How a Custom Manufacturer Reclaimed $500K of Exec Time

A PE-acquired manufacturer's COO was burning half his week on VLOOKUPs. An ML stack now pulls live data and quotes jobs automatically — reclaiming $500K of executive time a year.

~$500K

Saved annually in exec and ops time

4–8 weeks

Implementation Time

Michael Cohen

Managing Director

Stalliant

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The Challenge

A Midwest custom manufacturer, newly acquired by private equity, installed a Harvard-educated COO to professionalize operations. Within months, the COO was consuming nearly half his working hours pulling data exports, running VLOOKUPs, and managing a 20-to-30-tab Excel workbook just to generate the information he needed to do his actual job. The business had no clear visibility into cash flow, labor profitability, or cost-per-job. Job quoting relied entirely on the CEO’s institutional intuition, creating extreme key-person risk. Without intervention, the organization would continue bleeding executive capacity to manual data wrangling while flying blind on profitability.

What They Built

Stalliant began with the tools the client already owned — Excel and QuickBooks — building a Power Query-based data model that connected live to the system of record, eliminating manual CSV exports. The stack expanded into Microsoft Fabric and Azure to create data pipelines and nightly snapshots, with machine learning applied to job quoting accuracy and OCR deployed for purchase orders and invoices. The entire solution was built inside the client’s existing Microsoft environment with no new SaaS licenses required. The unexpected outcome: once all operational data was connected, the team identified the most profitable customers and prioritized them — turning a cost-reduction project into a top-line revenue lever that improved customer lifetime value and reduced acquisition cost.

Stalliant began with the systems the client already owned, rather than introducing new tooling — a deliberate choice that eliminated the adoption friction of onboarding new software into an organization already stretched thin. The first phase established a Power Query-based data model connected live to QuickBooks, the system of record. This eliminated the manual CSV export cycle and gave the COO real-time data access for the first time. The model was validated before any ML capability was layered on top. The second phase expanded the stack into Microsoft Fabric and Azure, creating structured data pipelines and nightly snapshots across job costs, labor hours, and vendor expenses. With a governed data foundation in place, machine learning was applied to job quoting — encoding historical cost and margin data into a model that could generate quotes without relying on the CEO’s institutional knowledge. OCR was deployed for purchase orders and invoices, eliminating manual document entry. An unexpected strategic outcome emerged: once all operational data was connected, the team identified the company’s most profitable customers, turning a cost-reduction engagement into a revenue optimization lever.

AI Role

Machine learning models are applied to job quoting, learning from historical cost and margin data to improve quoting accuracy over time. OCR technology processes purchase orders and invoices automatically, extracting structured data without manual entry. Power Query and Azure data pipelines connect live to source systems, automating the data extraction and consolidation that previously consumed the COO's time.

Infrastructure

Microsoft Excel (existing — primary source system and reporting layer) • QuickBooks Online (existing — system of record for financials) • Microsoft Fabric and Azure (expanded data pipeline and storage infrastructure) • Power BI (reporting and visualization) • Power Query (live data model layer connecting source systems)

Integration Points

Power Query connected live to QuickBooks Online, eliminating manual CSV export cycle • Azure data pipelines pulling nightly snapshots from operational source systems • OCR processor reading purchase orders and invoices and writing structured data to the data model • ML quoting model reading from historical job cost and margin data in the unified data model

Impact

~$500K Annual Time Savings

Automation returned approximately $500,000 per year in combined executive and operator time — $100,000 attributed directly to the COO’s reclaimed capacity (50% of his role), with an additional $300,000–$400,000 from employees no longer tied up in manual Excel processes.

5–15% Margin Improvement Per Job

With accurate, real-time visibility into labor hours, cost of goods, and vendor expenses, the client could quote custom manufacturing jobs with precision for the first time — translating to a 5–15% bottom-line margin improvement on individual jobs.

Key-Person Risk Eliminated

All job quoting had previously flowed through the CEO’s intuition — a single point of failure. The new system encoded historical data into an ML-assisted quoting foundation, removing the CEO as the sole dependency on the firm’s most critical pricing decisions.

Technology Utilized

Implementation Complexity

Time Savings

Cost Reduction

Revenue Growth

The solution was built inside the client's existing Microsoft environment (Excel, QuickBooks, Power Query, Microsoft Fabric, Azure), requiring no new SaaS licenses. However, integrating these systems into a unified data model with ML-powered quoting and OCR-based document processing required meaningful custom development and data engineering work.

Best Fit For

Lower-middle-market manufacturers, distributors, or industrials ($10M–$100M revenue) that have recently received PE investment and are suddenly responsible for investor reporting their finance function was never built to produce. Also relevant to PE operating partners who need to rapidly professionalize portco reporting without replacing existing systems.

Michael Cohen

Managing Director

Stalliant

Managing Director at Stalliant, delivering CFO and data engineering solutions for lower-middle market industrials and PE portfolio companies.

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Industry:

Manufacturing & Industrial

Financial Services

Business Function:

Finance & Accounting

Operations

Executive & Strategy

Company Size:

51-250

Project Cost:

Cost not disclosed

Ownership:

Private Equity-Backed

Organization Type:

Private Company

AI Pattern:

Data Synthesis & Reporting

Predictive Analytics & Forecasting

Document Processing & Extraction

Value Type:

Time Savings

Cost Reduction

Revenue Growth

AI Model:

Frequently Asked Questions

How did a mid-market manufacturer reclaim about $500K of executive time with an ML data stack?

The experts started with the tools the company already owned — building a Power Query data model connected live to QuickBooks to kill manual CSV exports — then expanded into Microsoft Fabric and Azure for nightly data pipelines, applied machine learning to job quoting, and added OCR for purchase orders and invoices. This returned roughly $500K a year in combined executive and operator time.

What AI approach and tools were used in this manufacturing project?

The approach combined data synthesis and reporting, predictive analytics, and document processing, with machine learning applied to job quoting and OCR for documents. It was built inside the company's existing Microsoft environment using Excel, Microsoft Fabric, Azure, Power BI, and QuickBooks Online, with no new SaaS licenses; no specific AI model was named.

What results did the manufacturer achieve?

Three outcomes: a 5–15% margin improvement per job from accurate, real-time cost visibility; elimination of key-person risk by encoding the CEO's quoting intuition into an ML-assisted foundation; and roughly $500K in annual time savings ($100K from the COO's reclaimed capacity plus $300K–$400K from staff freed from manual Excel work).

How long did the AI data and quoting project take?

About two to four months, built in two phases — first the live QuickBooks data model, then the Fabric/Azure pipelines with ML quoting and OCR layered on top.

Who is this approach best for?

Lower-middle-market manufacturers, distributors, and industrials ($10M–$100M revenue) recently backed by private equity and suddenly responsible for investor reporting, and PE operating partners who need to professionalize portfolio-company reporting without replacing existing systems.