What we build and how we deliver it

Each engagement starts with understanding your equipment, constraints, and goals—then we build exactly what's needed to connect your systems and surface the data that matters.

01

Industrial Integration & Orchestration

Connect robots, PLCs, conveyors, sensors, and software systems into one orchestration layer—regardless of vendor or vintage.

The Problem

Most factories run equipment from multiple vendors—each with its own protocol, API, and data format. When you add a new AMR fleet or PLC line, connecting it to existing conveyors, ERP, or MES systems turns into a brittle, custom-coded mess that breaks when anything changes.

Our Approach

We build a vendor-agnostic middleware layer that normalizes communication across your equipment. APIs, event pipelines, message brokers, and field protocol adapters—designed for reliability and maintainability, not one-off scripts.

Deliverables

  • Integration architecture document and protocol map
  • Middleware services connecting robots, PLCs, conveyors, and business systems
  • REST and MQTT APIs for cross-system communication
  • Event pipelines for real-time workflow coordination
  • Monitoring and alerting for integration health

Typical Timeline

  • Discovery & architecture: 1–2 weeks
  • Pilot integration (1–2 systems): 4–6 weeks
  • Production rollout: 2–4 weeks per additional system
  • Ongoing support: available monthly or per-incident
AMRsPLCsConveyorsREST APIsMQTTMiddlewareERP / MESVendor-Agnostic
02

Factory Data & Analytics

Turn raw machine data into utilization metrics, downtime reasons, and efficiency insights that drive real operational decisions.

The Problem

You know machines go down, but you can't quantify how much time is lost or why. CNC controllers, PLCs, and legacy equipment hold valuable data—cycle times, error codes, run/idle/fault states—but it's locked inside proprietary interfaces with no unified view.

Our Approach

We extract data directly from controllers (Fanuc/FOCAS, Siemens, Allen-Bradley, and others), normalize it into a consistent schema, and feed it into dashboards and reports your team will actually use. OEE-style metrics without the enterprise software overhead.

Deliverables

  • CNC and PLC data extraction pipelines
  • Machine state tracking (run, idle, fault, setup)
  • Cycle time analysis and production counts
  • Error code capture and downtime categorization
  • Utilization and OEE dashboards (Grafana, custom, or your existing BI tool)
  • Automated shift and daily reports

Typical Timeline

  • Discovery & data audit: 1–2 weeks
  • Pilot (3–5 machines): 4–6 weeks
  • Full floor rollout: 2–6 weeks depending on scale
  • Dashboard iteration: ongoing as metrics evolve
OEEFanuc / FOCASSiemensCycle TimesError CodesDowntime AnalysisGrafanaDashboards
03

Industrial IoT Edge Systems

Deploy on-prem edge gateways that collect, normalize, and publish factory data securely—with low latency and high reliability.

The Problem

Cloud-only architectures add latency, depend on internet uptime, and raise security concerns for factory data. But running everything on a single on-prem server creates a fragile single point of failure with no scalability.

Our Approach

We design and deploy edge gateways (Raspberry Pi, Jetson, or industrial-grade hardware) that run locally at the cell or line level. Data is collected via OPC UA, MQTT, Modbus, or direct controller APIs, processed at the edge, and forwarded to cloud or on-prem systems as needed.

Deliverables

  • Edge gateway hardware selection and provisioning
  • OPC UA / MQTT broker architecture
  • Data collection agents for PLCs, sensors, and controllers
  • Local buffering and store-and-forward for network resilience
  • Secure remote access and device management
  • Container-based deployment for easy updates

Typical Timeline

  • Architecture & hardware spec: 1–2 weeks
  • Pilot node deployment: 3–5 weeks
  • Multi-node rollout: 1–2 weeks per node
  • Remote management setup: included in pilot
OPC UAMQTTModbusRaspberry PiJetsonEdge ComputeDockerStore & Forward
04

Energy Monitoring & Optimization

Track machine-level power consumption, detect anomalies, and allocate energy costs to specific assets and production lines.

The Problem

You get a monthly utility bill but have no idea which machines or lines are consuming the most energy, running inefficiently, or exhibiting abnormal draw patterns that indicate maintenance issues. Cost allocation across products or lines is guesswork.

Our Approach

We install CT clamps and power meters at the machine or circuit level, feed data to edge gateways, and build dashboards that show real-time and historical consumption. Alerts flag anomalies—a motor drawing 40% more than normal, or a machine left running overnight.

