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.
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.
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