AI ML Intelligence & Predictive Analytics
Deploy predictive maintenance, anomaly detection, and autonomous workflows on Flex83—no infrastructure build required. Turn connected assets into intelligent operations through our IoT platform in 6 weeks, not 6 months.
Knowledge Hub
Unify your diverse data sources and external manuals into tailored knowledge contexts—deploying enterprise RAG models that transform fragmented operational data into instant, conversational intelligence.
Isolate your industrial data and maintenance manuals into focused asset contexts—ensuring your AI delivers highly accurate, domain-specific answers every time.
100% of your operational data, instantly unified. Connect internal databases and external asset manuals—securely ingested and ready for AI processing without building complex data pipelines.
Enterprise AI that actually knows your equipment. Deploy advanced LLMs directly against your custom contexts—transforming static operational data into instant, conversational intelligence in minutes.
ML Studio
Flex83 accelerates the journey from raw data to predictive intelligence through a fully unified ML Studio pipeline. Profile your datasets, engineer custom features, and train robust machine learning models seamlessly within one integrated environment.
Centralize your industrial data instantly. Browse, search, and select specific FlexLake tables directly within the studio—ensuring your machine learning models are trained on clean, governed operational datasets.
Automate data readiness instantly. Analyze columns, uncover hidden quality issues, and validate data integrity scores—ensuring your raw information is perfectly structured before model training begins.
Transform raw inputs with a single click. Automatically detect anomalies, drop redundant fields, and resolve data issues globally to achieve 100% data quality before machine learning extraction.
Lakehouse architecture unifies operational, analytical, and historical IoT data for seamless policy enforcement and scalable data storage.
Visualize how raw edge data evolves into actionable insight. Track provenance, see dependencies, and comply with every audit step.
Review your full training configuration, finalize model and experiment details, and launch training with confidence through a guided submission workflow built for speed and control.
Model Registry
Centralize, version, and continuously track your machine learning models across their entire lifecycle—ensuring total governance, reproducibility, and seamless deployment within a single unified registry.
Experiments & Runs
Monitor active experiments, compare model performance, and seamlessly manage predictive capabilities—from IoT anomaly detection to churn prediction—within one centralized operational view.
Registered Models
Access your library of production-ready machine learning models, manage active versions, and trigger live intelligence across your operations directly from a unified deployment dashboard.
Trusted by global enterprises you already aware of
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Head of Product, Actiontec
The FLEX83 as an Application Enablement Platform (AEP) was easily customized to handle our sophisticated and high-scale application. The IoT83 team worked with us to deliver this high-quality solution in a very short time.
Networks
IoT83 is revolutionizing the industrial IoT by enabling digital transformation with its secure & scalable platform (Flex83), its application tools, and agile services, streamlining the big data deployments in a cost-effective manner for a faster and increased ROI.
Product Lead
The software is very easy to use & intuitive. FLEX83 has been extremely helpful & committed to nVent success moving the company forward with connecting our legacy installed controller base to the cloud platform.
Electronics
IoT83's cloud platform (Flex83) enabled Vision AI applications on Renesas Virtual Lab's RZ/V series MPUs. It supports RZ/V2M and RZ/V2L evaluation boards, using standard ONNX models and a DRP-AI translator. This solution performs AI inference while delivering metrics like FPS, FPS/Watt, and Inference Time.
American Appliance, OEM
When working with Flex83, you are not just getting a world-class Application Enablement Platform (AEP), you are also gaining access to some of the brightest Dev Minds in the cloud space. It added immense value to our operations.
Niclas Anderson
VP Sales, Vitronic
Frequently Asked Questions
Do we need data scientists to use Flex83's AI capabilities?
No. Pre-built ML models work out-of-the-box for common industrial IoT use cases (anomaly detection, predictive maintenance, asset health scoring). Low-code tools enable domain experts to customize without deep expertise. Data science teams focus on differentiation—not infrastructure plumbing on your IoT platform.
How accurate are predictive maintenance models?
Pre-trained models adapt to your asset-specific patterns within 2-4 weeks through automated learning via machine learning in IoT. Performance improves as operational data accumulates—no manual retraining required.
Can Flex83 integrate with models we've already built?
Absolutely. Import models from TensorFlow, PyTorch, scikit-learn, H2O.ai, or any framework exporting to ONNX/PMML. Flex83 handles deployment, scaling, monitoring through MLOps Kubernetes and data governance. Retain full ML Model ownership. Mix platform intelligence with custom algorithms via AI data orchestration.
What's the typical time from data to AI in production?
2-4 weeks from data ingestion to production on your IoT platform. Custom ML models: 4-6 weeks including train models, validation, and testing. Most customers see measurable results within the first month.
How does Flex83 handle model degradation?
Automated data drift detection monitors prediction accuracy and model confidence through MLOps Kubernetes workflows. Shadow deployments test updated models against production traffic before cutover via over the air updates. This ensures zero disruption with full version control and rollback capability.
Can edge devices run AI models for low-latency decisions?
Yes. Flex83 supports Edge ML deployment with model compression for resource-constrained connected assets through IoT edge layered deployment. Also allows to train model centrally and deploy at edge. Models sync automatically via IoT connectivity when available.
What about AI explainability for regulated industries?
Every prediction includes SHAP-based explanations showing feature contributions and model confidence through AI data fabric. Generate compliance-ready reports satisfying FDA, EU AI Act, and ISO standards. Complete audit trails link predictions to data, machine learning model versions, and approval workflows via data lineage.
How do you prevent AI models from amplifying data biases?
Built-in fairness metrics detect bias across operational technology contexts during train model and production where ias mitigation techniques apply automatically. Regular fairness audits included in MLOps Kubernetes workflows. Full transparency into data sources, model decisions, and performance across asset management classes.
What's the pricing model—per prediction or platform license?
The AI IoT capabilities included in Flex83 platform licensing—no per-prediction fees. Compute scales with usage (train model frequency, inference volume), but you control infrastructure deployment (cloud native, cloud agnostic, on-premises) to optimize business transformation economics. Predictable costs from pilot through enterprise scale.
Can we A/B test multiple AI models in production?
Yes. Multi-armed bandit algorithms allocate traffic across model variants based on real-time data analytics. Define success metrics (accuracy, latency, business KPIs). Flex83 promotes winning models automatically with statistical significance testing. Full experiment tracking via MLOps workflows.
How does Flex83 AI compare to building custom with cloud providers?
AWS/Azure/GCP provides infrastructure—you build the application layer. Flex83 provides infrastructure + pre-built intelligence + application framework. Result: 6 weeks vs. 8-12 months deployment, 3x lower TCO via observability cost optimization, and focus on business logic, not AI plumbing.
