Industrial IoT & Cloud AI
Manufacturing plants generate enormous volumes of operational data that has never been used for machine learning. We build the infrastructure that changes that — from OT ingestion to cloud MLOps to edge inference.
The Reality
Manufacturing plants, utilities, and industrial operations generate enormous volumes of real-time data — from sensors, SCADA systems, historians, PLCs, and production equipment. Most of it has never been used for machine learning. The barrier isn't the data. It's the infrastructure to move it, store it, and build models on it.
We build the cloud infrastructure that turns your operational data into production AI. Data lakes that ingest from your OT layer, streaming and batch pipelines that handle historian and sensor data at scale, and MLOps platforms on AWS where models get trained, versioned, and deployed — back to the edge where the decision needs to be made, or to dashboards and alerting systems where your operations teams act on the insight.
How We Engage
Three phases of an industrial AI engagement
Assess
OT Data Accessibility Assessment
Comprehensive evaluation of your SCADA systems, historians, sensor networks, and plant data infrastructure — where data lives, what format it's in, and what's needed to make it ML-ready. Most industrial organizations have years of operational data that has never been touched by analytics. We map it, assess its quality, and design the ingestion architecture to unlock it.
Design
Cloud Data Platform Design
Architecture for ingesting, storing, and transforming operational data at scale — AWS data lakes, time-series pipelines, historian connectors, and the feature engineering layer that feeds your ML workloads. We design for your data volume, latency requirements, and compliance obligations — whether that means near-real-time streaming from the plant floor or nightly batch loads from a legacy historian.
Deploy
MLOps Platform & Edge Deployment
Model training, versioning, serving, and monitoring on AWS — plus the deployment pipeline that pushes trained models back to edge hardware for local inference where cloud latency isn't acceptable. Production AI in industrial environments requires a complete loop: data in, models trained in the cloud, inference running at the edge, and a monitoring layer that detects drift and triggers retraining.
Industrial AI Playbooks
End-to-end delivery frameworks from plant data to production cloud AI
Plant Data to Cloud Data Lake
Ingesting from SCADA systems, historians (OSIsoft PI, Ignition), PLCs, and IoT sensors into a structured AWS data lake. Streaming and batch pipelines normalize, validate, and store decades of operational data in formats ready for ML training and analytics.
6–12 weeks phased delivery
Predictive Maintenance on AWS
Equipment failure prediction models trained on historical sensor telemetry — vibration, temperature, pressure, cycle counts — and served from AWS SageMaker. Retraining pipelines that keep models current as equipment ages.
8–16 weeks phased delivery
Edge Inference Deployment
Models trained in the cloud, deployed back to constrained edge hardware for real-time local inference — AWS IoT Greengrass, NVIDIA Jetson, and industrial PCs. Models run locally when connectivity is unavailable and sync results to the cloud when it resumes.
4–10 weeks phased delivery
Not sure which vertical
fits your situation?
Most of our best engagements start with a direct conversation. Tell us what you're working on and we'll point you in the right direction.