A cash forecast the CFO can actually use — grounded in your own ledgers
Aged AR weighted by historical collection probability per debtor. Pipeline weighted by close probability. Near-term payables and PO commitments. Every projection cites the source, and confidence intervals surface the uncertainty explicitly.
AI cash-flow forecasting projects a 30/60/90-day cash position by combining current bank balances, aged receivables weighted by historical collection probability per debtor, pipeline from your CRM weighted by close probability, and near-term payables including subscription renewals and open purchase orders. The forecast reads directly from TallyPrime, Zoho (Books + Inventory + CRM), ERPNext, or your mapped custom ERP, refreshes daily, and returns confidence intervals so the CFO can make a real decision — not a guess. Every projected figure drills back to the underlying vouchers, deals, or POs it was derived from.
Last updated 2026-07-02 · Part of the AI Copilot for ERPs early access
What this capability does
A 30/60/90-day cash position is only as good as the assumptions behind it. Most finance teams build the forecast in Excel by summing aged AR, applying a rule-of-thumb collection rate, subtracting known payables, and calling it done. The result is directionally right and rarely reliable enough to base a hiring or purchase decision on. AI cash-flow forecasting replaces the rule-of-thumb layer with grounded, per-debtor collection probabilities derived from your own historical data. The copilot reads current bank balances, aged AR from TallyPrime or Zoho Books or ERPNext, weights each debtor by their observed payment cadence over the last 6–12 months, adds pipeline from Zoho CRM (or any CRM you mapped) weighted by close probability, subtracts near-term payables from Bills / Purchase Invoices, and folds in open Purchase Orders and subscription renewals as commitments. The output is a 30/60/90-day forecast with confidence intervals — because the model knows how tight the historical patterns are per debtor, it can express uncertainty rather than hiding it behind a single point estimate. Every figure drills back to the underlying vouchers, deals, POs, or subscriptions it was derived from, so the CFO can pressure-test the forecast the way they would pressure-test a bank's treasury projection.
How it works
End to end, from question to grounded action.
Historical baseline per debtor
The copilot reads 6–12 months of Sales Invoice and Payment Entry history per debtor. It derives an observed collection cadence — median days to pay, variance, seasonality. Debtors with tight historical patterns get high-confidence weights; debtors with erratic patterns get wider confidence intervals rather than a single number.
Pipeline weighting
For customers with a CRM in scope (Zoho CRM or an integrated custom CRM), each open deal is weighted by close probability × your historical close rate for that stage × your historical AR days for that segment. Deals with weak stage-hygiene are weighted down explicitly, not silently.
Commitment folding
Near-term payables from Bills or Purchase Invoices, open Purchase Order commitments, subscription renewals from Zoho Subscriptions or ERPNext Subscription DocType, and tax outflows (GST payment, TDS remittance) are folded into the payables side of the projection.
Confidence-banded output
The final 30/60/90-day view is a range with confidence intervals, not a point estimate. Every underlying figure — expected collections from Debtor X, expected close of Deal Y — is drillable back to source. The forecast recalculates daily.
How this shows up on each ERP
Same capability, tuned to each ERP's data model.
AR read through the on-prem bridge; per-debtor cadence learned from voucher history. Multi-company Tally: each company can be forecasted separately or rolled up. GST and TDS outflow schedules read from the same Tally data.
This is where the copilot delivers its strongest forecast because Books + Inventory + CRM are all in scope. Pipeline weighting from CRM deals catches cash squeezes that a Books-only forecast misses — the pattern of a big deal expected to close alongside a large PO commitment.
Reads Sales Invoice, Payment Entry, Purchase Invoice, Purchase Order, and Subscription DocTypes through Frappe webhooks. Multi-company installations get per-Company forecasts respecting the built-in Company boundary.
Forecasts as good as the exported fields allow — AR aging, payment history, and payable dates at minimum. Adding pipeline (from any CRM export or a mapped middleware source) strengthens the forecast materially.
