90-Day Cash Flow Intelligence Engine

Augustus Chukwu

Completed work

AI Automation

Software Engineer

Business Workflow Automation

N8N

React

Supabase

FinTech & Payments

90-Day Cash Flow Intelligence Engine for SaaS & Fintech Founders

Summary

Built an end-to-end cash flow “radar” that gives founders a live 90-day cash forecast, scenario modeling, and runway alerts—powered by n8n, Supabase, and a React dashboard.

Client / Use Case

Ideal clients:

SaaS / fintech startups (Seed → Series B)

Founders, CFOs, and finance leaders who are currently forecasting in spreadsheets

Companies with multiple systems: bank, accounting (QuickBooks/Xero), payroll (Gusto/Deel), card processors (Stripe), etc.

This project was built as a full, public case study to demonstrate what I can implement for real clients who want production-style financial automation and decision intelligence.

The Problem

Finance teams and founders kept running into the same issues:

Cash forecasts live in fragile Excel models that break when the business changes.

Data is scattered across different tools (bank, AR, AP, payroll, expenses).

There’s no easy way to answer “what if?” questions without hacking formulas:

“What if we hire 5 more people?”

“What if growth slows 20%?”

“What if we cut marketing by 30%?”

Cash crunches are often discovered in the bank app, not 60 days ahead.

Key pain points:

Manual data gathering and spreadsheet wrangling every month

No single source of truth for cash inflows and outflows

No scenario engine

No proactive runway alerts

I set out to build a repeatable, automation-first solution that founders and finance teams could actually rely on.

My Role

Role: Financial Automation Engineer / Architect & Implementer

I handled:

System architecture and data model design

Supabase schema & sample data

All n8n workflows (data sync, aggregation, forecasting, scenarios, alerts, external APIs)

React dashboard design and integration with Supabase

Documentation, dev.to technical deep-dive, and Substack explainer

This is the kind of end-to-end work I can do for clients: from idea → architecture → automation → dashboard → documentation.

Tech Stack

Automation & Orchestration: n8n

Database & Warehouse: Supabase (Postgres)

Frontend: React + TypeScript + Tailwind + charting library

Logic: Python in n8n Code nodes where needed

(Optional) External APIs:

QuickBooks / Xero (AR/AP)

Stripe (payments)

Gusto (payroll)

Plaid (banking)

Repo & article:

GitHub: https://github.com/Etherlabs-dev/cashflow-forecasting-engine

Technical breakdown (dev.to): https://dev.to/etherlabsdev/how-i-built-a-90-day-cash-flow-intelligence-engine-541l

The Solution

I designed and built a 90-Day Cash Flow Intelligence Engine with three core layers:

Data Layer (Supabase)

Normalised tables for:

bank_transactions

ar_invoices (Accounts Receivable)

ap_bills (Accounts Payable)

payroll_runs

operating_expenses

A unified cash_events table where every inflow/outflow is stored in a consistent, source-agnostic format.

Derived tables:

daily_cash_snapshots – one row per day with opening balance, inflows, outflows, closing balance, burn averages

forecast_runs, cash_forecasts – 90-day projections (base/best/worst)

scenario_runs, scenario_forecast_deltas – what-if simulations

alert_events – runway and risk alerts

Automation Layer (n8n)

Data Sync workflows that:

Pull data from external APIs (Stripe, QuickBooks, Gusto, Plaid, etc.) or seeded sample data

Map provider-specific responses into the Supabase schema

Maintain idempotency using (data_source_id, external_id)

Aggregation workflow that:

Converts cash_events into daily_cash_snapshots

Forecast Engine workflow that:

Reads recent history and generates a 90-day cash forecast

Stores base, best, worst case trajectories

Scenario Runner workflow that:

Accepts scenario parameters via webhook (e.g. from the React app)

Recalculates the 90-day forecast with new assumptions (extra payroll, growth changes, expense cuts)

Risk Alerts workflow that:

Scans forecasts for points where cash drops below thresholds

Logs alert_events and can push messages to Slack/email

n8n workflow

Presentation Layer (React Dashboard)

Overview page – Current cash, base & worst-case runway, next 30-day net cash, historical + forecasted cash curves, and active alerts.

Scenarios page – Baseline vs scenario comparison charts, scenario creation form, runway impact summaries.

Working Capital page – AR/AP aging buckets (0–30, 31–60, 61–90, 90+), totals, and net working capital.

Case Study page – Explains the problem, architecture, and links to code + technical article.

home dashboard

Architecture at a Glance

Flow:

External tools → n8n → Supabase raw tables

Supabase raw tables → n8n → unified cash_events

cash_events → n8n → daily_cash_snapshots

daily_cash_snapshots → n8n → forecast_runs + cash_forecasts

Baseline forecasts → n8n → scenario runs & deltas

Forecasts → n8n → alert_events (+ Slack/email)

Supabase → React → dashboards & scenario UI

systems design

From a client perspective, this means:

You plug your systems in once,

Automation keeps the cash model updated,

The dashboard shows you where you are and where you’re heading,

You can safely ask “what if we do X?” and see a quantified answer.

Key Features

1. Unified Cash Events All money movements—regardless of source—are transformed into a consistent cash event model. This removes the complexity of reconciling multiple tools.

2. Daily Cash Snapshots Every day, the system computes:

Opening balance

Cash in / cash out

Net cash

Closing balance

Rolling burn rates

This powers fast, simple dashboard queries.

3. 90-Day Cash Forecast Using recent history, the engine generates:

Base case forecast

Best/worst-case bands

Day-by-day projected closing balances

This gives founders a clear “cash radar” instead of a static spreadsheet.

4. Scenario Modeling (“What If”) From the UI, users can run scenarios like:

“Hire 5 engineers at $X/month each”

“Growth slows 20%”

“Cut marketing by 30%”

The backend recomputes the full 90-day forecast with new assumptions and shows:

New runway vs baseline

Change in minimum cash level

When risk starts in each scenario

5. Runway & Risk Alerts Automated workflows monitor the forecast and create alerts when:

Base-case runway drops below a threshold (e.g. 60 days)

Worst-case forecast goes negative within the forecast window

Alerts can be stored in Supabase for the dashboard and pushed to Slack/email.

Value for a Real Client

If this system were deployed for a real SaaS/fintech client, they would:

Save hours per month No more manual CSV exports and spreadsheet rebuilds for cash forecasting.

See cash crunches 60+ days early Instead of being surprised by the bank balance, they get proactive alerts.

Make decisions with numbers, not guesses Hiring plans, marketing spend, and runway conversations can be anchored in modeled scenarios.

Have a reusable, API-driven tool It’s not just a one-off Excel; it’s a system that can grow with their tech stack.

What I Can Do for You (as a Freelancer)

Based on this project, here’s how I can work with you:

Design & implement financial automation systems Cash forecasting, payment recovery, revenue leakage detection, commission engines, etc.

Connect your existing tools QuickBooks, Xero, Stripe, Gusto, Plaid, banks, custom APIs—pulled into a clean data model.

Build dashboards your team will actually use For founders, finance, and ops teams—simple, visual, and tied directly to live data.

Ship full, documented projects With readmes, diagrams, technical articles, and handover docs so your team can own it long-term.

If you’re interested in implementing a version of this Cash Flow Intelligence Engine for your own company—or want something similar tailored to your stack—feel free to reach out on Contra and we can map out a build together.