Cloud Ground Control engineers production-ready platforms for computer vision, autonomous fleets, predictive maintenance, agent orchestration, RAG knowledge systems, and model operations. Built for field environments where latency, privacy, bandwidth, and reliability matter.
Cloud Ground Control is an advanced systems engineering company specialising in edge AI, computer vision, and autonomous fleet intelligence. We build the platforms that let autonomous systems perceive, decide, and act — with or without cloud connectivity.
Our work spans real-time object detection on constrained hardware, multi-agent drone coordination, predictive fleet analytics, AI-driven observability, LLM fine-tuning pipelines, and grounded knowledge systems for complex IoT deployments.
Everything we build shares one philosophy: intelligence should live as close to the data source as possible — on the device, in the ward, on the drone, in the field. Not in a cloud data centre 500 milliseconds away.
Each platform solves a distinct layer of the autonomous intelligence stack — from real-time computer vision at the edge to domain-adapted LLMs trained on your private data.
Architected edge AI computer vision stack using YOLOv8, TensorRT, and OpenVINO for real-time object detection on constrained edge devices. Event-driven architecture with MQTT and WebRTC cuts bandwidth by ~80% and inference latency by ~60–70%. Autonomous multi-agent coordination across distributed drone fleets reduces manual monitoring by 50%+.
Predictive analytics platform using XGBoost, LSTM, and time-series forecasting reduces fleet downtime by ~30% through proactive maintenance. Kafka and Pandas ingestion pipelines feed TimescaleDB for high-throughput telemetry storage and sub-second querying. Mission planning algorithms achieve ~15% battery efficiency gain across large-scale autonomous operations.
Multi-agent orchestration platform using a Manager–Worker–Monitor hierarchy for autonomous diagnostics and incident management across drone and IoT edge device fleets. Manager agents decompose alerts into tasks dispatched to specialised Workers, while a Monitoring agent observes agent health and triggers self-healing workflows. Human-in-the-loop gates for high-risk actions. Built on LangGraph and LlamaIndex.
Industry-standard RAG chatbot for drone and IoT device knowledge management. ClickHouseDB serves as the high-performance vector store; Redis caches hot repeated queries. LlamaIndex handles document ingestion and chunking, LangChain orchestrates retrieval. MCP servers provide live device data access. Every answer is cited. Supports chat and voice interfaces for field engineers.
End-to-end platform for fine-tuning LLMs and vision models on your private organisational data — drone telemetry, healthcare imaging, IoT streams, operational SOPs. LoRA/QLoRA fine-tuning on Modal serverless GPU, AWS SageMaker, or local air-gapped hardware. YOLOv8 custom training for edge vision tasks. MLflow experiment registry with full lineage. Exports to ONNX, TensorRT, and OpenVINO for edge deployment.
The five CGC platforms are not independent products — they form a self-reinforcing intelligence loop. Data flows from edge detection into prediction, prediction informs agents, agents generate training data, training improves knowledge retrieval. The system gets better from its own operational experience.
Tell us about the environment, constraints, data sources, and operational outcome you need. We will help map the right edge, cloud, and AI architecture.