How teams put SpatialTechSolutions to work

Challenge: Estimates were rebuilt by hand each cycle from exported spreadsheets. Nobody could say with confidence which variogram, search neighbourhood, or composite length produced last quarter's tonnage figure, which made technical review slow and due diligence uncomfortable.

Approach: Ran the chain from the governed project database instead of exports — desurveying logged holes, compositing assays, kriging a block model, and simulating realizations — with every result content-addressed and its manifest recording the kernel, inputs, parameters, actor, and toolchain pins.

Challenge: Water-level, EC, TDS, and pressure readings were spread across loggers, spreadsheets, and field notes, making it slow to spot anomalies or prepare regulatory reporting.

Approach: Consolidated well records and time-series data in the Data Hub, connected IoT logger imports, and put monitoring on a map-first dashboard with AI-assisted trend and anomaly review.

Challenge: Routine spatial questions — buffers, overlays, filtered views, map exports — created a backlog because only a few specialists could run them end to end.

Approach: Deployed Plato GIS as a browser-based workspace so analysts could ask for spatial work in plain English, with agents calling governed tools and every step reviewable.

Challenge: Large areas of interest made manual imagery inspection slow, and imagery analysis lived in tools disconnected from the operational map.

Approach: Used Sentinel imagery, historical imagery, and change detection inside the same GIS workspace to focus review on areas where surface conditions actually changed.