Core IoT & Edge AI Capabilities
End-to-end IoT solutions engineered for production environments, from sensor integration to cloud orchestration.
Custom hardware selection, environmental hardening, and seamless sensor integration for industrial IoT deployments.
Hardware Selection & BOM Optimization
Strategic component selection balancing cost, performance, and availability. We evaluate ESP32, STM32, Raspberry Pi, and industrial-grade alternatives based on your environmental constraints and processing requirements.
- Environmental rating analysis (IP65/67, temperature ranges)
- Power consumption profiling and battery life optimization
- Supply chain risk assessment and alternative sourcing
Sensor Integration & Calibration
Multi-protocol sensor integration with automated calibration routines. Support for analog, digital, I2C, SPI, and Modbus sensors with real-time data validation and drift detection.
- Temperature, humidity, pressure, vibration, and optical sensors
- Industrial protocols: 4-20mA, 0-10V, RS485, CAN bus
- Automated calibration sequences and drift compensation
On-device machine learning with sub-100ms inference times, optimized for resource-constrained edge environments.
Model Optimization & Deployment
Transform cloud-trained models into edge-optimized inference engines. ONNX and TensorRT optimization with quantization, pruning, and hardware-specific acceleration.
- Model quantization (INT8, FP16) for memory efficiency
- TensorRT optimization for NVIDIA Jetson platforms
- ONNX Runtime deployment for ARM and x86 edge devices
Real-Time Inference Pipeline
Sub-100ms decision making with predictable latency. Implement preprocessing, inference, and post-processing pipelines optimized for your specific use case and hardware constraints.
- Deterministic inference scheduling and resource allocation
- Multi-threaded processing with priority queuing
- Fallback mechanisms and graceful degradation
Enterprise-grade security with MQTT, Modbus, and custom protocols. Certificate management and encrypted data transmission.
Protocol Implementation & Security
Multi-protocol communication stacks with end-to-end encryption. MQTT 5.0, Modbus TCP/RTU, OPC-UA, and custom protocols with TLS 1.3 and certificate-based authentication.
- MQTT 5.0 with QoS guarantees and retained messages
- Modbus TCP/RTU for industrial equipment integration
- OPC-UA for standardized industrial communication
Network Resilience & Failover
Robust connectivity with automatic failover between cellular, WiFi, and Ethernet. Offline operation with local data buffering and intelligent synchronization strategies.
- Multi-WAN failover with connection health monitoring
- Local data buffering with compression and deduplication
- Intelligent sync strategies for bandwidth-constrained environments
Scalable device management with OTA updates, health monitoring, and predictive maintenance for 99.9% uptime.
Over-the-Air (OTA) Updates
Secure, atomic firmware and model updates with rollback capabilities. Staged deployments with canary releases and automatic health checks to ensure fleet stability.
- Atomic updates with automatic rollback on failure
- Staged deployment with configurable rollout percentages
- Delta updates to minimize bandwidth usage
Predictive Health Monitoring
Comprehensive device telemetry with anomaly detection and predictive maintenance. Real-time alerting and automated response to prevent downtime before it occurs.
- Hardware health metrics: temperature, voltage, memory usage
- Performance monitoring: inference latency, throughput, accuracy
- Predictive failure detection with maintenance scheduling
Implementation Approach
Structured deployment methodology from proof-of-concept to production rollout with measurable milestones.
1. Discovery & Planning
1-2 weeks
- Technical specification document
- Hardware BOM and sourcing plan
- Risk assessment and mitigation strategies
- Success metrics and KPI definitions
2. Proof of Concept
2-3 weeks
- Working prototype with core functionality
- Performance benchmarks and validation
- Integration testing with existing systems
- Go/no-go recommendation with data
3. Pilot Implementation
6-8 weeks
- Production-ready system deployment
- Monitoring and alerting infrastructure
- Security hardening and compliance validation
- Operational runbooks and documentation
4. Production Rollout
Phased
- Automated deployment and provisioning
- Fleet management and OTA update system
- Team training and knowledge transfer
- Ongoing support and optimization plan
Performance & Reliability Metrics
Quantifiable results and success indicators for IoT & Edge AI implementations with industry benchmarks.
0ms
Edge Response Time
Sub-100ms inference and decision making at the edge
0%
Fleet Uptime
Enterprise-grade reliability with predictive maintenance
0M+
Items Processed
Proven scale across industrial deployments
0-bit
AES Encryption
End-to-end security with certificate management
Learn More About Edge AI
Explore our blog posts to deepen your understanding of edge AI deployment strategies and best practices.
Understand when to deploy AI at the edge versus in the cloud, with practical guidance on latency, connectivity, and privacy trade-offs for real-time industrial systems.
Learn about different types of AI systems and when to use rule-based systems, machine learning, or LLMs for your specific business requirements.
