholding requirements. A governed workflow lets experts review exceptions and continuously improve existing models. The result: high-volume processing at low unit cost, improved accuracy through human-in-the-loop, and faster, audit-ready outputs that enables specialists to focus on higher-value advisory work.
Tech Stack
GPT-4o mini
SQL
RAG
Celery
Redis
Langchain
Azure
Django
Python
Executive Summary
A leading global professional services firm needed to classify millions of transactions for indirect and direct tax obligations. Manual review couldn’t keep pace with volume, ambiguous descriptions, and changing regulations—leading to delays, cost, and risk. Shorthills built a GenAI-powered Transaction Analyser that ingests ledger and invoice data, applies OCR(Optical Character Recognition) and enrichment, and uses LLMs with rule libraries to classify expenses, assign tax slabs, assess input-credit eligibility, and determine withholding requirements. A governed workflow lets experts review exceptions and continuously improve existing models. The result: high-volume processing at low unit cost, improved accuracy through human-in-the-loop, and faster, audit-ready outputs that enables specialists to focus on higher-value advisory work.
Tech Stack
GPT-4o mini
RAG
Celery
Redis
Langchain
Azure
Django
SQL
Python
Executive Summary
A leading global professional services firm needed to classify millions of transactions for indirect and direct tax obligations. Manual review couldn’t keep pace with volume, ambiguous descriptions, and changing regulations—leading to delays, cost, and risk. Shorthills built a GenAI-powered Transaction Analyser that ingests ledger and invoice data, applies OCR(Optical Character Recognition) and enrichment, and uses LLMs with rule libraries to classify expenses, assign tax slabs, assess input-credit eligibility, and determine withholding requirements. A governed workflow lets experts review exceptions and continuously improve existing models. The result: high-volume processing at low unit cost, improved accuracy through human-in-the-loop, and faster, audit-ready outputs that enables specialists to focus on higher-value advisory work.
Tech Stack
GPT-4o mini
Celery
RAG
Redis
Reranker (Cross-Encoder)
Langchain
Django
SQL
Python
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.
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
Volume & Ambiguity
Massive data with short, inconsistent, often cryptic descriptions.
Evolving Rules
Deriving actionable insights was slow and unreliable, hindering timely strategic decisions and accurate forecasting.
Cost of Error
Penalties, rework, and delayed filings due to misclassification.
Professional services—especially large tax and legal practices—process millions of invoices and ledger lines across vendors and jurisdictions. Cryptic descriptions, uneven metadata, and shifting indirect/direct tax rules make transaction classification slow and mistake-prone—driving rework, penalty risk, and delays to close and filings. Without smarter automation, unit costs rise and peak-cycle throughput stalls.
Challenges
Professional services—especially large tax and legal practices—process millions of invoices and ledger lines across vendors and jurisdictions. Cryptic descriptions, uneven metadata, and shifting indirect/direct tax rules make transaction classification slow and mistake-prone—driving rework, penalty risk, and delays to close and filings. Without smarter automation, unit costs rise and peak-cycle throughput stalls.
Volume & Ambiguity
Massive data with short, inconsistent, often cryptic descriptions.
Evolving
Rules
Frequent regulatory updates required current, context-aware interpretation.
Cost of
Error
Penalties, rework, and delayed filings due to misclassification.
Our Solutions
Data Foundation: Ingestion, OCR & Enrichment
Automated pipelines ingest invoices and ledger entries, normalize scans with OCR, and extract metadata (entities, jurisdictions, document types). Cleaned records are embedded and stored for fast, governed retrieval—complete with lineage and versioning.
Smart Classification & Tax Logic
LLMs categorize transactions, apply the right tax slabs, check input credit eligibility, and flag withholding needs — using semantic understanding and an up-to-date rule base.
Human-in-the-Loop Review & Learning
Analysts review flagged exceptions, validate or correct model outputs, and feed insights back to improve accuracy—ensuring transparency and auditability.
Scalability, Governance & Cost Control
Parallelized jobs process millions of rows rapidly; RBAC, encryption, and logging enforce compliance. Optimized I/O and autoscaling keep per-transaction costs low while maintaining throughput during peak cycles.
What Shorthills AI Did
We pulled millions of raw transactions into one pipeline and used GenAI to read short, messy descriptions, then applied up-to-date tax rules to classify each line: expense category, GST slab, ITC eligibility, and whether TDS applies (and at what rate). Reviewers only see exceptions; everything else is auto-tagged with an explainable trail so compliance teams can trust and audit the outcome. The system keeps learning and stays current with rule changes, so accuracy improves over time.
