GeoAI & deep learning on imagery
Feature extraction, building and road detection, land cover classification and change detection – trained and validated on your own imagery, not a benchmark dataset.
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Innovation & artificial intelligence research
AI work that starts with an evaluation plan rather than a demo – including GeoAI, where the training data is imagery and the ground truth is a survey.
The problem with most AI programmes
Demos are easy. Systems you can govern are not.
It takes very little to produce something impressive in a workshop. What separates that from a system you can put in front of customers, auditors or a regulator is evaluation, data lineage, oversight design and an operating model.
We work the unglamorous half first. Before writing a line of model code we agree what “good” means numerically, how it will be measured, on which held-out data, and what result would cause us to recommend stopping.
Our evaluation-first sequence
1. Define the decision
What action changes because of this system, and what error would it be unacceptable to make?
2. Build the evaluation set
Held-out, representative, and agreed with you before modelling begins.
3. Establish a baseline
Often a simple rule or existing process. A model that cannot beat it does not ship.
4. Test what could go wrong
Robustness, bias, drift and failure modes – documented, not assumed away.
5. Hand over the harness
Your team inherits the evaluation suite, not just the model.
Capabilities
What we are asked for most
AI readiness & strategy
An honest assessment of where AI would pay for itself and where it would not – scored against data availability, process maturity and regulatory exposure.
Machine learning R&D
Problem framing, feature and data design, model development and rigorous evaluation. We publish the evaluation protocol before we report the numbers.
Generative AI & LLM solution design
Retrieval architectures, prompt and tool design, grounding strategy, evaluation harnesses and the cost model – built to survive a model change.
Computer vision & sensor analytics
Inspection, monitoring and detection systems for industrial and environmental settings, including edge deployment and drift monitoring.
Responsible AI governance & assurance
Risk classification, model documentation, bias and robustness testing, human-oversight design and audit trails a regulator would accept.
Responsible AI
Governance is not a slide at the end
Every AI engagement carries an assurance workstream from day one. These are the artefacts you get, whether or not you asked for them.
Model & system cards
Purpose, training data provenance, known limits and out-of-scope uses, written for a non-specialist reader.
Evaluation dossier
The test sets, metrics, thresholds and results – including the cases where performance was poor.
Human oversight design
Where a person must be in the loop, what they see, and what authority they have to override.
Monitoring & drift plan
What is watched in production, at what cadence, and the trigger conditions for retraining or rollback.
Start a conversation
Have a geospatial problem, a research question, or a technology decision to get right?
Tell us what you are trying to find out. We come back within two working days with an honest view of whether we can help, how long it would take and what it would cost – before you commit to anything.