Finance Infrastructure
Finance infrastructure for multi-location growth.
The reporting, systems, and data architecture that let a multi-unit business scale without losing the thread.
Growth exposes infrastructure. The spreadsheets and franchise-system reports that carried a handful of locations quietly stop working somewhere on the way to many. The close runs long enough to obscure where margin is leaking, every location keeps its own version of the truth, and the forecast model depends on the one person who built it.
I build the finance infrastructure a multi-location business actually runs on: a fast, reliable close; unit-level reporting operators trust; the right ERP, implemented so it gets adopted; and a data foundation solid enough that automation and AI have something real to stand on. The order matters: trustworthy data first, speed and automation after.
When to bring me in
What is usually breaking.
Infrastructure work tends to get deferred until it becomes the bottleneck. These are the signs it already has.
- 01The monthly close runs weeks behind and hides where margin is leaking.
- 02Every location or brand has its own dashboard, and there is no single number the whole business trusts.
- 03You are facing an ERP selection or mid-flight on an implementation and worried it will fail in adoption.
- 04AI is a board-level conversation, but the underlying data is not reliable enough to automate.
- 05The forecast model has become fragile and depends on a single owner to run it.
- 06Acquired brands each arrived with their own chart of accounts, close calendar, and controls.
The work
What the engagement covers.
Infrastructure is sequenced, not assembled all at once. The work makes the numbers trustworthy before it makes them fast, and earns the right to automate before automating. Each layer is built to be owned by the team rather than dependent on the person who built it.
Close transformation and controls
A monthly close compressed into a handful of business days through process redesign, with the control environment rebuilt entity by entity so audit findings stop following the company into capital conversations. A Controller function built or repaired to own the calendar with confidence.
FP&A and unit-level reporting
An FP&A function stood up from scratch where none exists, with weekly unit-level P&L, labor, and pacing built for operators to use. A unit-economics framework that defines what is measured, who owns it, and the cadence that keeps leadership informed.
ERP selection and implementation
The right enterprise system, selected through a structured RFP driven by the people who will run on it, then implemented on the close calendar so the team adopts it as the operating record. Adoption is the success criterion; a system that goes live but does not get used is expensive shelfware.
Data foundation and practical AI
An enterprise data warehouse and governance framework that create a single trusted source of metrics across brands and locations, with calculation logic moved out of fragile reporting layers and into the data itself. AI and automation deployed on top of that foundation, with governance built in from day one.
Proof
Infrastructure that scales with the footprint.
Anonymized engagements across close transformation, FP&A buildout, ERP implementation, and data infrastructure. The proof below maps directly to the work above.
View all engagements- 01
Close cut from weeks to days. Finance team made materially leaner as revenue grew. Consecutive clean audits.
PE-backed multi-brand operator. Multi-brand portfolio assembled through acquisition, operating across many locations.
Close transformation / Control environment / Team building
- 02
First enterprise data warehouse deployed. AI tools live across Finance, Accounting, and field support. Silent margin expansion through automation.
PE-backed multi-unit operator. Multi-brand, multi-unit portfolio operating on fragmented legacy analytics.
Data infrastructure / AI implementation / Operating intelligence
- 03
FP&A function built from scratch. ERP selected and implemented. New brands integrated within six months.
PE-backed multi-brand franchisor. Multi-brand franchise system in an active growth phase, adding brands through acquisition.
FP&A buildout / ERP selection & implementation / Unit economics framework
- 04
Full ERP lifecycle from RFP to go-live. Adoption treated as the success criterion.
PE-backed multi-unit operators. Two distinct businesses: a multi-brand franchisor and a multi-unit consumer services platform.
ERP selection & implementation / Data infrastructure
- 05
Enterprise-grade data warehouse and AI analytics built for an operator-owned franchise, run by one person, no BI team. Daily operating intelligence at a scale where nobody builds this.
Operator-owned multi-unit franchise. Small business: operator-owned and run, not PE-backed, not venture-funded.
Operating intelligence / Data infrastructure
- 06
FP&A team built during 4x location growth. Weekly unit-level dashboards. EBITDA consistency held through scale.
National multi-unit franchise. Grew its location count roughly fourfold during the engagement.
FP&A buildout / Forecasting & planning rigor / Unit economics framework
- 07
ASC 606 recognition engine delivered in under a quarter using AI. Ongoing journal entries automated. Defensible path off cash basis.
Online coaching & subscription business. Multi-product subscription catalog across coaching programs and digital products, multiple payment processors.
Revenue recognition / Data infrastructure / AI-assisted delivery
- 08
Model rebuilt for multi-user collaboration. Decisions move at the pace of the business.
Early-stage subscription business. Preparing for the next stage of growth.
Forecasting & planning rigor / Data infrastructure
Questions
Frequently asked.
A close that runs fast and clean enough to produce a defensible number on demand, a control environment that survives diligence, unit-level economics that explain where the value actually comes from, and a liquidity forecast that holds. Capital providers are not only buying the performance. They are buying confidence that the numbers are real and repeatable. Infrastructure is what makes that case.
Most ERP implementations finish on paper and fail in practice, because the accounting team is handed a new system and a new way of working at the same moment the close still has to go out the door. The fix is to select around the requirements of the people who will run on the system, pace the implementation against the close calendar rather than the vendor's timeline, and treat adoption, not go-live, as the finish line.
Only after the data foundation can support it. You cannot automate unreliable data, and you cannot deploy AI on top of a system nobody trusts. Done in the right order (trusted data warehouse first, then practical automation embedded in the workflow with governance built in), AI earns its place by removing manual workload and freeing analysts for higher-value work. Done in the wrong order, it is theater.
It depends on how fragmented the starting point is. A platform assembled by acquisition, with a different chart of accounts and close calendar per entity, is a longer road than a single-entity cleanup. The pattern is consistent, though: an honest diagnostic of the close as it actually runs, a Controller function to own it, the control environment rebuilt entity by entity, and the manual reconciliation work removed so the team can close in a fraction of the time it used to take.
The fastest way to find out if we are a fit is a short conversation. Tell me about the work you are trying to do.