- AI that works where connectivity doesn't: remote sites, vehicles, offline production lines, and field devices;
- Inference optimized to meet the latency thresholds your hardware specs, quality control, safety systems, and patient monitoring require;
- Sensitive data processed on-device where regulations require it, with architecture designed around your compliance constraints;
- Cloud infrastructure costs tied to insights, not raw data volume;
- A maintained production fleet with OTA updates and monitoring across thousands of devices;
- A hybrid architecture where edge handles speed and privacy, and cloud handles long-cycle learning and coordination.
Our clients
N-iX brings AI expertise and edge capabilities
N-iX is an edge AI consulting company with a dedicated IoT development, embedded, and robotics practice built for edge AI projects. We handle the full stack from gateway architecture to on-device deployment across industrial and regulated environments.
We take a Pragmatic AI engineering approach structured around our APEX framework. Our engineers assess your edge environment, run a measured pilot on your actual hardware, and scale only what the data supports.
Our AI and ML teams work alongside embedded engineers from day one to decide what runs on the device, what moves to the cloud, and where split inference makes sense. It means you can commit to an edge AI architecture, knowing it has been tested in your specific environment before making any significant investments.
23
years of engineering experience
200
AI and data experts
60
IoT and embedded projects delivered
What our edge AI consulting company helps clients achieve
Greg Svitak
Chief Software Architect, WorkWave
What surprised me was how far the APEX framework reached beyond engineering. By month four, our business analysts, our QA team, and our developers were all running AI workflows. That's what got us into the top 5% on GenAI adoption within the EQT Group. We're still maturing our AI lifecycle, but N-iX gave us a spec-driven way of working that the rest of the organisation could pick up.
Greg Svitak
Chief Software Architect, WorkWave
Our edge AI consulting services
Edge AI readiness assessment
A structured assessment that maps your use cases against edge feasibility, identifies hardware-model fit, estimates ROI, and produces a deployment roadmap with clear decision gates.
AI model optimization for edge deployment
Quantization, pruning, and knowledge distillation to reduce model size and inference time without unacceptable accuracy loss—targeting your specific hardware: NPU, GPU, MCU, FPGA, or CPU.
Edge AI system architecture & hardware selection
N-iX architects recommend hardware and design edge AI systems across air-gapped, fully isolated environments to edge-cloud hybrid architectures with intelligent communication patterns that define when and what devices send upstream, and how the system scales from pilot to fleet.
Embedded AI & firmware development
Our embedded engineers write firmware that connects AI inference to hardware, implement real-time operating systems (RTOS) where deterministic performance is required, and integrate edge AI into existing OT environments.
Computer vision (CV), VLMs, and VLAs at the edge
Our edge AI consulting company builds on-device computer vision pipelines for defect detection, object recognition, pose estimation, safety monitoring, and quality inspection, optimized for the camera and compute constraints of your deployment environment.
We also help build and adapt Vision Language Models (VLMs) and Vision Language Action models (VLAs) for edge hardware, supporting use cases that require language-grounded visual reasoning or physical actuation with limited or, where feasible, no cloud dependency.
MLOps for edge AI deployments
At the infrastructure layer, we build the systems that support on-device AI end to end, including edge simulation, hardware-in-the-loop testing, OTA model updates, retraining pipelines calibrated to edge constraints, and rollback protocols.
At the device layer, we implement runtime capabilities that operate directly on the hardware, including local inference monitoring, drift detection, confidence tracking, and retraining triggers designed around device compute, memory, and connectivity limits.
Physical AI at the edge
We develop AI systems and inference pipelines for cobots, autonomous mobile robots, and ADAS, where perception, decision-making, and actuation must work in real time.
For intelligent systems that operate in the physical world, we build the perception, reasoning, and control logic needed to turn sensor input into safe, reliable action.
Industry-specific edge AI solutions we help you build
N-iX builds custom edge AI applications for industrial and regulated environments where off-the-shelf solutions aren't enough.
Manufacturing
- Predictive maintenance
- Real-time visual defect detection
- Energy optimization
- OT/IT integration
- Cobot and robotic assembly AI
Healthcare
- On-device diagnostics
- Wearable AI (ECG, glucose, vital signs)
- Medical imaging at the point of care
- HIPAA/GDPR-compliant architectures
Automotive & mobility
- ADAS inference pipelines
- In-cabin monitoring
- Fleet telematics AI
- V2X edge processing
- In-vehicle AI agents
- On-device voice processing (STT/TTS)
- External environment monitoring
Energy & utilities
- Remote asset monitoring (wind, solar, grid)
- Anomaly detection on SCADA-connected infrastructur
- Offline-capable edge nodes for field equipment
Logistics & supply chain
- Warehouse vision systems
- Autonomous mobile robot (AMR) and AGV AI
- Real-time damage and inventory detection
Client's success stories Case studies
Our tech expertise powering edge AI consulting
The capabilities below are the technical foundations that make an edge AI deployment work end-to-end—from data pipelines and cloud orchestration to security and on-device AI inference.
Embedded & IoT
N-iX designs and develops embedded systems, sensor integrations, and IoT architectures that give AI models a physical environment to run in.
Our approach to edge AI consulting and delivery
N-iX runs the entire arc under one engagement model: four phases, each with a defined output that the client owns.
Assess
We map your use cases against edge feasibility, benchmark model options on your target hardware class, and deliver an ROI model with go/no-go criteria.
