Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Manual satellite image analysis is slow, costly, and prone to human error, creating critical intelligence gaps.
Traditional geospatial intelligence relies on analysts manually scanning thousands of satellite images—a process that is painfully slow, impossible to scale, and vulnerable to oversight. Key objects and subtle changes are easily missed, leading to delayed decisions and operational risk.
Our geospatial computer vision service automates this process, delivering 99%+ detection accuracy for objects like vehicles, ships, and infrastructure at planetary scale, turning imagery into actionable intelligence in minutes, not months.
For enterprises requiring real-time situational awareness, this manual bottleneck is a critical vulnerability. Explore our approach to planetary-scale satellite imagery AI processing or learn how we build Geospatial RAG Systems for accurate, sourced intelligence reporting.
Our geospatial computer vision solutions translate directly into measurable operational improvements and cost savings. We focus on delivering specific, quantifiable results for national security, logistics, and infrastructure monitoring.
Implement edge AI deployment on drones and field devices to cut analysis latency from hours to seconds, delivering real-time intelligence for rapid decision-making in disaster response and dynamic security scenarios.
Utilize time-series geospatial AI to predict failures in pipelines, power lines, and railways weeks in advance, shifting from reactive to prognostic maintenance to prevent costly downtime and safety incidents.
Ensure all data processing and model training adheres to sovereign data mandates and security protocols. Our architectures are designed for air-gapped or hybrid-cloud environments, meeting defense and enterprise compliance standards.
A structured breakdown of our engagement phases for developing and deploying a custom geospatial computer vision system, from initial model design to production integration.
| Phase & Deliverables | Timeline | Key Outcomes |
|---|---|---|
Phase 1: Discovery & Data Strategy | 1-2 Weeks | Technical requirements document, annotated data sample strategy, and architecture blueprint. |
Phase 2: Model Development & Training | 3-5 Weeks | Custom-trained object detection model (e.g., YOLOv8, Detectron2) with validated performance metrics on test set. |
Phase 3: Deployment & Integration | 2-3 Weeks | Production-ready API endpoint or containerized model integrated with your GIS platform (e.g., ArcGIS) or edge device. |
Phase 4: Performance Optimization & Scaling | 1-2 Weeks | Optimized inference latency (<100ms per tile), scalability plan, and operational runbook. |
Ongoing Support & MLOps | Optional SLA | Model monitoring, periodic retraining on new data, and 99.9% uptime guarantee. |
Total Project Duration | 7-12 Weeks | Fully operational, high-precision object detection system for aerial and satellite imagery. |
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers about our object detection service for satellite and aerial imagery, from timelines and costs to security and support.
Standard deployments for a production-ready object detection model (e.g., vehicle tracking) take 2-4 weeks. Complex, multi-class detection across diverse geographies or integration with existing systems like ArcGIS may extend to 6-8 weeks. We provide a detailed project plan with milestones after the initial discovery phase.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
