Processing petabytes from constellations like Sentinel and Landsat requires more than cloud storage. It demands specialized AI infrastructure capable of continent-scale object detection, land cover classification, and change detection with sub-meter accuracy.
Service
Planetary-scale Satellite Imagery AI Processing

The Challenge of Petabyte-Scale Geospatial Intelligence
Engineering high-throughput AI pipelines to extract intelligence from petabytes of satellite imagery for defense, climate, and urban planning.
- High-Throughput Pipelines: Architect dataflows that ingest, preprocess, and analyze multi-terabyte daily volumes without bottlenecks.
- Scalable Model Serving: Deploy and orchestrate thousands of concurrent inference endpoints for models like SAM 2 and YOLO across hybrid cloud and edge.
- Deterministic Analytics: Transform probabilistic AI outputs into trusted, actionable intelligence layers for integration into ArcGIS and custom geospatial intelligence platforms.
Without engineered AI pipelines, raw satellite data remains a cost center, not an intelligence asset. We build systems that deliver structured insights at planetary scale.
Move from data overload to operational command. Explore our comprehensive approach to Geospatial AI and Spatial Analytics or dive into specialized services like Geospatial AI Model Training and Fine-tuning and Vector Database Solutions for Spatial Data.
Business Outcomes of Scalable Satellite AI Pipelines
Our planetary-scale AI processing pipelines deliver measurable ROI by transforming petabytes of raw satellite data into actionable intelligence, enabling faster, data-driven decisions for environmental monitoring, defense, and infrastructure planning.
Typical Project Timeline and Deliverables
A transparent breakdown of the phased approach and key outputs for our planetary-scale satellite imagery AI processing engagements, designed for enterprise technical leaders.
| Phase & Key Deliverables | Timeline | Core Activities | Outcome |
|---|---|---|---|
Discovery & Feasibility Assessment | 1-2 weeks | Requirements gathering, data source evaluation, technical scoping | Project roadmap & architecture proposal |
Data Pipeline Engineering & Model Selection | 2-4 weeks | Ingestion pipeline setup, model benchmarking (e.g., SAM 2, YOLO), initial PoC | Validated data flow & model performance baseline |
Custom Model Training & Fine-tuning | 3-6 weeks | Dataset curation, distributed training, hyperparameter optimization | Production-ready model with >95% target accuracy |
High-Throughput Inference System Deployment | 2-3 weeks | Scalable API development, containerization (Docker/K8s), load testing | Deployed pipeline processing >1M km²/day |
Integration & MLOps Lifecycle Setup | 1-2 weeks | CI/CD pipeline, monitoring dashboards, integration with client GIS (e.g., ArcGIS) | Fully operational system with retraining triggers |
Knowledge Transfer & Ongoing Support | Ongoing | Documentation, team training, optional SLA for maintenance | Autonomous internal operation capability |
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Frequently Asked Questions on Satellite Imagery AI
Get clear answers on timelines, costs, and technical capabilities for deploying AI to analyze petabytes of satellite data.
A standard deployment for a continent-scale object detection or land cover classification pipeline takes 2-4 weeks from kickoff to production-ready inference. This includes data pipeline setup, model fine-tuning on your domain, and integration with your GIS (e.g., ArcGIS) or data lake. Complex multi-model workflows or custom foundation model training extend this to 6-8 weeks. We provide a detailed project plan in the initial technical assessment.

About the author
Prasad Kumkar
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.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
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.
01
Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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