Cash Flow Snapshot

Cash Flow Snapshot

Produces a 30/60/90-day cash flow forecast with percentage-variance confidence bands and named risk flags. Delivers a two-part output: a concise chat summary and a downloadable XLSX workbook.

When inputs are supplied through report URLs, read the SandBase API map. Resolve the listed capability with sandbase_discover, inspect its returned name with sandbase_inspect, then follow execute_as to call sandbase_run(name: "<returned name>", arguments: { ... }) using only the current schema. For async results, poll sandbase_run_get with the returned run_id within the task budget; report pending or failed runs without automatically resubmitting them.

Quick start

"Will I make payroll next month?"

The agent pulls AR/AP and fixed costs from authorized connected sources or supplied files, calculates expected inflows and outflows across 30, 60, and 90-day windows, applies confidence bands based on each customer's historical payment variance, and flags specific risks by name.

Workflow

Step 1 — Identify available data sources

Check which authorized sources are available. Prefer them in this order when present:

1. QuickBooks — primary source for AR aging, AP, and fixed costs
2. PayPal — transaction history and settlement timing
3. Stripe — charge and payout history
4. Square — sales and payout history
5. CSV upload — fallback if no connector is connected

If no connector is live and no file is attached, ask the user to either connect a source or upload a CSV (income/expense tabular data, any reasonable format). Note which sources were used in the output — this affects confidence band width.

Step 2 — Pull the data

From QuickBooks:

• Opening cash balance and as-of date
• AR aging report: customer name, invoice amount, invoice date, due date, days outstanding
• AP: vendor name, amount due, due date
• Recurring fixed costs: rent, payroll, subscriptions (look for recurring transactions)

From PayPal / Stripe / Square:

• Settlement history: transaction date, amount, settlement date
• Use settlement lag (transaction date → payout date) to compute each source's average and variance payment delay

From CSV upload:

• Parse as income/expense tabular data
• Required columns (flexible naming): date, amount, type (income or expense), description
• If columns are ambiguous, show the header row and ask the user to confirm mapping
• Obtain an opening cash balance and as-of date before claiming ending liquidity or payroll coverage

Step 3 — Compute historical payment timing

For each AR customer (or income source from CSV), calculate:

• Mean payment lag — average days from invoice/transaction date to receipt
• Payment variance — standard deviation of payment lag across last 6–12 payments
• Use variance to set confidence band width (see Step 4)

If fewer than 3 payments exist for a customer, use the population mean as the point estimate and apply a ±30% variance band as the default. When running on CSV data with sufficient history (≥3 payments per source), compute the band from the actual payment variance — do not assume ±30%.

Step 4 — Build the 30/60/90-day forecast

Produce three time windows: 0–30 days, 31–60 days, 61–90 days.

For each window, compute:

Confidence band formula:

band_pct = weighted_avg_stddev_days / avg_payment_lag_days
band_amount = expected_inflows × band_pct
low_net_flow  = net_cash_flow − band_amount
high_net_flow = net_cash_flow + band_amount

Display band_pct as a percentage rounded to one decimal place. Cap at ±50% — higher variance means the data is too thin to model; flag it instead (see Step 5).

When opening cash is available, calculate liquidity separately:

expected_ending_cash = opening_cash + cumulative_expected_net_flow
low_ending_cash      = opening_cash + cumulative_low_net_flow
high_ending_cash     = opening_cash + cumulative_high_net_flow

Without opening cash, report net cash flow only and state that ending liquidity and payroll coverage cannot be determined.