Data Foundation: Ingestion, OCR & Enrichment
We built automated pipelines to ingest invoices and ledger entries, normalize scans with OCR, and extract metadata (entities, jurisdictions, document types). The hence produced cleaned records are then embedded and stored for fast, governed retrieval—complete with lineage and versioning.
Smart Classification & Tax Logic
Our LLMs categorize transactions, apply the right tax slabs, check input credit eligibility, and flag withholding needs — using semantic understanding and an up-to-date rule base.
Human-in-the-Loop Review & Learning
Analysts review flagged exceptions, validate or correct model outputs, and feed insights back to improve accuracy—ensuring transparency and auditability.
Scalability, Governance & Cost Control
We scaled the system to run many parallel jobs so it can classify millions of transactions quickly. At the same time we enforced strict governance—role-based access, encryption, and full audit logs-to meet compliance needs. Finally, we tuned input/output and enabled autoscaling to reduce unit costs while ensuring capacity during busy filing cycles.
What Shorthills AI Did
We pulled millions of raw transactions into one pipeline and used GenAI to read short, messy descriptions, then applied up-to-date tax rules to classify each line: expense category, GST slab, ITC eligibility, and whether TDS applies (and at what rate). Reviewers only see exceptions; everything else is auto-tagged with an explainable trail so compliance teams can trust and audit the outcome. The system keeps learning and stays current with rule changes, so accuracy improves over time.
Data Foundation: Ingestion, OCR & Enrichment
We built automated pipelines to ingest invoices and ledger entries, normalize scans with OCR, and extract metadata (entities, jurisdictions, document types). The hence produced cleaned records are then embedded and stored for fast, governed retrieval—complete with lineage and versioning.
Smart Classification & Tax Logic
Our LLMs categorize transactions, apply the right tax slabs, check input credit eligibility, and flag withholding needs — using semantic understanding and an up-to-date rule base.
Human-in-the-Loop Review & Learning
Analysts review flagged exceptions, validate or correct model outputs, and feed insights back to improve accuracy—ensuring transparency and auditability.
Scalability, Governance & Cost Control
We scaled the system to run many parallel jobs so it can classify millions of transactions quickly. At the same time we enforced strict governance—role-based access, encryption, and full audit logs-to meet compliance needs. Finally, we tuned input/output and enabled autoscaling to reduce unit costs while ensuring capacity during busy filing cycles.
Higher accuracy, less rework
Exception handling and feedback loops improve precision over time.
Throughput at scale
Millions of rows processed quickly with auditable
lineage.
Lower unit cost
Automation reduces per-transaction analysis costs substantially.
Outcomes
A global professional services firm was falling behind on filings because manual classification couldn’t keep pace with millions of transactions and shifting tax rules. With Shorthills AI’s Transaction Analyzer, ledgers and invoices now flow through a single pipeline that auto-classifies most lines and surfaces only true exceptions for review. Associates spend less time deciphering cryptic descriptions and more time on edge cases, lifting accuracy and cutting rework. End-to-end lineage and citation-style traces make outputs audit-ready, reducing penalty risk and speeding approvals. Autoscaling keeps throughput high during peak periods while optimized I/O drives down per-transaction costs. In short, the firm moved from slow, error-prone processing to fast, governed classification that accelerates filings and frees specialists for higher-value advisory.
Outcomes
A global professional services firm was falling behind on filings because manual classification couldn’t keep pace with millions of transactions and shifting tax rules. With Shorthills AI’s Transaction Analyzer, ledgers and invoices now flow through a single pipeline that auto-classifies most lines and surfaces only true exceptions for review. Associates spend less time deciphering cryptic descriptions and more time on edge cases, lifting accuracy and cutting rework. End-to-end lineage and citation-style traces make outputs audit-ready, reducing penalty risk and speeding approvals. Autoscaling keeps throughput high during peak periods while optimized I/O drives down per-transaction costs. In short, the firm moved from slow, error-prone processing to fast, governed classification that accelerates filings and frees specialists for higher-value advisory.
Higher accuracy, less rework
Exception handling and feedback loops improve precision over time.
Throughput at scale
Millions of rows processed quickly with auditable lineage.
Lower unit cost
Automation reduces per-transaction analysis costs substantially.
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