Treasury cash forecasting that reduces the idle cash you carry.
Build explainable short-term cash forecasts, surface uncertainty and drivers, and let treasury approve scenarios and funding actions.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Cash forecasts combine bank data, AR/AP timing, payroll, debt, and business estimates in fragile spreadsheets; one-point forecasts hide uncertainty.
Treasury analysts, FP&A, controllers, liquidity managers
Trigger to act: Liquidity buffers are costly, forecast misses are material, acquisitions add accounts/entities, or covenant/funding decisions need better daily visibility.
The result you can model before you sign.
Illustrative only: $20 million average liquidity buffer × 10 basis-point annual carrying-cost reduction = $20,000 annual opportunity. Funding decisions require treasury validation.
The outcome, plainly: Build explainable short-term cash forecasts, surface uncertainty and drivers, and let treasury approve scenarios and funding actions.
Inputs in. A cited, review-ready result out. Your expert decides.
A Predictive model + decision copilot. Every material fact is grounded in an allowed source and returned with its identifier, no invented data.
Warehouse-native statistical or ML model (for example XGBoost, LightGBM, Prophet or a task-specific model); GPT-5.4 mini, Gemini 2.5 Flash or Claude Haiku 4.5 for explanations and planner interaction. Feature pipeline and model registry; Back-testing and drift monitoring.
- Cash balances
- Expected receipts/payments
- Invoice/payment behavior
- Payroll/tax/debt dates
- Intercompany flows
- Scenario assumptions
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Treasury approves every funding, investment, borrowing, transfer, hedge, or liquidity decision; uncertainty remains visible.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
Monthly retraining, warehouse compute, feature pipelines and 5,000 to 50,000 scored entities.
Planning assumptions, not vendor quotations. Your Bank APIs and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Treasury approves every funding, investment, borrowing, transfer, hedge, or liquidity decision; uncertainty remains visible. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No autonomous transfer, investment, borrowing, or hedge; no forecast guarantee; one-off events and management discretion remain explicit assumptions.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $1,150–8,400/month (monthly retraining, warehouse compute, feature pipelines and 5,000–50,000 scored entities), plus a $3,400/month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Kyriba?
Tools like Kyriba, Trovata, GTreasury are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it’s live, and how do we prove it works?
This is a specialist opportunity. We baseline “Forecast error by horizon” first, then measure against that baseline. You see the scorecard before expanding scope, the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work