A model for the decision you make
Developed for the risk you actually take. Measured on your history, including the cases that went wrong. If another model does the job better, we switch, and you can take the comparison to the business.
Finance teams already have models. The work we take is the application people use, scored on their own book, with a log when someone asks why a number moved.
We start with the result the business needs. Then we develop the model for that task and ship the software around the review people already do.
Developed for the risk you actually take. Measured on your history, including the cases that went wrong. If another model does the job better, we switch, and you can take the comparison to the business.
Forecasting that lives in the application, not in a notebook. The inputs are yours. The result can be explained when the forecast and the actuals diverge.
Exceptions, packages, the questions an analyst asks every week. Each call is logged. A person stays accountable when the agent would change something it should not.
Development Data Analytics does this for construction draws: budget, covenants, lien waivers, retainage, and risk flags. More than 8 million square feet. SOC 2. That is the shape of a finance application we will put in production.
We score the model on your book, not a sample set from somewhere else. The misses count. A better model replaces it when the misses say so.
A forecast can inform a person. An agent that posts, approves, or sends needs a boundary, and a named person past that boundary.
Every model call can be explained. The input, the output, and who was accountable. Same standard as the regulated work.
The book, the documents, and the model artifacts stay with you. We do not use them to train a product for someone else.
AI for construction finance. It reads a draw package as it arrives and runs the review: budget, covenants, lien waivers, retainage, risk flags. More than 8 million square feet. SOC 2.
developmentdataanalytics.com RelatedWhen the same application has to hold up in an audit. Policy in the path, a named person, and a record produced as the system runs.
Regulated workYes. The application keeps the input, the output, and the person accountable. When someone in risk or audit asks why a result moved, that is the record they get.
We develop and measure the model on your data, for your task. The data and the model artifacts stay with you. They are not training material for someone else's product.
An application. Screens, an agent where the job needs one, and the integrations into the systems you already run. Your team has to be able to operate it when we leave.
Regulated industry, manufacturing, and e-commerce.
We'll tell you what we'd develop on your data, what the application has to log, and what has to be governed before it goes live.