Industrial Connectivity
HMI/SCADA Add-Ins
Digital Transformation
Application Development
AI Readiness Services
AI Readiness Services: From Plant Floor to AI-Ready Data
Industrial AI only works when the data underneath it is connected, contextualized, and secure. Our AI Readiness Services get you there in six building blocks: acquire data from any OT source, enrich it with business context, organize it into a real-time Unified Namespace, expose it safely through an Industrial DMZ, preserve it in historians and lakehouses, and stream it to cloud-native pipelines for analytics and AI.
Each service stands on its own or builds on the one before it, and all six follow the same phased methodology: assess, deploy, model, validate, then document and train your team. Typical timelines are measured in days, not months.
Select a service below to see what it is, the problems it solves, and how we deliver it.
AI Readiness Services
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OT Data Acquisition
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IT/OT Contextualization
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Unified Namespace
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DMZ Deployment
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Historian & Lakehouse
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Cloud-Native Pipelines
OT Data Acquisition & Ingestion
What it is: The first step in any industrial digital transformation. We connect your PLCs, sensors, and industrial equipment to modern data platforms so every asset is visible in real time and previously isolated process data flows into a single, accessible stream.
The problem it solves: Production data trapped in proprietary equipment, a different protocol on every machine (Modbus, PROFINET, EtherNet/IP, OPC UA), legacy devices with no modern connectivity, and batch reports that arrive too late to act on. The result is blind spots, reactive decision-making, and no data foundation for analytics or AI.
How we deliver it: Using TOP Server (140+ native drivers), OPC Router, MQTT, and edge data collectors, we follow a six-phase methodology: assessment and device discovery, edge collector deployment, data mapping and tag configuration (ISA-95 or custom naming), data pipeline setup with store-and-forward and encrypted transport, testing and validation, then documentation and knowledge transfer. Typical timeline: 7–14 days from assessment to production-ready infrastructure.
Common use cases: production and OEE monitoring, quality data collection for SPC, energy monitoring, and predictive maintenance.
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IT/OT Data Contextualization
What it is: Bridging enterprise business systems (ERP, WMS, QMS, CRM) with shop-floor operations (SCADA, MES, PLCs) so production events carry business context and business decisions are informed by real-time production reality. Integration is bidirectional: production updates flow back to ERP, and machine events can trigger work orders.
The problem it solves: Disconnected data silos, hours spent manually reconciling CSV exports, downtime records with no link to the customer orders or materials affected, and business intelligence built on end-of-shift reports instead of live data.
How we deliver it: Built on N3uron, OPC Router, and enterprise data connectors (REST/SOAP APIs, ODBC/JDBC, automated file imports), the engagement runs six phases: IT/OT data source identification and mapping, integration platform deployment, data modeling and contextualization (Order + Machine + Material), integration logic and workflows with error handling, publishing enriched data to UNS topics, data lakes, and analytics platforms, then testing, validation, and documentation. Typical timeline: 12–19 days.
Common use cases: production-to-order tracking, material and inventory optimization, quality and traceability, and OEE with business context.
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Real-Time Unified Namespace (UNS)
What it is: An event-driven, publish/subscribe data fabric built on MQTT that replaces brittle point-to-point integrations with a single, real-time source of operational truth. Every system connects once to the UNS instead of integrating separately with every other system.
The problem it solves: Integration complexity that grows exponentially with each new system, stale data from polling and batch updates, conflicting copies of operational data with no authoritative view, and tightly coupled architectures where every change risks a cascading failure.
How we deliver it: We deploy enterprise MQTT brokers (cloud-managed such as AWS IoT Core, Azure IoT Hub, and HiveMQ Cloud, or on-premise such as HiveMQ, Mosquitto, and EMQX), industrial data gateways (N3uron, Ignition, HighByte, OPC Router), and the Eclipse Sparkplug B standard across six phases: broker deployment and infrastructure, namespace architecture design (ISA-95 topic hierarchy and JSON payload schemas), publisher configuration and integration, subscriber and consumer application setup, security hardening with topic-level ACLs and certificate-based authentication, then testing, documentation, and training. Typical timeline: 16–25 days.
