Core Infrastructure
StreamMesh — Real-Time Intelligence Infrastructure
StreamMesh
Organizations deploying sensor networks and IoT infrastructure face three architectural challenges: **Cloud-Centric Latency**: Traditional architectures send all sensor data to centralized cloud infrastructure for processing. By the time data travels to the cloud, gets analyzed, and control signals return, critical events have already occurred. Round-trip times of 200-500ms (or seconds over congested networks) make real-time control impossible. You're always reacting to outdated information. **Bandwidth and Cost Constraints**: Streaming every sensor reading to the cloud consumes enormous bandwidth. At scale (thousands of sensors reporting at 10-100 Hz), egress costs become prohibitive and network capacity saturates. You're forced to downsample data or batch uploads, losing temporal fidelity and missing transient events. The data you need for detailed analysis never reaches analytics systems. **Edge Devices Lack Coordination**: Running analytics locally on individual edge devices solves latency, but creates fragmentation. Each device operates in isolation without visibility into the broader system. Cross-device correlation, fleet-wide anomaly detection, and coordinated control become impossible. You gain speed but lose the ability to understand system-level behavior.
Who This Is For
**Industrial Operations Managers** overseeing plants, facilities, or infrastructure where real-time monitoring and control are essential but centralized cloud processing creates unacceptable latency. **IoT Product Leaders** building connected device fleets where sending all data to the cloud is cost-prohibitive or technically infeasible due to bandwidth constraints. **Technical Directors** responsible for edge computing initiatives who need coordinated intelligence across distributed devices rather than fragmented local processing. This is for organizations in manufacturing, energy, transportation, smart buildings, agriculture, or telecommunications deploying hundreds to hundreds of thousands of sensors. If you're hitting cloud bandwidth costs, latency prevents real-time control, or you need cross-device analytics that centralized architectures can't deliver, distributed stream processing becomes essential.
What You Get
StreamMesh provides a complete distributed stream processing platform spanning edge devices, edge clusters, and cloud backends. You get millisecond-latency analytics at the edge for time-critical decisions, coordinated intelligence across device fleets, and comprehensive cloud analytics for long-term insights—all orchestrated through a unified data pipeline architecture. Your operations teams see real-time dashboards reflecting current system state, control systems respond to anomalies instantly, and data scientists access both real-time and historical data for model development—without building custom data infrastructure.
How We Work
Key Deliverables
1
Distributed Stream Processing Architecture
Multi-tier data pipeline spanning the compute continuum:
2
Real-Time Analytics & Event Detection
Sophisticated stream analytics at the edge:
3
Edge-to-Cloud Data Orchestration
Intelligent data routing and transformation:
4
Time-Series Data Management
Optimized storage for sensor data:
5
Device Fleet Management
Centralized orchestration for distributed edge infrastructure:
6
Machine Learning Integration
Deploying and updating ML models across the edge:
7
Integration with Operational Systems
Connecting stream processing to control and analytics:
8
Security & Compliance Framework
Protecting distributed data pipelines:
9
Operational Dashboards & Monitoring
Real-time visibility into system behavior:
10
Knowledge Transfer & Training
Ensuring your team can operate and extend the platform: