Target environment

Use data and AI where quality, inventory, demand, and supplier work meet.

For smaller manufacturers and distributors that need practical analytics, document automation, forecasting, and controlled AI without replacing the core ERP or quality system.

Discuss this operating problem Explore solutions

Potential use cases

Start with one measurable workflow.

The first project should be bounded enough to deliver, evaluate, and operate without a broad system replacement.

Demand and inventory forecasting

Supplier document workflows

Margin and cost analytics

Operational early warning

Worked example

How much useful life remains, and how uncertain is that estimate?

Open-data model demonstrationNASA C-MAPSS FD001 simulated turbofan run-to-failure data.

Open source information

Source, licence and limits

SourcesEngine sensor tracesRun-to-failure cycles · 21 sensors

NigatForecast and decision modelBaseline · uncertainty · monitoring

OutputDecision rangeSchedule on the lower bound, not the estimate

Held-out engine Operating cycle — Unexpected-failure cost

Predicted useful life…Cycles remaining, capped at 125

90% interval…Calibrated on separate engines

Decision state…Uses the lower bound, not the point estimate

Held-out test MAE…Engine-level split

Remaining useful life and uncertainty band cycle — of —

90% intervalPredicted Benchmark actual

Driver proxies at this cycle

Test RMSE: …
Nominal interval: 90%
Measured coverage: …
Held-out engines: …

Open the full demonstration

Start 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.

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