Executive Summary
A leading global professional services firm needed to streamline responses to government tax notices. Their manual workflow—reading notices, searching client records and internal databases, and drafting replies—took ~3 days and depended heavily on multiple internal reviews. Shorthills built a GenAI “co-pilot” that ingests notices, extracts issues, retrieves context from filings, prior replies, and an internal knowledge base, and auto-drafts replies using a structured template with human-in-the-loop validation. As a result, the solution cut first-draft creation to 10–15 minutes with ~90% initial accuracy, improving throughput, consistency, and auditability while freeing experts for higher-value work.
Tech Stack
Django
OpenAI
Langchain
Celery
Redis
MySQL
Azure
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
Real-Time M&A Intelligence for 18,000+ Dealerships
Modernizing tax-notice response at a leading professional services firm—LLM co-pilot cuts first drafts from ~3 days to 10–15 minutes.
Industry
Professional Services
Region
APAC
Technology
Azure
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
Executive Summary
A leading global professional services firm needed to streamline responses to government tax notices. Their manual workflow—reading notices, searching client records and internal databases, and drafting replies—took ~3 days and depended heavily on multiple internal reviews. Shorthills built a GenAI “co-pilot” that ingests notices, extracts issues, retrieves context from filings, prior replies, and an internal knowledge base, and auto-drafts replies using a structured template with human-in-the-loop validation. As a result, the solution cut first-draft creation to 10–15 minutes with ~90% initial accuracy, improving throughput, consistency, and auditability while freeing experts for higher-value work.
Tech Stack
Open AI
Langchain
Redis
MySQL
Celery
Azure
Django
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
Professional services—especially large tax and legal practices—handle high volumes of government notices across jurisdictions and formats. Manual, research-heavy replies drag out timelines, use up senior bandwidth, and risk non-compliance under deadline pressure. Without smarter drafting and evidence retrieval, response costs rise and tasks queue up into backlogs that delay filings and increase compliance risk.
Time-Intensive, Manual Process
Researching, synthesising, and drafting replies consumed ~3 days per notice.
Complexity &
Risk
Increased error risk and rework due to manual intervention, evolving laws, multi-issue notices, and large evidence sets.
Scalability & Consistency
Manual processes led to inconsistent quality and limited scalability due to reliance on senior reviews.
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.
Our Solutions
Notice Ingestion & Issue Extraction
Upload the notice (PDF), then LLMs identify and structure each issue raised by authorities for targeted research and drafting.
Contextual Retrieval (RAG) Across Sources
For each issue, the system pulls client filings, prior replies, internal laws/acts/circulars, precedents, and templates—grounding responses with evidence and citations.
Structured Drafting with Human-in-the-Loop
A 13-point template organizes arguments and references; consultants review, edit, and approve drafts, preserving accountability and audit trails.
Secure, Scalable
Architecture
Azure-hosted GPT-4, custom RAG, Databricks processing, SQL storage, Celery/Redis orchestration, and strict data governance support reliability and scale.
Challenges
Time-Intensive, Manual Process
Researching, synthesising, and drafting replies consumed ~3 days per notice.
Complexity &Risk
Increased error risk and rework due to manual intervention, evolving laws, multi-issue notices, and large evidence sets.
Scalability & Consistency
Manual processes led to inconsistent quality and limited scalability due to reliance on senior reviews.
Professional services—especially large tax and legal practices—handle high volumes of government notices across jurisdictions and formats. Manual, research-heavy replies drag out timelines, use up senior bandwidth, and risk non-compliance under deadline pressure. Without smarter drafting and evidence retrieval, response costs rise and tasks queue up into backlogs that delay filings and increase compliance risk.
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.
Streamlining tax-notice response with an LLM co-pilot at a leading professional services firm—cutting first drafts from 3 days down to an efficient 10–15 minutes.
