Cash Flow Scenario Modeling
Your agent runs nightly scenario models using burn rate, AR aging, pipeline data, and committed expenses. If projected runway drops below your threshold — say, 9 months — it sends an immediate alert with three remediation scenarios: cut spend, accelerate collections, or bridge financing.
By the time you see a cash problem in a spreadsheet, it's been building for weeks.
Most CFOs check runway weekly or monthly in a spreadsheet. That means a problem that started 2-3 weeks ago shows up as a surprise. According to CB Insights' 2024 Startup Failure analysis, 38% of startups that fail cite running out of cash — and most say they saw it coming too late to act.
A $10M ARR company burning $800K/month has a 12.5-month runway. Comfortable — until a single delayed enterprise deal cuts that by 2 months overnight. No spreadsheet updated weekly catches that in time.
Five data pulls, three scenarios, zero manual work.
Every night at midnight, the agent pulls your current cash balance from your banking API, calculates your trailing 30-day average burn rate, factors in AR aging with likely collection dates, and includes committed expenses like signed contracts and upcoming payroll.
Then it runs three scenarios: base case (current trajectory), pessimistic (20% revenue miss), and optimistic (pipeline closes on time). If any scenario drops below your threshold, you know by 7 AM — not next Friday.
Not a dashboard you have to check. A Slack DM at 7 AM.
The alert names the driver — not just "runway is low" but "$340K invoice from Acme Corp is 45 days overdue." Then it gives you three scenarios with specific runway impact so you can act the same morning, not schedule a meeting to discuss it.
Each scenario includes the projected runway extension, the action required, and the trade-off. You pick one, forward it to your team, and move. According to McKinsey's 2024 CFO survey, companies that act on cash flow signals within 48 hours reduce working capital gaps by 30%.
After 90 days, the model starts predicting problems before the numbers show them.
Over months of nightly runs, the agent identifies patterns human analysis misses: burn spikes in Q1 from annual software renewals, AR aging increases when sales teams hit quota reset periods, runway volatility that correlates with enterprise deal timing.
These patterns get built into the model automatically. By quarter three, your scenarios account for seasonal effects your spreadsheet never captured. According to Deloitte's 2024 CFO Signals report, 67% of CFOs say their biggest forecasting gap is failing to account for recurring operational patterns.