Edge AI Integration

Intelligence Where Cloud Can't Reach

Not everything belongs in the cloud. We build AI systems that run on-site — for privacy, latency, or autonomy. We start with your engineers, not your executives. They know where it hurts.

Why Edge

Latency <50ms

Real-time decisions on-device

Zero cloud dependency

Operates offline

Data never leaves site

Privacy by architecture

No vendor lock-in

Hardware-agnostic approach

Quick Answers

When do I need Edge AI instead of cloud?

When latency matters (real-time control), when connectivity is unreliable, when data can't leave premises (privacy/compliance), or when you need autonomous operation.

What hardware do you work with?

Primarily NVIDIA Jetson and Khadas platforms, selected based on your performance needs and budget. Industrial controllers can be augmented with single-board computers for AI capabilities.

Can AI really make autonomous decisions?

Depends on scope. Monitoring and alerts — yes, quickly. Bounded decisions within defined parameters — yes, with calibration. Full autonomy — requires R&D, RLHF, extensive testing. We're honest about what's a 3-month project vs. a research endeavor.

Why not use off-the-shelf solutions?

If they fit — use them. We're for cases where generic solutions don't match your specific processes, equipment, or constraints. Your machine #7's 5% material overrun won't be fixed by a dashboard designed for average factories.

What industries do you serve?

Manufacturing (predictive maintenance, quality control), energy/utilities (grid monitoring), defense (rugged environments), agriculture (process automation), and any enterprise with strict data residency requirements.

Do I need to replace existing equipment?

Usually no. Edge AI typically augments existing infrastructure. We connect to your PLCs, sensors, and control systems — not replace them.

Autonomy Levels — Honest Expectations

Not every AI project is a 3-month deployment. Here's what's realistic.

Monitoring & Alerts

2-3 months

AI observes, detects anomalies, alerts humans. No autonomous actions.

Examples: Bearing degradation detection, quality deviation alerts, energy consumption anomalies

Risk: Low

Bounded Automation

3-6 months

AI makes decisions within strict parameters. Stop/continue, adjust value X within range Y. Requires calibration per installation.

Examples: Automatic parameter adjustment, predictive maintenance scheduling, defect rejection

Risk: Medium — requires thorough testing

Full Autonomy

6-18+ months (R&D)

AI operates independently, learns, adapts. Requires RLHF, extensive simulation, safety validation. This is research, not deployment.

Examples: Self-optimizing production lines, adaptive process control

Risk: High — outcome not guaranteed

What We Actually Do

We don't sell AI boxes. We dig into your specific processes and build what actually solves your problem.

Interview your engineers — they know where it hurts, not the executive summary
Analyze your actual data, processes, and edge cases
Design architecture with privacy as default, not afterthought
Deploy on appropriate hardware (Jetson, Khadas, or your existing infrastructure)
Train models on your specific conditions — not generic datasets
Implement monitoring, logging, and human oversight appropriate to autonomy level
Knowledge transfer or ongoing support — your choice

Edge AI vs Cloud AI vs Traditional Automation

Capability	Edge AI	Cloud AI	Traditional SCADA/PLC
Latency	<50ms	100ms-seconds	<10ms
Offline operation	Full	None	Full
Data privacy	On-site	Cloud storage	On-site
Pattern recognition	Advanced	Advanced	Rule-based only
Adaptability	Learns from data	Learns from data	Manual reprogramming
Initial cost	Medium	Low	Low
Ongoing cost	Low	Usage-based	Low

Typical Starting Points

Predictive Maintenance

Manufacturing

Approach:

• Connect to vibration, temperature, current sensors
• Collect baseline data during normal operation
• Train anomaly detection model (CNN/LSTM)
• Deploy on edge device near equipment
• Alert maintenance team before failure
• Refine model with feedback loop

Expected outcomes:

✓ Unplanned downtime reduction
✓ Maintenance cost optimization
✓ Equipment lifespan extension

Quality Control

Manufacturing

Approach:

• Install vision system or connect to existing sensors
• Label defect types with your QC team
• Train classification model
• Deploy with configurable rejection thresholds
• Log all decisions for traceability
• Continuous improvement from edge cases

Expected outcomes:

✓ Defect escape rate reduction
✓ Consistent quality regardless of shift
✓ Full traceability for compliance

Grid/Energy Monitoring

Utilities

Approach:

