Build vs. Buy
The hidden iceberg of geospatial AI agents
12 to 24 months and a low rate of production deployment, versus weeks and a system designed to be operated. Building a geospatial AI agent looks straightforward, until the demo meets real data, real users and a security review.
TL;DR
A weekend prototype is not a production system. The 90% under the waterline, data, evals, security, ops, is where in-house geospatial AI projects stall.
01Speed to production
Years of work, or weeks
| COMPONENT | BUILD IN-HOUSE | WITH GIA |
|---|---|---|
| Data connectors and ingestion | 8 to 12 weeks | Hours |
| Geospatial RAG and spatial joins | 6 to 10 weeks | Included |
| Evaluation and traceability | 3 to 5 weeks | Included |
| Analytics and monitoring | 3 to 4 weeks | Included |
| Embedded UI and integrations | 3 to 6 weeks | Hours |
| Security and compliance posture | 3+ months | Inherited |
| Ongoing maintenance | 2+ specialists | Operated for you |
Bottom line: 12 to 24 months and a low rate of production deployment, versus weeks and a system designed to be operated.
02Decision framework
When to buy. When to build.
BUYRECOMMENDED FOR MOST
Choose buying when
- Geospatial AI supports your business but is not the product you sell
- Speed to value matters for the next planning cycle
- Engineering should focus on your domain, not GIS plumbing
- You want production reliability without absorbing the failure risk
BUILDNARROW FIT
Choose building only when
- Geospatial AI is your core product and your differentiator
- You already have Earth Observation, GIS and Machine Learning specialists on staff
- You can absorb 12 to 24 months of build time and the failure risk
- You can fund 2+ specialists indefinitely for maintenance
BOOK A DEMO
Ready to skip the 12 to 24 month build?
Talk to the team! We'll walk you through GIA on your data and what an enterprise rollout looks like.