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NVIDIA Nemotron analyzes forecast evidence to explain cash risk, uncertainty, questions, and recommended actions.
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30/60/90-day cash forecast showing confirmed and scenario balances after human-reviewed financial data.
Inspiration
Small businesses often have financial data spread across invoices, payment notices, and other systems, but knowing what has already happened is not enough.
What matters is understanding what may happen to cash over the next 30, 60, and 90 days — and having enough time to act before a cash problem becomes urgent.
CashOps Agent was built to turn fragmented financial events into a human-reviewed cash-flow forecast and an explainable AI risk assessment.
What it does
CashOps Agent retrieves invoice data from PayPal Sandbox and converts it into structured cash-flow events.
The workflow is:
Financial Data → Human Review → 30/60/90-Day Forecast → AI Risk Review
CashOps Agent:
- structures invoice data into cash-flow events
- separates confirmed cash from draft invoice scenarios
- detects missing or uncertain information
- asks for human confirmation instead of guessing
- calculates deterministic 30 / 60 / 90-day cash forecasts
- sends the resulting forecast and supporting evidence to NVIDIA Nemotron
- returns an explainable AI review of risk, uncertainty, questions, and recommended actions
Human-in-the-loop design
Financial AI should not invent missing values or calculate balances from assumptions.
If an invoice is missing an expected due date, CashOps Agent flags it for Human Review.
Only after the user confirms the missing information does the scenario forecast update.
The original source data is not overwritten.
This keeps financial calculations deterministic while still allowing AI to help interpret the resulting evidence.
NVIDIA Nemotron + Nebius Token Factory
The AI Risk Review uses NVIDIA Nemotron-3.5-Lightning through Nebius Token Factory.
Nemotron receives structured evidence generated by CashOps Agent, including:
- opening cash balance
- confirmed cash events
- draft invoice scenarios
- human-confirmed information
- 30 / 60 / 90-day forecast results
- unresolved uncertainty
The model then returns:
- cash risk level
- data confidence
- AI summary
- identified issues
- questions for the user
- recommended actions
The AI does not calculate the underlying cash balances.
Those calculations are performed deterministically by CashOps Agent before the evidence is sent to the model.
Why this architecture matters
Large language models are useful at interpreting uncertainty, but financial numbers require reliability.
CashOps Agent therefore separates two responsibilities:
Deterministic layer Calculates the financial forecast.
AI reasoning layer Explains what the forecast means and what the user may need to investigate next.
This reduces the risk of an AI model inventing financial values while still using its reasoning capability where it is most useful.
How we built it
CashOps Agent is built with Python and Streamlit.
PayPal Sandbox provides synthetic invoice source data.
The application transforms those invoices into structured cash-flow events and calculates confirmed and scenario forecasts.
Human Review handles incomplete financial information.
The AI layer uses NVIDIA Nemotron through the Nebius Token Factory OpenAI-compatible API.
The application is deployed as a public Streamlit web app.
Challenges
One major challenge was deciding where AI should — and should not — be used.
Allowing an AI model to directly calculate financial balances would make the result difficult to verify.
We solved this by keeping all financial calculations deterministic and giving Nemotron only established evidence to interpret.
Another challenge was handling incomplete financial information.
Instead of allowing the system to guess missing dates, CashOps Agent requests human confirmation and records the resulting impact on the forecast.
What we learned
The most useful AI agent does not necessarily automate every decision.
For financial operations, combining deterministic calculations, human confirmation, and AI interpretation can provide a safer and more explainable workflow.
AI becomes most useful after reliable evidence has already been established.
What's next
Future versions could support:
- additional financial data sources
- accounting-system integrations
- cash-outflow events
- automated reconciliation
- multi-currency conversion
- recurring AI cash-risk monitoring
The long-term goal is to build a continuously updated cash-operations agent that gives small-business owners more time to understand risk and act.
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