How Team Car Care Reached 70% Reconciliation Automation With Nominal

At a glance

• Reduced a four-person inventory reconciliation workflow to one agent-supervised role
• Reached 70% automation of reconciliation tasks within months of going live
• Freed senior accounting talent to move into GL leadership
• Supported deal readiness for a private equity-backed exit process

Nominal CEO Guy Leibovitz sat down with Matt Stanguay to hear how Team Car Care is using AI agents to transform its accounting operations. You can watch the full interview here.

When transaction volume outgrows the team doing the work

Team Car Care is the largest franchisee of Jiffy Lube in the United States, with nearly 500 locations across the country operating under six entities. Every customer interaction generates transactions. Multiply that by hundreds of locations, and by the time month-end arrives, the accounting team is working through a volume of data that few mid-market back offices are built to handle.

The company transitioned to Workday in 2022, replacing a 20-year-old financial system that had accumulated years of workarounds and manual processes built outside the core platform. The ERP brought meaningful consolidation, a single source of truth across entities, and a foundation for higher-volume automation in areas like sales. But some workflows remained stubbornly manual, and none more so than inventory reconciliation.

The accounting team was also operating under a different kind of pressure. As a private equity-backed business, Team Car Care needed its back office ready for a potential deal. Clean books, accurate entity structures, and the ability to produce audit-ready financials quickly were not aspirational, they were requirements. The legacy environment could not support that. The new one still needed work.

The reconciliation problem, at scale

The reconciliation challenge at Team Car Care is structural. The company's point-of-sale system and its ERP do not communicate bidirectionally. When a store receives a shipment and checks in an incorrect quantity, the discrepancy does not resolve itself automatically.

A store receives six cases. The team checks in five. The invoice arrives for six. A few days later, the sixth case turns up, gets entered separately, and now sits in the ledger with no matching invoice. Someone on the accounting team has to trace that mismatch across hundreds of locations, determine whether the entries can be married together or whether a journal entry is needed to true up the balance, and repeat that process for every similar exception, every month.

Before automation, four people were assigned to inventory reconciliation. The volume was simply too high to do less with.

CFO Matt Stanguay was direct about what that meant in practice: even with the team in place, the work consumed capacity that could not be redirected anywhere else. There was no bandwidth left to solve harder problems, because the team was fully occupied keeping up with the existing ones.

Building an agent to execute the reconciliation logic

The team Car Care began working through how to approach the problem about a year and a half before going live, before Nominal was in the picture. Stanguay's framing was deliberate: identify a use case large enough in volume to matter, narrow enough in scope to be solvable, and specific enough that the logic of the manual work could be translated into an agent.

Inventory reconciliation fit that profile: the logic was defined, the inputs were known, and the outcomes, whether matching entries or posting a journal entry, were finite.What was missing was a platform that could execute that logic at scale, integrated with Workday, without requiring a full ERP overhaul.

When they encountered Nominal, implementation began with data: pulling together the right datasets, establishing a clean baseline, and then building the agent model to replicate the reconciliation logic. That foundation work took several months, with the deployment going live in fall 2025.

From four people to agent-supervised, in under a year

Within months of going live, Nominal's agents were handling approximately 70% of inventory reconciliation automatically. The remaining 30% surfaces as flagged exceptions requiring human review, and the team continues to refine the model to push the automation rate higher.

The impact on headcount and team structure was immediate. The accountant who had led the reconciliation process was promoted to run the GL team. His old role needed to be backfilled, but the job description had fundamentally changed. Rather than hiring a specialist to replicate what he had done manually, the team assigned the work to someone already on staff. That person now supervises the agent workflow: reviewing outputs, handling exceptions, building journal entries where needed, and submitting them to the GL team for posting.

The person doing that work today is not a trained accountant.

That shift reflects something Stanguay describes as one of the more consequential outcomes of the Nominal deployment. When agents are executing the work, the skill requirement for overseeing that work changes. The technical accounting knowledge that previously went into reconciliation can now be applied at a higher level, in more complex analysis, in GL oversight, in the kinds of problems the team previously had no capacity to address.

Continuous close, not a shorter close

One framing that emerged from Team Car Care's experience with Nominal reflects a broader shift in how the accounting team thinks about monthly close. Rather than measuring success by how many days the close can be compressed, the team operates closer to a model where the work does not accumulate until month-end.

When reconciliation is running continuously, when exceptions are flagged in real time rather than discovered during close, the month-end event becomes a review rather than a recovery. That distinction matters at scale. At nearly 500 locations, processing tens of thousands of transactions per day, a reconciliation backlog compounds quickly. Eliminating that accumulation changes the nature of the close process entirely.

Deal readiness as a forcing function

The private equity context shaped how Team Car Care approached the entire back-office transformation. Going into a deal process with fragmented systems, manual reconciliation, and limited auditability is not just an operational inconvenience. It creates material risk in the diligence workstream and can slow or complicate a transaction.

The approach to the Workday implementation, and subsequently to Nominal, was grounded in that reality. Clean data, a coherent entity structure, and the ability to produce accurate financials quickly were not nice-to-haves. They were part of the deal.

That framing has practical implications for how other finance leaders should think about automation investments. The advice: start with a problem that is both large in volume and narrow in scope, one that is clearly a pain point but has defined inputs, defined logic, and a finite set of outcomes.

Inventory reconciliation was that problem for them. It will be something different for every organization, but the approach of finding the right problem before selecting the tool is consistent.

The goal is to build processes that are anti-fragile: designed well enough that when exceptions occur, the system handles them, rather than depending on individual judgment to catch and fix them each time.

About Nominal

Nominal is the Agentic Performance Management (APM) platform for multi-entity accounting teams. Its agents execute reconciliation, consolidation, close management, flux analysis, and transaction matching, operating on top of any existing ERP without replacing it. Every agent action is traceable, reviewable, and human-approved.

Ready to move your team from executing the work to overseeing it? Book a demo and see what agent-powered reconciliation looks like in your environment.