One desktop app for spatial data, classical ML, and deep learning — instead of stitching QGIS, Python notebooks, and a cloud GPU together. Built for researchers, engineers, and labs that need full control over their data, models, and experiments.
Spatial layers
Model training
Evaluation & metrics
Sensitive rasters, field surveys, and client datasets never leave the machine — no upload, no third-party storage, no review cycle with IT.
Training a CNN for eight hours costs you electricity, not GPU-hours. No per-seat cloud fees for your lab or your students.
Air-gapped networks, field laptops, restricted university environments — AIMU runs wherever Windows runs, online or offline.
Four things AIMU does that fragmented stacks struggle with — integrated GIS + AI, offline execution, reproducible projects, and honest scope.
Prepare spatial data, engineer features, train ML and DL models, and review results in one desktop app — no gluing tools together.
Multi-raster sampling, shapefile support, coordinate systems, and interactive mapping sit alongside Random Forest, SVM, CNN, LSTM, GRU, and Time-Series Transformers.
AIMU is a workbench, not a black box. Preprocessing, training, and evaluation are inspectable; you choose every model parameter, and every run is saved with its config.
Preprocessing, model configuration, and evaluation are saved with each project, so you can reproduce runs, compare models, and hand work off.
Running spatial modeling for environment, earth observation, hydrology, agriculture, or geoscience — and wanting one tool instead of five.
Building ML and DL models on geospatial or tabular data without handing datasets to a third-party cloud.
Standardizing a local, reproducible AI pipeline across students and staff — without cloud lock-in or recurring per-seat cloud costs.
A typical AIMU project moves through five stages — load, preprocess, train, compare, evaluate — all saved in one reproducible project file.
Import rasters, shapefiles, and tabular datasets; browse coordinate systems and spatial layers.
Handle missing data, normalize, transform, and engineer features ready for spatial ML.
Random Forest, SVM, and other classic ML, plus CNN, LSTM, GRU, and Time-Series Transformers.
Run and compare experiments side by side — with tuning, clear metrics, and reproducible project state so you can justify every model choice.
Inspect results, export outputs, and reload saved models to run predictions on new data.
Your data stays on your hardware. No silent telemetry of project contents, no remote training queue, no vendor lock-in.
Runs on your Windows machine, online or offline. Built to sit next to your data.
Projects capture preprocessing, model configuration, and evaluation together.
Student, Standard, and Team/Lab plans. License-based, no usage meters.
Quick answers to the things customers ask most.
No. AIMU runs entirely on your local hardware. You pay only for the license; there are no hidden costs for training time or data storage.
Yes. You can deactivate AIMU on one machine and reactivate it on another (e.g., upgrading your lab laptop).
No functional restrictions. The Student license includes the full workbench feature set but is legally restricted to non-commercial, academic use.
Yes, for Team/Lab licenses. Please contact our sales team to arrange invoice billing and university procurement.
Install AIMU on your Windows machine in under five minutes. Try it on your own data, offline — then pick a plan when you’re ready.