Deliverables

  • CT clamp and power meter installation plan
  • Real-time energy consumption dashboards by machine/line
  • Anomaly detection alerts (abnormal draw, off-hours usage)
  • Cost allocation reports by asset, line, or product
  • Historical trending and seasonal analysis

Typical Timeline

  • Site survey & sensor plan: 1 week
  • Sensor installation + data pipeline: 2–4 weeks
  • Dashboard & alerting setup: 1–2 weeks
  • Reporting iteration: ongoing
CT ClampsPower MetersShelly EMCost AllocationAnomaly DetectionDashboards
05

Camera Intelligence (RTSP)

Add AI-powered detection to your existing camera infrastructure—no camera replacement needed.

The Problem

You have RTSP cameras across your facility for security, but they're passive—someone has to be watching. Safety incidents, spills, unauthorized access, and PPE violations go undetected until after the fact. Upgrading to "smart" cameras means replacing an entire infrastructure.

Our Approach

We tap into your existing RTSP streams and run AI detection models—on edge devices (Jetson, local GPU) or hybrid cloud. Real-time alerts for safety hazards, spills, restricted objects, and incidents. Your cameras stay, the intelligence is added.

Deliverables

  • RTSP stream integration with AI inference pipeline
  • Custom detection models (PPE, spills, restricted zones, hazards)
  • Real-time alerting (SMS, email, dashboard, PLC signal)
  • Event logging with frame captures for incident review
  • Edge or hybrid cloud deployment based on your requirements

Typical Timeline

  • Camera audit & use case scoping: 1 week
  • Model training & pipeline build: 4–6 weeks
  • On-site deployment & tuning: 1–2 weeks
  • Additional camera/zone rollout: 1 week each
RTSPComputer VisionYOLOJetsonPPE DetectionSafety AlertsEdge AI
06

Automation & IoT Consultancy

Expert guidance on automation strategy, system architecture, and technology selection—before you commit to a build.

The Problem

You know you need to modernize or scale automation, but the landscape of vendors, protocols, platforms, and architectures is overwhelming. Making the wrong technology bet means wasted budget, vendor lock-in, or building something that doesn't scale. Your team may lack the specialized industrial IoT expertise to evaluate options confidently.

Our Approach

We work alongside your engineering and operations teams as a hands-on technical partner. We assess your current state, identify gaps and opportunities, evaluate vendor and technology options, and deliver a clear roadmap—with architecture decisions justified and trade-offs documented. We can stay on as advisors through implementation or hand off to your team.

Deliverables

  • Current-state assessment of equipment, protocols, and data flows
  • Technology evaluation and vendor comparison
  • Integration architecture and roadmap document
  • Build-vs-buy analysis for key components
  • Risk assessment and phased implementation plan
  • Ongoing advisory retainer (optional)

Typical Timeline

  • Assessment & interviews: 1–2 weeks
  • Architecture & roadmap delivery: 1–2 weeks
  • Vendor evaluation (if needed): 1–3 weeks
  • Advisory retainer: monthly, ongoing
StrategyArchitectureTech SelectionRoadmapsBuild vs BuyAdvisoryVendor Evaluation
07

Staff Augmentation & Embedded Teams

Skilled engineers and operations leads who embed with your team—filling critical roles without the overhead of full-time hiring.

The Problem

You have automation projects stacking up but not enough specialized people to execute them. Hiring full-time controls engineers, integration specialists, or service ops leads takes months—and the work can't wait. Contractors from generic staffing firms don't understand industrial systems deeply enough to be productive quickly.

Our Approach

We place experienced engineers and operations professionals directly into your team. They work on your systems, attend your standups, and deliver like internal staff—but with deep industrial IoT and automation expertise from day one. Engagements flex from part-time advisory to full-time embedded roles.

Roles We Fill

  • Service Ops Lead — oversee deployment, commissioning, and ongoing support of automation systems
  • Controls / Automation Engineer — PLC programming, robot integration, system commissioning
  • Integration / Software Engineer — middleware, APIs, data pipelines, edge systems
  • IoT / Edge Engineer — gateway deployment, OPC UA/MQTT architecture, device management
  • Data / Analytics Engineer — dashboards, OEE metrics, reporting pipelines
  • Project / Technical Lead — coordinate cross-functional automation rollouts

Engagement Models

  • Embedded full-time: dedicated engineer on your team, monthly
  • Part-time / fractional: 2–3 days per week for ongoing support
  • Project-based: scoped engagement with defined deliverables
  • On-site + remote: flexible based on project needs
  • Ramp-up time: typically productive within 1–2 weeks
Service Ops LeadControls EngineerIntegration EngineerIoT EngineerData EngineerTechnical LeadEmbedded Teams

Not sure where to start?

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