Beyond monitoring

Problem

Multi-vendor equipment with different protocols. Every new system means another brittle point-to-point integration.

Approach

We build middleware that normalizes communication across all your equipment. APIs, event pipelines, and protocol adapters designed for reliability.

Deliverables

• Integration architecture and protocol map
• Middleware connecting robots, PLCs, and business systems
• REST + MQTT APIs for cross-system communication
• Monitoring and alerting for integration health

Timeline

• Discovery: 1–2 weeks
• Pilot: 4–6 weeks
• Rollout: 2–4 weeks per system

Problem

Machines go down. You can't quantify how much time is lost or why. Data is locked in proprietary controller interfaces.

Approach

We extract data directly from controllers (Fanuc, Siemens, Allen-Bradley), normalize it, and build dashboards your team will use.

Deliverables

• CNC and PLC data extraction pipelines
• Machine state tracking (run, idle, fault)
• Cycle time analysis and OEE dashboards
• Automated shift and daily reports

Timeline

• Data audit: 1–2 weeks
• Pilot (3–5 machines): 4–6 weeks
• Full rollout: 2–6 weeks

Problem

Cloud-only adds latency and depends on internet uptime. A single on-prem server is a fragile single point of failure.

Approach

Edge gateways at the cell or line level. Data collected via OPC UA, MQTT, or Modbus, processed locally, forwarded as needed.

Deliverables

• Edge gateway provisioning and architecture
• Data collection agents for PLCs and sensors
• Local buffering and store-and-forward
• Secure remote access and device management

Timeline

• Architecture: 1–2 weeks
• Pilot node: 3–5 weeks
• Multi-node rollout: 1–2 weeks each

Problem

Monthly utility bill, no idea which machines consume the most. Cost allocation is guesswork. Abnormal draw goes undetected.

Approach

CT clamps and power meters at the machine level, data to edge gateways, real-time dashboards, automatic anomaly alerts.

Deliverables

• CT clamp and power meter installation
• Real-time energy dashboards by machine/line
• Anomaly detection and alerting
• Cost allocation reports by asset or product

Timeline

• Site survey: 1 week
• Installation + pipeline: 2–4 weeks
• Dashboards + alerts: 1–2 weeks

Problem

Cameras are passive. Safety incidents and PPE violations go undetected until after the fact. "Smart" cameras mean replacing everything.

Approach

We tap into existing RTSP streams, run AI models on edge devices. Real-time alerts for hazards, spills, restricted zones.

Deliverables

• RTSP integration with AI inference pipeline
• Custom detection models (PPE, spills, zones)
• Real-time alerting (SMS, email, dashboard)
• Event logging with frame captures

Timeline

• Camera audit: 1 week
• Model training + pipeline: 4–6 weeks
• Deployment + tuning: 1–2 weeks

Problem

Overwhelming landscape of vendors and protocols. The wrong technology bet means wasted budget and vendor lock-in.

Approach

We assess your current state, evaluate options, and deliver a clear roadmap with architecture decisions and trade-offs documented.

Deliverables

• Current-state assessment
• Technology evaluation and vendor comparison
• Integration architecture and roadmap
• Build-vs-buy analysis

Timeline

• Assessment: 1–2 weeks
• Architecture + roadmap: 1–2 weeks
• Advisory retainer: monthly, ongoing

Problem

Projects are stacking up, specialized people are hard to hire, and generic staffing firms don't understand industrial systems.

Approach

We place experienced engineers directly into your team. They deliver like internal staff with deep industrial IoT expertise.

Roles

• Service Ops Lead — deployment, commissioning, support
• Controls Engineer — PLC programming, robot integration
• Integration Engineer — middleware, APIs, data pipelines
• IoT / Edge Engineer — gateways, OPC UA, MQTT
• Data Engineer — dashboards, OEE, reporting

Engagement Models

• Embedded full-time: dedicated, monthly
• Part-time: 2–3 days per week
• Project-based: scoped deliverables
• Ramp-up: productive within 1–2 weeks