Industry
Professional Services
Region
APAC
Technology
Azure
What Shorthills AI Did
We ingest each tax notice, let GenAI read and break it into clear issues, then pull the right context from client filings, past replies, and the firm’s knowledge base. The system drafts a reply in a standard template—clearly citing evidence—so reviewers only fine-tune and approve. Everything is tracked for audit, and the model improves with feedback, staying current with rule changes.
Notice Ingestion & Issue Extraction
We upload the notices (PDFs), then LLMs identify and structure each issue raised by authorities for targeted research and drafting.
Contextual Retrieval (RAG) Across Sources
For each issue, our system pulls up client filings, prior replies, internal laws/acts/circulars, precedents, and templates—grounding responses with evidence and citations.
Structured Drafting with Human-in-the-Loop
A 13-point template organizes arguments and references; consultants review, edit, and approve drafts, preserving accountability and audit trails.
Secure, Scalable Architecture
Azure-hosted GPT-4, custom RAG, Databricks processing, SQL storage, Celery/Redis orchestration, and strict data governance support reliability and scale.
What Shorthills AI Did
We ingest each tax notice, let GenAI read and break it into clear issues, then pull the right context from client filings, past replies, and the firm’s knowledge base. The system drafts a reply in a standard template—clearly citing evidence—so reviewers only fine-tune and approve. Everything is tracked for audit, and the model improves with feedback, staying current with rule changes.
Notice Ingestion & Issue Extraction
We upload the notices (PDFs), then LLMs identify and structure each issue raised by authorities for targeted research and drafting.
Contextual Retrieval (RAG) Across Sources
For each issue, our system pulls up client filings, prior replies, internal laws/acts/circulars, precedents, and templates—grounding responses with evidence and citations.
Structured Drafting with Human-in-the-Loop
A 13-point template organizes arguments and references; consultants review, edit, and approve drafts, preserving accountability and audit trails.
Secure, Scalable Architecture
Azure-hosted GPT-4, custom RAG, Databricks processing, SQL storage, Celery/Redis orchestration, and strict data governance support reliability and scale.
From ~3 days to 10–15 minutes
First-draft turnaround time dropped dramatically.
~90% first-draft accuracy
Less rework, faster finalization, and better compliance.
Higher throughput & consistency
Standardized structure and visibility; improved consultant productivity.
Outcomes
A global professional services firm was losing days to manual, research-heavy responses for government tax notices, tying up senior reviewers and risking deadlines. With Shorthills AI’s reply-to-notice co-pilot, teams now get a structured first draft in 10–15 minutes instead of ~3 days, grounded with citations from client history and internal guidance. Initial accuracy is ~90%, so associates focus on clarifications rather than writing from scratch, lifting throughput and cutting rework. A consistent template standardizes arguments across jurisdictions, while built-in audit trails strengthen defensibility. Because fewer drafts need senior rewrites, review queues shrink and backlogs clear faster. Net result: faster responses, higher consistency, and better compliance—freeing specialists to handle complex cases and client strategy.
Outcomes
A global professional services firm was losing days to manual, research-heavy responses for government tax notices, tying up senior reviewers and risking deadlines. With Shorthills AI’s reply-to-notice co-pilot, teams now get a structured first draft in 10–15 minutes instead of ~3 days, grounded with citations from client history and internal guidance. Initial accuracy is ~90%, so associates focus on clarifications rather than writing from scratch, lifting throughput and cutting rework. A consistent template standardizes arguments across jurisdictions, while built-in audit trails strengthen defensibility. Because fewer drafts need senior rewrites, review queues shrink and backlogs clear faster. Net result: faster responses, higher consistency, and better compliance—freeing specialists to handle complex cases and client strategy.
From ~3 days to 10–15 minutes
First-draft turnaround time dropped dramatically.
~90% first-draft accuracy
Less rework, faster finalization, and better compliance.
Higher throughput & consistency
Standardized structure and visibility; improved consultant productivity.
Frequently asked questions
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