Use cases
Teams join their most sensitive data sets and run AI on them inside a protected workspace. Approved results go out. The raw data stays in.
Move from BI in the hands of a few to AI in the hands of many, and build a true learning organization.
Each of these joins data sets that are too sensitive to hand to an outside AI service. With i4 Ops, the analysis runs where the data already lives.
Move from BI in the hands of a few to AI in the hands of many, and build a true learning organization.
Demand data showing where, when and what is selling, with supply data showing forecasts and outbound finished goods.
Run manufacturing more efficiently, with faster turns on raw materials and finished goods inventory.
Demand and sell-through data, with supply data from the factory planning forecast.
Keep high-value customers, rather than spending to find and onboard new ones again and again.
Granular demand data showing sales, customers and problems as they happen.
Send limited inventory where the need is greatest, even when that need shifts quickly, so what is on the shelf matches what customers want.
Large supply data, such as inbound inventory, cross-correlated with large demand data, such as sales transactions.
Reduce costly churn by finding the root cause of in-house events, with a dashboard that shows the business impact of supplier events.
Helpdesk data cross-correlated with demand data, such as sales transactions.
Make better informed decisions on spending across generation, transmission, distribution and consumption, and lower the cost of delivering reliable power.
Regional energy demand time series and geodata, cross-correlated with shifting populations and businesses.
The same protected workspace sits under each use case, so teams can work on the real data instead of a sanitized copy.
Sales, supply, helpdesk and operational data are joined on infrastructure you control, next to the people who analyze them.
Private models, notebooks and dashboards work on the full data set without sending it to someone else's cloud. More on sovereign AI.
Forecasts, reports and dashboards leave with two-person approval. Attempts to copy, upload or paste the raw data out are stopped and reported.
These are examples. If your teams need to analyze data that should not leave your organization, bring the use case to a demo call and we will walk through how it would run.
No. Models run on infrastructure you control, inside the protected workspace. Public AI chatbots are blocked by default, like any other route out.
Through approved exports. A forecast or report leaves once a second person approves it, and each export is recorded in a tamper-evident audit chain.
Thirty minutes. Tell us the data you need to put to work, and we show you how it runs inside a protected workspace.