openfpa

Use when the user wants their model in Excel with real working formulas - "export this to Excel", "a workbook I can hand my board", "something that recalculates when I change an assumption". Produces a live-formula workbook generated from the model structure and verified against the engine before delivery.

Use when producing a board deck, investor update, or CFO briefing from an openfpa forecast - turning model output into a board-ready narrative and exportable artifacts.

Use when building a new openfpa forecast model from a company's financials - a trial balance, a P&L export, or a pasted income statement - and you need a runnable config to exist before any forecasting or analysis.

Use when modeling a multi-segment company that discloses segment net sales and segment Adjusted EBITDA (ASU 2023-07) but not segment COGS or operating income - rolls segment P&Ls into a consolidated forecast and reconciles total segment Adjusted EBITDA to the disclosed total. Generated for Fox Factory (PVG/AAG/SSG).

Use when you want the model to learn from how its past forecasts actually turned out - scoring forecasts against the company's real actuals, backtesting assumptions on history, and proposing ratified improvements. Runs at/after monthly close.

Use when a human reviewing a forecast catches something off ("December always spikes", "you're double-counting deferred revenue", "that Q3 number was a one-time contract") - captures it as a durable, typed correction in the company's memory so future forecasts are grounded by it.

Use when answering "when do we run out of cash", building a 13-week cash forecast, sizing a credit line, or analyzing near-term liquidity and payment timing in openfpa.

Use when interpreting financial actuals, reviewing margins or cash, drawing conclusions from a P&L or balance sheet, or about to tell someone a number means something - the judgment layer that separates "AI that does math" from "AI that thinks like a CFO."

Use when wiring a company's real numbers into an openfpa model - from local spreadsheets (P&L, balance sheet, AR/AP aging, inventory), a live system via MCP or API (QuickBooks, NetSuite), public filings (10-K/10-Q), or anything else. Not married to one source - build the ingestion for whatever the company has; produces one normalized account-amount shape the rest of the toolkit reads.

Use when starting FP&A work for a new company, onboarding a business into openfpa, or asked to "understand my business / set up a model for us" before any forecasting - produces a durable business profile and generates business-specific skills.

Use when running a month-end close, refreshing a forecast with the latest actuals, computing plan-vs-actual variance, or producing a "how did the month go" analysis in openfpa.

Use when you run FP&A for several clients and want your practice to compound - mines patterns that generalize across your same-type clients, validates them by leave-one-out cross-client backtesting, and promotes ratified priors and skills into a local library that seeds every new client. All local; nothing leaves your machine.

Use when analyzing which products make or lose money, ranking SKUs by margin or contribution, running a Pareto/80-20 on a product line, or deciding which SKUs to cut, reprice, or push in a product business.

Use after forecasts have scored actual outcomes and you want the AI to run bounded autonomous champion/challenger research epochs, discard weak candidates, and propose only evidence-backed model promotions.