What this looks like in practice
Real question-and-answer patterns from pilot deployments.
“What is my 60-day cash position at 80% confidence?”
Between ₹1.82 Cr and ₹2.14 Cr. Drivers: expected collections ₹2.4 Cr (65% high-confidence, 35% probability-weighted), pipeline contribution ₹42 lakh (three deals at Contract stage), commitments ₹1.9 Cr including July GST outflow and two PO renewals. Drill into each row for the underlying source.
“If Vendor A pays 15 days late and Deal B closes on time, what does 30 days look like?”
Baseline shifts from ₹94 lakh (median) to ₹78 lakh (low band). The shortfall is driven almost entirely by Vendor A's ₹22 lakh outstanding — Deal B's contribution to the 30-day view is modest because the average AR days for their segment already pushes cash into the 45-day window.
“Show me the biggest single risk to my 90-day cash.”
Debtor "Rajarshi Industries Ltd" — outstanding ₹1.28 Cr, historical median payment 87 days, variance ±22 days. If they slip beyond 110 days the 90-day forecast drops by ₹1.28 Cr since their contribution is currently the largest single line in the collections model.
Why this matters
Per-debtor, not average
Weights reflect each debtor's actual payment history.
Confidence intervals
Uncertainty surfaces explicitly, not hidden behind averages.
Drillable to source
Every projected figure links to its underlying voucher or deal.
Refreshes daily
The forecast updates against fresh ERP data every morning.
Frequently asked questions
How is this different from the cash-flow report in Zoho Books or ERPNext?+
The built-in reports show historical cash flow and, at best, an aged AR extrapolated forward on a single collection rate. The AI Copilot forecast weights each debtor by their observed payment cadence, adds pipeline from CRM weighted by close probability, folds in commitments, and expresses uncertainty as a confidence band. It answers the CFO's actual question ("what could my cash look like in 60 days, worst reasonable case") rather than restating what has already happened.
How long does the baseline take to become reliable?+
The copilot needs 6–12 months of Payment Entry history to produce well-calibrated per-debtor weights. For customers with less history, the model uses segment-level fallback weights (Enterprise / Mid-Market / SMB) and marks the affected debtors as lower-confidence in the output. As history accumulates the per-debtor weights take over automatically.
What does the confidence interval actually represent?+
It is a range within which the forecasted cash position is expected to fall at the stated confidence level (default 80%). The width of the interval reflects the noise in your historical patterns — a business with tight, predictable collections gets a narrow band; a business with volatile debtor behaviour gets a wide band. This is the opposite of a spreadsheet that quietly hides its own uncertainty behind a single number.
Can I model what-if scenarios?+
Yes. Every input to the forecast is exposed as a scenario knob — "if Debtor X pays 15 days late", "if Deal Y closes on time", "if we delay a ₹40 lakh PO by 30 days". Scenarios recompute the forecast without touching the ERP. This is how pilot customers use the copilot before board meetings.
Does the forecast include GST and TDS outflows?+
Yes. GST payment schedules are derived from your filed and pending GSTR-3B liabilities, and TDS remittance dates from the corresponding vouchers. Both flow into the payables side of the projection. Statutory outflows are the largest and most predictable near-term cash events for most Indian businesses — a forecast that ignores them is not a forecast worth acting on.
What if I run multiple companies?+
Every company gets its own forecast respecting the ERP's Company boundary (Tally multi-company, ERPNext Company DocType, Zoho multi-org). A group roll-up view is available for CFOs who need the consolidated position across entities, with each company's contribution visible.
Related AI Copilot capabilities
Every capability composes with the others.
Available today on these ERPs
Each edition uses the same connectivity ERPPlugs already ships.
Want this capability running on your ERP?
Join the AI Copilot early-access pilot. We calibrate against your own data during the first 2–4 weeks and lock in early-adopter terms before general availability.
Join Early Access