Fracfai’s cash flow forecasting service applies institutional-grade data science to real-world accounting data. The result: forecasts that are more rigorous, more stable, and more transparent than traditional spreadsheet models, built to support your advisory work, not to replace it.
We do not offer bookkeeping, cleanup, or tax services. Forecasting is a dedicated, neutral layer that sits behind your firm and strengthens your conversations about future cash.
Models are designed to work with real-world books: seasonality, gaps, odd outliers, and imperfect categorization.
Outputs are written so partners, controllers, and clients can understand why the forecast looks the way it does.
Forecasts are decision-support tools. They are meant to sit alongside your own judgment, not to dictate outcomes.
Most forecasts used in practice are built in spreadsheets, driven by a few growth-rate assumptions and manual tweaks. They can be helpful, but they struggle with noise, structural change, and uncertainty. Fracfai’s approach is different by design.
Instead of relying on a single forecasting formula, we use a family of models, tuned to different horizons and patterns, to reduce over-reliance on any one view of the future.
We do not simply average prior periods and extend a line. The system examines how inflows and outflows actually behave — including volatility and clustering — and reflects that in the forecast.
This is not a visualization tool with a thin forecasting layer. The engine is forecasting-first: the visuals and narratives exist to explain the underlying statistical view, not to replace it.
Manual Excel models can be fragile: a single assumption change can swing the entire result. Our method is designed to be less sensitive to small edits and more consistent across time.
The service is intentionally partner-friendly. We provide a rigorous forecast; you decide how to frame it, adjust it, and use it inside your broader advisory work.
Forecasts come with explanations, not just numbers. The aim is to be something you can stand behind in partner meetings and client conversations.
The modeling pipeline is built to be robust to noise, honest about uncertainty, and informative for real-world decisions — without requiring you to think like a quant.
Rather than commit to a single view, we look at the business through multiple forecasting lenses. This helps reduce the risk that one modeling choice dominates the story.
The system is designed to handle irregularities, outliers, and structural breaks more gracefully than simple trend-extensions or rules-based tools.
Instead of hiding volatility, we quantify it. Forecasts include intervals that frame upside, base, and downside paths so advisors can talk in ranges, not certainties.
We examine which patterns and drivers seem most influential for the forecast — turning raw structure into plain-English commentary you can use.
Technical details (such as specific model types or training procedures) are deliberately abstracted. The focus for firms is on reliability, interpretability, and fit with professional judgment — not on algorithms.
Deliverables are built so you can incorporate them directly into advisory meetings, internal files, and lender or board conversations.
All forecasts are intended as decision-support, not as guarantees. They should be used alongside your own analysis, client knowledge, and professional standards.
Engagements are intentionally straightforward. The goal is to plug into how you already work, not to force a new software stack on your firm.
Short call to understand the client, their business model, the forecasting horizon you care about, and how you plan to use the forecast (runway, planning, lender, board, etc.).
You provide exports or controlled access from your accounting system. Where appropriate, we encourage data minimization and anonymization of direct identifiers.
For new or complex clients, we may recommend a Financial Structure Diagnostic first. This helps avoid forecasting on obviously distorted books.
We run the forecasting engine, perform stability and reasonableness checks, and structure forecasts into clear horizons with uncertainty intervals.
We prepare a written summary, supporting visuals, and any agreed-upon scenario framing, tailored to how you want to present this to stakeholders.
We walk through the forecast with you, answer questions, and discuss how you may want to adapt or contextualize it within your own advisory framework.
Fracfai does not dictate decisions to your clients. Forecasts are inputs; your firm remains responsible for recommendations, engagement structure, and compliance with applicable standards.
Under the hood, the forecasting engine reflects years of quantitative work in financial time-series — but the interface is designed for CPAs and finance leaders.
You do not have to read research papers or debug modeling code. You get the benefit of that work translated into practical forecasts and narratives you can use with clients and stakeholders.
