Practical machine learning for spatial problems — applied where it actually moves the needle, not where it sounds impressive.
The volume and complexity of geospatial data has outpaced what teams can analyse manually. We help organisations apply AI and ML to the specific decisions where it makes a measurable difference — imagery classification, change detection, predictive modelling, natural-language access to spatial data, ML-driven validation.
If you have a problem you suspect ML could help with — even if you're not certain — talk to us. We'll tell you honestly whether it's a good ML fit, or whether a SQL query, a rule, or a person with a checklist would do the job better.
That conversation is free. The cost of not having it tends to be a six-figure proof-of-concept that didn't need to exist.
If you've got something in one of these shapes, we're a good fit. If it's somewhere else, get in touch anyway and we'll be straight with you.
Sentinel-2, aerial, drone. Per-parcel or per-pixel. Land-cover, crop, structural change. Outputs as versioned GeoPackages with confidence scores.
Yield, soil properties, anomalies, demand. Tabular and spatial features. Where the right answer is a number with an uncertainty band attached.
Letting non-GIS staff ask questions of a spatial database in their own words. Useful when the analyst bottleneck is the problem.
Models that flag suspect records as they come in. Catches what a hand-written rule misses — odd GPS jumps, inconsistent labelling, photos that don't match the form.
The fastest way to waste money on AI is to start with the technology. Our process inverts that.
The ones we get most often, answered straight.
No — we're a geospatial consultancy. Applied AI is one of five linked solutions we deliver, and a current growth area. We get called when an organisation has a specific spatial decision that ML could move the needle on, not when they want an AI strategy deck.
It means we start with a decision an organisation has to make, work backwards to the data and the model, and ship a pipeline that produces an answer the organisation can act on. Not a research project. Not a chatbot demo.
Whatever fits the problem. Esri GeoAI when the work sits inside an ArcGIS estate; PyTorch and scikit-learn for everything else; classical methods when ML would be overkill. We don't pick the trend, we pick the tool.
No. The models, training data, weights and pipelines are yours. The handover is a documented repo with reproducible training and inference. If you want a different vendor to extend it later, they can.
Yes. We are happy to spend a discovery call talking you out of using ML if a SQL query or a rules engine would do the job. That conversation usually saves more money than the eventual project.
Yours isn't here? Ask us directly.
We will tell you straight whether ML is the answer here, or whether something simpler does the job.