Outcome: Evidence-based assessmen
Design
We design the edge-cloud hybrid system: where inference runs, how data is tiered, how devices connect, and how the architecture scales. We also address compliance requirements for your specific stack at this stage.
Outcome: System design your team can review, challenge, and own
Deploy
We build and optimize models for your hardware, write firmware and embedded integration, run QA on physical devices, and push the system into production.
Outcome: A single-site deployment or a phased multi-site rollout
Maintain
We implement the MLOps infrastructure that keeps deployed models accurate and up to date.
Outcome: OTA pipelines, drift monitoring, retraining triggers, rollback protocols, and performance dashboards.
Certifications and awards
Our tech partner ecosystem
Client's testimonials
Big advantage of working together with N-iX, is that we are very flexible in finding out the best possible way of achieving the goals.
I don't think there have been any Cardo software milestone in the last two years that were achieved without N-iX team. We trust N-iX to deliver product ready for production.
N-iX created a successful POC.
Top German engineering company
N-iX helped us scale our teams faster than we could have by recruiting only at our HQ. They helped us launch an alpha product more quickly, migrate an application to the Cloud, and implement our general roadmap faster.
What makes N-iX a reliable edge AI consulting partner
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ML and embedded under one roof
N-iX's AI and embedded developers work together on edge AI projects. A model that runs cleanly in Python on a cloud VM behaves differently on an ARM processor at −20°C with 256KB of RAM. Our teams know both environments and the gap between them.
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Pragmatic approach to AI
Pragmatic AI Software Engineering means every edge AI engagement starts with an assessment of latency benchmarks on your actual target hardware, accuracy vs compression tradeoffs, total cost of ownership at fleet scale before you commit to full development.
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Proprietary APEX framework for enhanced development
Our APEX (Assess, Pilot, Excel, eXceed) framework helps you make AI-augmented development faster, more secure, and aligned with organizational changes.
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We cover the full lifecycle
From initial use case validation through model optimization, hardware selection, embedded integration, and long-term MLOps—N-iX runs the entire SDLC. You don't need a separate strategy consultant, a separate development shop, and a separate DevOps team. One team, one engagement model, one accountability structure.
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Ready for physical AI
Edge AI is evolving. The layer of physical AI, where systems don't just perceive but act, requires the same embedded depth N-iX already brings to edge deployments, extended into robotics, autonomous systems, and actuation logic. We're building in that direction now, with clients in manufacturing robotics, autonomous logistics, and ADAS.
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Regulated industry experience
The use of edge AI in healthcare, energy, and other regulated industries requires compliance. N-iX designs architectures that satisfy HIPAA, IEC 62443 (industrial cybersecurity), and ISO 26262 functional safety requirements for the automotive industry. Compliance is part of the architecture, not a checkbox at the end.
Our edge AI consulting and development experts Our tech leaders
Pawel Bulowski
Director, Head of AI Consulting
Yaroslav Kisylychka
Director, Head of GenAI Productivity Practice
Mykhaylo Kohut
Solution Architect, Embedded & IoT Practice
Sergii Netesanyi
Head of Solution Group
FAQ
Edge AI consulting covers use case assessment, architecture design, and hardware selection. Edge AI development extends that into building, optimizing, and deploying the solution, including firmware, model optimization, and MLOps. N-iX covers both, depending on your needs.
Edge AI consulting services cover the strategic and technical work required to deploy AI models directly on devices rather than routing data to a cloud server. A typical N-iX engagement includes a readiness assessment (use case viability, hardware selection, ROI modeling), architectural design, model optimization for constrained hardware, embedded integration, and post-deployment MLOps planning. Every engagement starts with the assessment: we do not begin development without evidence that the architecture is viable for your environment.
Look for a partner with expertise in both AI engineering and embedded hardware. The best edge AI development company for your project will have production experience on your target hardware class, a structured assessment process before development begins, and MLOps capability to maintain deployed models at fleet scale.
Edge AI makes sense when your use case requires response times under 100–200ms, when connectivity is unreliable or unavailable, when regulatory rules restrict where data can travel (e.g., patient data under HIPAA), or when cloud inference costs become unsustainable at the volume of data your devices generate. Not every AI workload belongs at the edge—N-iX assessments identify which do.
Existing models can often be adapted for edge deployment, but they typically require optimization first. Quantization, pruning, and knowledge distillation can reduce model size by 4–10x with acceptable accuracy tradeoffs. The right approach depends on your target hardware, latency requirements, and accuracy thresholds. N-iX benchmarks your model on target hardware during the assessment phase before making any recommendation.
This is edge MLOps—the part of edge AI most enterprises underestimate. N-iX designs OTA update pipelines that push new model versions to device fleets, monitor inference quality in production, detect data drift, trigger retraining when performance degrades, and enable rollback if an update causes issues. Fully connected fleets use cloud-orchestrated pipelines; intermittently connected devices need store-and-forward mechanisms.
N-iX's primary edge AI verticals are manufacturing (predictive maintenance, visual quality inspection), healthcare (wearables, on-device diagnostics, point-of-care imaging), automotive (ADAS, in-cabin monitoring), energy and utilities (remote asset monitoring, grid anomaly detection), and logistics (warehouse vision, autonomous guided vehicles). These are the industries where edge AI delivers the most measurable value.
Contact us
Briefly outline your project or challenge, and our team will respond within one business day with relevant experience and initial technical insights.
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