See the cash problem three weeks before the spreadsheet does.
The thing about a cash crunch is that it doesn't announce itself. It builds slowly — an AR slip here, a pipeline tightening there, two senior hires closing in the same week — and then your quarterly cash model runs and says "we have 7.8 months of runway" and the next board meeting is a scramble instead of a plan.
Your AI cash flow modeling agent runs the model every single night. Bank balances, AR aging, pipeline-weighted bookings, hire commitments — all refreshed, all re-scenarioed. The morning your worst case dips below your runway floor, you get a Slack alert with three specific remediation options. Not next week. Not next month. Tomorrow morning.
You call the bridge meeting before the problem becomes a problem.
Monthly cash models are reports on problems that already happened.
A cash crunch rarely shows up as a single event. It shows up as three small things that accumulate: a major customer stretches their payment terms from net-30 to net-60, two senior hires close in the same pay period, a Q2 deal you had baked into the forecast slips to Q3. Each one moves the needle half a month. Put together, they move it by three. Your monthly spreadsheet picks it up on the following Tuesday and you're in scramble mode.
The fix isn't a better quarterly model. It's not a more detailed annual plan. It's refreshing the model every single day so the half-month slips get caught at half-month, not at three-months-compounded. The FP&A analyst who'd rebuild the model nightly if they could is the same analyst who's buried in variance commentary for the first week after close. Nobody actually runs the model every night. Until now.
Five feeds. Three scenarios. One answer. Every morning at 7 a.m.
Overnight, the agent refreshes five independent feeds — bank balances, AR aging, AP commitments, pipeline-weighted bookings, trailing-90 burn — then reruns your cash model under three scenarios: best case (collections hit, close rates hold, hires slip), base case (current assumptions), worst case (collections slow, close rates drop, hires on schedule). All three runways are pinned against your floor threshold.
Every morning, either nothing moved and the dashboard stays green — or the worst-case dipped overnight and an alert is already in your Slack with three specific remediation options sized to the gap.
A Slack message with three options. Not another tab you have to check.
Most cash dashboards fail because they expect you to log in and notice something changed. The agent inverts that: it only contacts you when something actually needs a decision, and it ships the decision options with the alert. "Worst-case runway dropped to 8.6 months — here are three specific bridges, each sized to close the gap to 11 months or better."
Remediation options are grounded in your actual data. Option one names the specific invoices over $50K that are aging. Option two names the specific pending hires and computes the runway impact of a 60-day delay. Option three prices your existing credit facility draw against the gap. You're not choosing between theoretical moves — you're choosing between concrete, sized actions.
After 90 days, the model is more accurate than your spreadsheet.
Every cash model has the same weakness: it smooths over the lumps. Your spreadsheet assumes collections come in evenly when in reality Q4 accelerates by two weeks and Q1 slows by one. It assumes burn is flat when it's actually 12% lower in summer and 18% higher in Nov-Dec. It assumes vendor payments are linear when your annual insurance payment lands in March and eats a half-month of runway in a single day.
After 90 days of observation, the agent has learned your company's specific seasonality and bakes it into every scenario. The "8.6 month worst-case" figure now accounts for the fact that March always pulls 0.3 months out of runway, and Q4 always puts 0.4 back in. The noise drops. The model starts flagging real movements, not seasonal ones.
The three questions every CFO raises first.
Does this replace our FP&A team's strategic model?
No — it complements it. Your FP&A team still builds the deep annual and strategic models in Excel or Anaplan. The agent runs the daily operational layer on top: catching collection slips two weeks before the monthly review, flagging pipeline tightening before it shows up in the quarter. Strategic modeling stays with your team; daily monitoring moves to the agent.
How do I know the scenarios are realistic?
You configure the scenario assumptions during deployment — collections flex, close rate ranges, hire-slip windows. The defaults are calibrated from a dataset of Series A-C companies, but nothing is hardcoded. You can override any variable, and the agent shows its work on every scenario so you can verify the math before acting.
What if we have a complex cap table or multiple bank accounts?
Multiple accounts and entities are supported out of the box — Mercury + Brex + a savings account, plus an international subsidiary, all roll into one consolidated view. Cap table complexity (SAFEs, convertibles, warrants, preferred tranches) gets mapped during deployment so dilution math and cash-against-next-round calcs reflect your actual instruments.
AI cash flow scenario modeling — answered.
Which data sources does the AI cash flow modeling agent pull from?+
Bank balances via Plaid or direct-connect (Mercury, Brex, SVB, Chase), AR data from your accounting system (QuickBooks, Xero, NetSuite, Sage Intacct), pipeline-weighted bookings from HubSpot or Salesforce, AP commitments from Bill.com or Ramp, and burn run rate computed from your last 90 days of GL activity. All sources refresh overnight on their own cadence — no manual exports ever.
How are the three scenarios actually constructed?+
Base case uses your existing assumptions for collections velocity, close rates, and hiring pace. Best case flexes collections +20%, close rates +15%, and assumes planned hires slip by 30 days. Worst case assumes AR slows by 30 days, close rates drop 20%, and all planned hires land on schedule. You can override any variable during onboarding — the defaults are reasonable starting points, not hardcoded rules.
What's the default runway threshold and can I change it?+
Default is 9 months on worst-case runway — the point where most boards want the CFO to be actively planning a raise or bridge. The threshold is tunable per company: capital-efficient businesses often set it to 6 months, venture-backed software typically holds 12-18, and mature businesses can go quieter. Set once, enforced forever.
How does it handle lumpy revenue or one-time collections?+
The agent distinguishes recurring from non-recurring by default — revenue tagged as annual prepay, one-time setup fees, or professional services gets modeled separately from monthly subscription revenue. The three scenarios explicitly show the impact of whether or not you collect the one-time items, so you see both the clean MRR runway and the with-lumps runway.
What do the remediation options actually look like?+
Each alert ships with three concrete actions sized to the gap. Example: "Worst-case runway dropped to 8.2 months · bridge the gap by (1) accelerating Q2 collections — 7 invoices over $50K outstanding, (2) delaying two pending senior hires by 60 days, or (3) drawing $1.5M of your $4M facility." Each option shows how many months of runway it buys.
Does the agent replace our FP&A team's models?+
No — it complements them. Your FP&A team typically builds the deep quarterly or annual plan in Excel or Anaplan. The agent runs the daily maintenance layer: catching the collections slip two weeks before it shows up in the monthly review, alerting when the pipeline tightens enough to threaten the hire plan. Strategic modeling stays with your team; operational monitoring moves to the agent.
How much does AI cash flow scenario modeling cost?+
Included in every beeeowl deployment tier, starting at $2,000 for Hosted Setup. One-time payment — no per-scenario fee, no per-seat charge, no monthly subscription. See the pricing page for the full breakdown.