Predictive models ignore proximity, movement, terrain, networks, or spatial dependence.
Production GeoAI Engineering & Spatial MLOps
Production GeoAI Engineering & Enterprise Spatial Intelligence Systems
We engineer production-grade GeoAI and spatial intelligence systems—from satellite data and spatial infrastructure to custom AI models, Spatial MLOps, APIs, and operational applications.
// The buying trigger
You probably need this when…
GIS and data-science teams produce separate outputs that never become one workflow.
A promising map or notebook has no production architecture, monitoring, or owner.
// Concrete scope
What the engagement delivers
Every item is tied to an acceptance owner and evidence. Final scope is confirmed after data, security, and integration review.
Spatial data and model feasibility assessment
Use-case definition, success metrics, and risk register
GeoAI reference architecture and integration plan
Prioritized roadmap with investment range
Optional production model, API, application, and monitoring stack
// Delivery process
From evidence to operational handover
- 01
Frame the decision
Define the user, operational decision, baseline, and measurable value.
- 02
Test the spatial signal
Audit coverage, resolution, labels, leakage, and geographic bias.
- 03
Design the system
Select the model, spatial stack, deployment boundary, and review points.
- 04
Prove and ship
Benchmark, deploy, document, and transfer ownership.
// Proof before promises
How value is verified
No vanity accuracy number. Evidence is chosen around the operational decision and agreed before delivery starts.
- ✓Holdout results segmented by geography—not one global accuracy score
- ✓Baseline comparison with the current process or a non-spatial model
- ✓Reproducible pipeline, decision log, architecture diagram, and acceptance test
// Buyer questions
Frequently asked questions
Do we need labeled geospatial data?+
Not always. Discovery tests whether existing outcomes, weak labels, remote-sensing products, or expert review can create defensible ground truth.
Can you work in our cloud or in-country data centers across the GCC?+
Yes. We support client-controlled infrastructure across Saudi Arabia (Riyadh, Dammam, Jeddah), UAE (Dubai, Abu Dhabi), Qatar (Doha Google Cloud region), Bahrain (AWS Middle East Manama), Kuwait (Kuwait City cloud zones), and Oman (Muscat data centers), complying strictly with national data residency regulations including Saudi PDPL/SDAIA, UAE Federal Decree-Law No. 45, and Qatar PDPPL.
What is not a good first use case?+
A speculative idea with no decision owner, representative data, or route for users to act.
Who owns the intellectual property and model weights upon completion?+
You own 100% of the intellectual property, model weights, custom training scripts, pipeline code, and documentation. Everything is delivered to your client-controlled repositories with zero vendor lock-in or recurring IP royalties.
What is typical GeoAI project pricing and GIS consulting cost?+
GeoAI consulting engagements typically start with a fixed-fee diagnostic or feasibility proof-of-concept sprint ($6,000 to $12,000) that validates spatial signal, checks data leakage, and benchmarks baseline accuracy over 2 to 3 weeks. Full production model and pipeline deliveries range from $30,000 to $80,000 across 10 to 20 weeks, structured in clear milestone-based deliverables with zero ongoing licensing fees or IP lock-in.
What engagement models do you offer for GeoAI delivery?+
We primarily work under two models: a fixed-fee 2-to-3 week diagnostic/feasibility sprint (starting from $6,000) that de-risks the problem and validates the spatial signal, followed by phased milestone-based delivery sprints (typically 10–20 weeks) with agreed acceptance criteria.
Can our models integrate with existing BI tools like Power BI or Tableau?+
Yes. Our architectures expose standard REST/GraphQL APIs, materialized database views, and cloud data warehouse tables (Snowflake, BigQuery, Databricks) so non-technical stakeholders view predictive spatial outputs inside their standard dashboards.
Live GeoAI Product · Free ToolExplore our live Wildfire BurnScar Detection PlatformAutomated Sentinel-2 & Landsat 8/9 multispectral processing, USGS dNBR/BAIS2 severity index calculation, NASA FIRMS active fire overlay, and Cloud-Optimized GeoTIFF downloads.Launch BurnScar Map →Technical Deep Dive · Spatial MLOpsSpatial Data Leakage in Machine Learning: Why Standard Splits FailA technical guide to spatial autocorrelation, spatial block cross-validation, and preventing artificial accuracy inflation in geospatial AI models.Read the technical guide →View all capabilities →
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Bring us the problem, not a perfect brief.
Three fields start the conversation. An engineer will help determine fit, data readiness, and the smallest useful next step.
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