Decision models
Forecast what matters. Show the uncertainty. Connect the result to action.
Nigat builds practical forecasting, scoring, optimization, and anomaly-detection systems only when a model improves a real decision.
The operating problem
Staffing, inventory, cash, sales, routing, and customer decisions rely on intuition because the data is fragmented or the current forecast is not trusted.
The first goal is not a platform. It is a reliable path from the current evidence to a better decision or completed workflow.
What changes
- A measurable baseline before model development
- Transparent model selection and error analysis
- Confidence ranges and human override
- Monitoring that shows whether the model remains useful
Use cases
Where this capability earns the right to stay.
Examples are illustrative and must be validated against the customer’s systems, data, and economics.
Lead, customer, risk, and priority scoring
Inventory, schedule, route, and capacity optimization
Operational early warning and change detection
Delivery path
From current state to an operated system.
- 01
Define the decision and error cost
- 02
Create a simple baseline
- 03
Test candidate models
- 04
Integrate the action
- 05
Monitor drift and value
Engagement limits
Four limits apply to every Nigat engagement regardless of capability, and they are stated once rather than restated on each service page.
Read the full boundariesStart with the operating problem
Show us where the work slows down.
You do not need a polished technical brief. Describe the decision, handoff, report, document workflow, or AI initiative that is harder than it should be.