Common use cases: real-time production monitoring, event-driven quality management, coordinated material handling, and closed-loop predictive maintenance.
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DMZ Deployment
What it is: A hardened Industrial DMZ at Purdue Level 3.5 that exposes your Unified Namespace to AI agents such as chatUNS.ai and to cloud analytics through an outbound-only, brokered boundary, while your OT network stays fully isolated.
The problem it solves: The core tension of the AI era: intelligent agents need to read live operational data, but opening inbound firewall rules into OT is the exact path ransomware and lateral-movement attacks exploit, and air-gapping the plant keeps it safe but blind. Flat networks also leave nothing defensible to show IEC 62443 or NIST 800-82 auditors and cyber-insurers.
How we deliver it: A layered stack in which each tool owns one Purdue level: TOP Server captures OPC data at Levels 1–2, N3uron contextualizes it into an ISA-95 UNS at Level 3 and publishes via MQTT Sparkplug B, a HiveMQ or Mosquitto broker in the DMZ receives it through outbound-only bridging, and chatUNS.ai subscribes read-only at Level 4. Delivered with our system integrator partners in six phases: network assessment and segmentation, TOP Server deployment, UNS modeling in N3uron, DMZ broker stand-up with mutual TLS bridging, access-control hardening with per-topic ACLs, then penetration testing, monitoring, runbooks, and hand-off. Typical timeline: 17–25 days.
Common use cases: natural-language plant insight for leadership, safe cloud analytics and cross-site benchmarking, vendor and integrator remote visibility, and IT/enterprise application integration.
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Historian & Lakehouse Connectivity
What it is: A two-tier architecture that turns ephemeral real-time data into a permanent enterprise asset. A historian preserves high-frequency OT telemetry at full resolution (typically 1–3 years), and a cloud lakehouse unifies aggregated OT data with ERP, quality, and maintenance records for analytics, AI, and compliance (typically 5–10+ years).
The problem it solves: Millisecond sensor data that gets downsampled or discarded, historical data fragmented across SCADA historians, QMS, and CMMS, archives trapped in proprietary formats or offline backups, and data scientists spending weeks stitching sources together before analysis can begin.
How we deliver it: Using industrial time-series databases (TDengine, OSIsoft PI, InfluxDB, TimescaleDB), lakehouse platforms (Snowflake, Databricks, AWS, Azure Synapse), and pipeline tools (N3uron, HighByte, Apache Kafka, OPC Router), we run six phases: historian deployment aligned to the ISA-95 equipment hierarchy, lakehouse architecture with raw, curated, and analytics-ready zones, ingestion pipeline configuration with store-and-forward, data quality and validation, analytics and visualization integration (Power BI, Tableau, Grafana, ML platforms), then documentation, training, and hand-off. Typical timeline: 17–26 days.
Common use cases: predictive maintenance from historical patterns, product quality root cause analysis, energy optimization and sustainability reporting, and process digital twin development.
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Cloud-Native Data Pipelines
What it is: Serverless, auto-scaling pipelines that move industrial data from the factory edge into AWS, Azure, or Google Cloud: managed IoT ingestion, stream processing, serverless transformation and routing, lakehouse storage, and managed analytics and AI/ML services, all on pay-as-you-go economics.
The problem it solves: On-premise servers that hit capacity limits and need costly upgrades, IT teams consumed by patching, backups, and hardware failures, months-long procurement cycles that delay time-to-insight, and no practical access to the GPU compute and ML services that modern AI workloads require.
How we deliver it: Multi-cloud by design (AWS IoT Core, Azure IoT Hub, or Google Cloud; Kinesis, Event Hubs, or Kafka; Lambda or Azure Functions; S3, Snowflake, BigQuery, or Synapse; SageMaker, Azure ML, Vertex AI, or Databricks). Six phases: cloud ingestion infrastructure with device certificates and edge gateways, stream processing pipeline configuration, serverless transformation and routing, cloud storage and lakehouse setup, analytics and ML platform integration, then monitoring, cost optimization, documentation, and training. Typical timeline: 21–31 days.
Common use cases: real-time production analytics across global sites, predictive maintenance with cloud ML, energy optimization with serverless analytics, and quality analytics with root cause analysis.
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