• Connect to smart meters, transformers, grid sensors
• Establish normal consumption/generation patterns
• Deploy anomaly detection for load balancing
• Local decisions for demand response
• Secure sync to central systems when connected
• Audit logging for regulatory compliance

Expected outcomes:

✓ Faster anomaly response
✓ Reduced grid instability
✓ Compliance-ready logging

Rugged/Field Deployment

Defense, Remote Operations

Approach:

• Define operational envelope (temperature, vibration, power)
• Select hardened hardware platform
• Design for degraded connectivity scenarios
• Implement local decision-making with defined bounds
• Secure data handling and transmission
• Field testing under real conditions

Expected outcomes:

✓ Autonomous operation in disconnected environments
✓ Reduced human intervention in hazardous conditions
✓ Data security in field conditions

Process

We need access to your facility and your engineers. No remote-only implementations.

1-2 weeks

Discovery

• On-site assessment: talk to engineers, observe processes
• Identify real pain points (not executive wishlist)
• Evaluate existing infrastructure and data availability
• Define realistic scope and success criteria
• Hardware recommendations if needed

4-8 weeks

Proof of Concept

• Collect and label data from target process
• Train and validate model on your specific conditions
• Deploy on edge hardware in test environment
• Measure actual performance against criteria
• Go/no-go decision with real data

Varies by scope

Production Deployment

• Harden system for 24/7 operation
• Integrate with existing monitoring and alerting
• Implement logging and audit trails
• Supervised rollout with your team
• Documentation and knowledge transfer

Where Edge AI Makes Sense

Not everywhere. But when cloud isn't an option, we're here.

Manufacturing

Predictive maintenance, quality control, process optimization, equipment monitoring. When milliseconds matter and data is proprietary.

Energy & Utilities

Grid monitoring, demand prediction, anomaly detection. When connectivity is unreliable and decisions can't wait for cloud round-trip.

Defense & Rugged Environments

Field-deployable AI, autonomous monitoring, secure local processing. When you can't rely on connectivity and data can't leave the device.

Agriculture & Remote Operations

Equipment monitoring, process automation, yield optimization. When infrastructure is limited and human oversight is expensive.

Privacy-Critical Enterprise

Any operation where data residency matters, regulatory requirements prohibit cloud, or competitive sensitivity demands local processing.

Technical Approach

Hardware-agnostic: NVIDIA Jetson, Khadas, or your existing infrastructure
Models: CNN, LSTM, classical ML — whatever fits the problem, not the hype
Quantization and optimization for edge constraints
Integration with PLCs, SCADA, existing control systems
Secure local storage, optional encrypted sync to central systems
Monitoring and alerting appropriate to your operations

Deliverables

Deployed Edge System

Working AI on your hardware, integrated with your sensors and control systems.

Documentation & Runbooks

Architecture diagrams, operational procedures, troubleshooting guides, and maintenance instructions.

Training Data Pipeline

Infrastructure for collecting, labeling, and using new data to improve models over time.

Knowledge Transfer

Your team trained to operate, monitor, and maintain the system. Or ongoing support contract — your choice.

Investment

Hardware costs are separate — you purchase based on our recommendations. Production deployment pricing depends on scope, number of integration points, and autonomy level.

Discovery & Assessment

CHF 15,000

On-site assessment, engineer interviews, feasibility analysis, architecture recommendations, hardware specifications

Proof of Concept

CHF 40,000

Working prototype on your data, deployed on edge hardware, measured against defined success criteria

Production Deployment

Scope-dependent

Full deployment, integration, hardening, documentation, knowledge transfer. Quoted after PoC validation.

Ongoing Support

By agreement

Model updates, performance monitoring, troubleshooting, continuous improvement. As long as you need.

What We Need From You

Access to facility and equipment — no remote-only projects
Engineers who know your processes available for interviews and testing
Historical data if available, or willingness to collect baseline data
Clear problem statement — what hurts, not what's trendy
Realistic expectations about timelines and autonomy levels

When We're Not a Fit

✗If you want AI because competitors have it, not because you have a problem
✗If engineers won't be involved in the process
✗If you expect full autonomy in 3 months
✗If off-the-shelf solutions actually fit your needs — use them, they're cheaper

Have a Process That Needs Intelligence?

Let's talk. Not about AI trends — about your specific equipment, your specific problems, your specific constraints. If edge AI isn't the answer, we'll tell you.

Schedule Discovery Call