GIS development
Microsoft Azure Devops for application development with the ArcGIS Maps SDK for .NET and the ArcGIS API for , experience builder widgets, custom web apps, for full client transparency and development visibility.
Geospatial · Development · Data
Empower your GIS.
Custom GIS development for small and medium businesses worldwide using the Esri stack with C#, JavaScript, and Python. We also extend ArcGIS Experience Builder and deliver advanced, complex FME Flow workbenches.
ESRI STACK SPECIALISTS
PRODUCTION-READY GIS DEV
1 BUSINESS DAY RESPONSE
Capabilities
Services & apps
From field data capture and automated ETL to bespoke web GIS apps, Experience Builder extensions, and enterprise APIs, we design solutions that match your geospatial workflows.
Scripting
Databricks, Microsoft Fabric notebooks, FME Form, ArcGIS Pro notebooks. Use Python to extend FME Flow or build complex GIS analysis processes.
Integration
Connect bespoke Esri apps to Azure Blob Storage, SharePoint, Microsoft Fabric, Dataverse, and Entra ID. Thus enabling full integration between ESRI and Microsoft.
Support
Already running custom code but short on in-house capacity? We support C#, Python, and Arcade. Depending on the version, PHP where used against Geoserver for example.
Why work with omnigis
Practical GIS delivery with measurable outcomes
Security, scalability, and interoperability—so your applications and data stay trustworthy end to end.
Enterprise-ready
Secure-by-design delivery with SSO, role-based access, and auditability through Azure Entra ID and standard enterprise controls.
Esri standards
We build on ArcGIS Pro, ArcGIS Enterprise, and ArcGIS Online using Esri best practice, then extend with custom apps and workflow automation.
Our stack
GIS-first engineering with C#/.NET, Python, JavaScript, and ETL, supported by Azure DevOps, Fabric, Power Automate, Entra ID, and SharePoint.
Esri & Microsoft
We integrate Esri platforms with Microsoft services your teams already use, creating connected workflows from data capture to operational reporting.
Consultation Funnel
How we work
A structured, standard delivery approach from first contact to production rollout. Each stage has clear deliverables and expected outcomes.
Step 01
Initial contact
We capture goals, current pain points, stakeholder groups, and timelines in a short discovery call.
Expected outcome: shared problem statement and clear next action.
Step 02
Scoping and architecture
We define data flows, security model, integration points, and delivery scope using proven GIS patterns.
Expected outcome: agreed technical plan, milestones, and estimate.
Step 03
Iterative build
Features are delivered in short cycles with visible demos, feedback loops, and transparent backlog tracking.
Expected outcome: validated functionality with reduced delivery risk.
Step 04
Testing and hardening
We complete functional checks, performance tuning, and user acceptance activities before go-live.
Expected outcome: release-ready solution with known quality baseline.
Step 05
Deployment and handover
We deploy to your target environment, hand over documentation, and provide guided onboarding.
Expected outcome: operational product with confident user adoption.
Step 06
Continuous improvement
Post-launch support, enhancement planning, and roadmap updates keep the platform aligned to business change.
Expected outcome: sustained value and a scalable GIS capability.
Results
Typical outcomes we target
Indicative outcomes based on standard GIS delivery patterns. Final results depend on data quality, process maturity, and deployment context.
Context: Utility
Inspection workflow digitised
Replaced paper and fragmented forms with a structured field-to-office GIS workflow.
Expected outcome: around 20% fewer incomplete submissions.
Context: Local authority
Routing + map automation
Connected operational data to automated routing and map-first decision support.
Expected outcome: up to 28% operational efficiency gain.
Context: Property
GIS cloud migration
Modernised deployment architecture for resilience, observability, and scalable delivery.
Expected outcome: support for 99.9% uptime target.
Machine learning
GIS that leverages ML for imagery and analytics
Machine learning extends GIS from maps and layers into repeatable prediction and monitoring—especially for remote sensing, imagery, and large raster workloads.
AI in GIS: modern AI can accelerate labelling, search, and workflow assistance, while your GIS stays the place for authoritative spatial data, provenance, and map-based decisions.
We build applications that put machine learning to work in real geospatial workflows. Our ML pieces are implemented in Python—from training and batch inference to APIs and services that plug into maps, dashboards, and your existing stack.
Whether you need a focused classifier, a processing pipeline, or a full app around models and GIS data, we can design and deliver it with Python at the core.
How GIS can use ML
- Land cover & land use — supervised classification from multispectral or aerial imagery to update maps and statistics.
- Object detection — find buildings, roads, vegetation, or infrastructure in imagery for monitoring and asset inventories.
- Semantic segmentation — pixel-level labels for parcels, water, canopy, or impervious surfaces tied to GIS geometry.
- Change detection — compare image time series to flag new development, vegetation loss, or damage after events.
- Remote sensing indices — feed NDVI, moisture, or thermal features into models for agriculture, environment, and risk.
- Training data from GIS — use existing layers, labels, and field surveys to build and validate ML datasets responsibly.
- Model outputs in the map — serve predictions as map layers, pop-ups, and dashboards next to operational GIS services.
- Raster analytics pipelines — scale preprocessing, tiling, and inference alongside your existing ETL and cloud storage.
- Spatial validation — stratified sampling, confusion metrics, and ground-truth workflows grounded in map context.
- Governance & trust — version models, document data lineage, and control who can publish ML-derived layers to production maps.
Contact
Start a conversation
Share your requirement, data sources, timelines, and who needs to be involved—we’ll reply with clear next steps.
- Location
- UK-based · remote-first
- Response
- Within one business day