Six connected practices covering strategy, build, automation, spatial intelligence and cloud operations.
Conceptual visualization only
Design, build and deploy production machine learning, generative AI and computer vision systems.
Turn operational data into decisions with rigorous analysis, dashboards and predictive models.
Assess readiness, prioritise use cases and build a defensible AI roadmap with governance built in.
Automation, APIs and data platforms engineered in Python with tested, observable pipelines.
Earth observation, remote sensing and Web GIS delivered with open Python geospatial stacks.
Multi-cloud AI platforms on AWS, Azure and Google Cloud with CI/CD, IaC and cost control.
Sector pages set out the operational problem, concrete use cases, the data involved and where a human signs off.
Engineering and design offices
Design and engineering teams lose time to repetitive document checks, inconsistent specification compliance and manual data extraction from drawings and reports.
View sector pagePublic sector and regulators
Public bodies handle high volumes of applications, submissions and records where consistency, traceability and defensibility matter more than speed alone.
View sector pageUtilities, transport and asset owners
Asset owners hold decades of records across GIS, maintenance systems, surveys and documents that are rarely usable together.
View sector pageAgribusiness and land management
Agricultural operations collect field boundaries, machinery logs and imagery-derived indices, but decisions are still made from spreadsheets.
View sector pageEnvironment, climate and disaster risk
Environmental and disaster-risk work depends on heterogeneous spatial data and must state its own uncertainty to be usable.
View sector pageShare your objective and constraints. A specialist reviews every request before any recommendation is issued.
