Four predictive models, one source of data.
Each model reads from your Tally ledgers and turns history into a forecast you can act on, with a recommended next move rather than just a chart.
See demand before it lands
Forecasts demand, optimises inventory and flags supplier risk, so you stop holding dead stock and stop running out of the lines that sell.
AI/ML: Holt-Winters time-series forecasting for seasonal demand; a statistical safety-stock model, with a separate method for genuinely intermittent, spare-parts-style items, for reorder points.
Know the cash gap early
Shows where working capital is trapped, flags when a cash gap will open, and surfaces the ledger transactions that warrant a second look, before they show up in your bank balance.
AI/ML: anomaly detection (isolation forest) scans every ledger transaction for entries that don't match your normal pattern.
Forecast the order book
Sales forecasting, cash-conversion tracking and customer default-risk scoring, so you know where the order book is heading and which accounts to chase first.
AI/ML: a trained credit-risk model scores each customer's probability of late payment or default from payment-history patterns, not just days overdue.
Spot risky accounts in time
Concentration-risk analysis and default-risk scoring that show which accounts are a concentration or payment risk while there's still time to act.
AI/ML: shares the same trained credit-risk model as Revenue Foresight, for consistent default-risk scoring across both.
Not a dashboard. A team of agents.
An MCP-based architecture where specialised agents read your data and coordinate to recommend the next action. It is the engine under every Foresight™ model and every prebuilt scenario forecast.
Forecasts are only as good as the books behind them.
Tally books at growing manufacturers are rarely spotless. Before any forecast or scenario runs, we check whether your data can be trusted, and tell you plainly when it can't.
- 01Profile
Find gaps, duplicates and outliers across ledgers, stock and receivables.
- 02Validate
Check entries against accounting rules, such as stock that doesn't reconcile or suspense balances that never clear.
- 03Reconcile
Match your books against GST returns, bank statements and stock records.
- 04Score and fix
A trust score for each dataset, and a fix list your accountant can work through.
Weak data widens the range on every forecast and scenario, instead of hiding behind a precise-looking number.
The right technique for each decision.
Each model uses the method suited to its own data pattern: time-series smoothing for demand, safety-stock arithmetic for reorder points, unsupervised anomaly detection for ledgers, supervised classification for credit risk, and plain, auditable arithmetic for working capital and scenario forecasts. Methods, data sources and limitations are documented in our AI Model Card.
Find out what your Tally data already knows.
Start with the free AI-readiness assessment, or book a 30-minute discovery call to see a forecast built on your own numbers.