Many cattle operations can only answer that once the books are reconciled, long after the operational decisions that shaped the result were made. AgriFinTech Pro is being built to answer it by lot, by production cycle, while the cattle are still on feed, by linking animal records and financial transactions in one data model.
Architecture described in USPTO provisional patent application 63/996,594, “Integrated Financial and Livestock Inventory Management System with Predictive Analytics for Cattle Operations.” A provisional application is a filing, not an examined or granted patent.
Assumes 1.5% death loss, $18 per head in health and processing cost, and $0.06 per head per day in allocated labor. In the platform these inputs come from the operation's own records instead of sliders.
Cattle and calves were the largest single source of U.S. farm cash receipts in 2024, at $112.1 billion. What that figure does not show is how the information behind it is kept. In cattle operations, animal inventory, weights and health events may be maintained in one set of tools, while payroll, payables and cost accounting are maintained in others, creating reconciliation and visibility challenges. That separation is the premise of this project and of the patent application behind it.
The consequence is not only wasted hours. It is that the link between an individual animal or lot and the money it consumed and produced gets broken. Feed is allocated after the fact. Labor lands in a general account instead of a lot. A manager who wants to know whether to hold or sell has a number that is weeks old, and a lender who wants to underwrite the operation gets financial statements that describe the business in aggregate but cannot show performance by production cycle.
Source: USDA Economic Research Service, cash receipts by commodity, calendar year 2024.
The layers below describe the intended system as set out in the patent application, not software already in production. The demonstration interfaces build out part of the top layer; the rest is design. The architecture is deliberately vendor-neutral. The ingestion layer accepts data through REST interfaces, file imports, message queues and manual entry, so an operation is not required to replace the herd, scale or accounting software it already runs.
Separate views for the operator, the credit analyst and the portfolio manager, plus programmatic access for external systems.
Configurable thresholds on mortality, cost coverage and production trends generate flags and recommended actions rather than static reports.
Performance indicators at animal, lot, cycle and operation level; forecasting of production and cash flow; risk indicators with the drivers that produced them.
The core of the design. Every financial transaction can be associated with the animals, lots and production cycles it belongs to, so cost per head and margin per lot are derived rather than estimated.
Connectors that validate, clean and standardize incoming records from herd management tools, scales, payroll and accounting systems.
The screens below are a working mock-up built for demonstration. The operation shown, Sundown Creek Cattle Co., does not exist, and all values are generated for illustration.
| Lot | Head | Days on feed | Cost/head | Margin/head | Lot margin |
|---|---|---|---|---|---|
| 1A · Holstein steers | 1,240 | 168 | $1,612 | $291 | $360,840 |
| 2C · Beef-on-dairy | 860 | 142 | $1,489 | $334 | $287,240 |
| 3F · Holstein steers | 1,510 | 96 | $1,205 | $118 | $178,180 |
| 4B · Beef-on-dairy | 720 | 211 | $1,844 | −$47 | −$33,840 |
Lot 4B is the point of the view. It has been on feed 40% longer than plan at a below-target gain, and in an aggregated income statement it would be invisible until the cattle were sold.
Open the full demonstration — producer dashboard, credit analyst dashboard and the standardized risk report, built on FIG. 5 and FIG. 6 of the patent application.
An operation that can show performance by cycle can be underwritten on something other than a year-end statement. This view assembles the same underlying records into projected cash flow and a risk indicator, with the drivers behind it shown rather than hidden.
| Cash-flow coverage | Favorable |
| Production volatility, 12 mo. | Low |
| Mortality vs. rolling average | Elevated |
| Days on feed vs. plan | Elevated |
| Payment history | Current |
A score without its drivers is not useful to an analyst who has to defend the decision. The design treats explainability as a requirement, not a feature.
AgriFinTech Pro is developed by Karen Ivonne González Martínez, a Contadora Pública (Mexican public accountant) with more than fifteen years in accounting and financial systems, based in Syracuse, Kansas. Since 2019 she has worked as an Accounting & Payroll Compliance Specialist inside a large-scale Kansas cattle operation, responsible for multistate payroll, accounts payable, bank reconciliations and compliance. She implemented Stampli as an accounts-payable automation platform and later managed the transition to MakersHub, while also taking part in coordinating the organization’s migration to a multistate payroll platform.
Earlier she worked as a business analyst on the modernization of financial and administrative systems at the Institute of Engineering of the National Autonomous University of Mexico, documenting accounting processes for an ERP implementation, and managed research project accounting at the Instituto Mora under Mexican public-sector accounting standards.
She is not a U.S. Certified Public Accountant and does not offer attest, audit or tax services in the United States.
An interactive lot-profitability calculator that computes from user-entered assumptions, together with demonstration interfaces for the producer and credit-analyst views, has been built. The underlying architecture is described in U.S. provisional patent application 63/996,594. A manuscript on financial management, digital tools and strategic decision-making for U.S. livestock operations has been drafted and is being prepared for publication in 2026. Two livestock businesses have expressed written interest in a future pilot. No commercial deployment has taken place, no predictive model has been trained or validated, and the system is not connected to the records of any operation.