Getting models out of notebooks and into the systems that run the business, including replacing the legacy code and spreadsheets they inherit.
We put your models into the systems that run your business, which is a different problem from building them. We write the integration code that connects a model to the platform it has to serve, such as an origination system or a pricing engine, make it run at the volume and speed that platform demands, and put the monitoring and the rollback route in place before anything goes live. A model that only runs in a notebook decides nothing.
We move models and analytics off legacy platforms and languages, such as SAS to Python, evidencing line by line that the new code reproduces the original figures. We rebuild what we move so it runs faster and runs unattended, which is often the reason the migration was worth doing at all.
We replace the end user computing (EUC) your reported results depend on, such as the spreadsheets and desktop tools that one person built and the whole business now relies on. We rebuild them from scratch as tested, version-controlled code that reproduces the original figures exactly, with the calculation documented and ownership no longer resting on whoever wrote the file.
A model that only runs in a notebook decides nothing. At Gini we write the integration code into the platform that has to serve it, at the volume and latency that system demands, then build the monitoring that keeps it honest: population stability, performance against outcomes as they emerge, back-testing on a schedule, and champion-challenger tracking across portfolios.
Gini measures input drift, score distribution shift and realised performance against prediction, and reports them to the owner named in your model inventory rather than to a shared inbox. Thresholds are set with the owner so an alert means something has to be decided, and the pack that goes to your model committee is generated from the same run rather than assembled by hand.
Yes, and it is usually the first thing worth automating, because back-testing done annually by hand tells you about a model twelve months after it started drifting. At Gini we schedule it, hold the results as a history rather than a single current view, and flag the point at which performance breaches the tolerance the model was approved against.
Yes. Gini takes a model as it stands, reproduces its figures to confirm we have understood it, then writes the production implementation and the tests that prove the deployed version agrees with the original. Where the model as written cannot meet the latency the system needs, we tell you what would have to change before rebuilding anything.
