No. DeepIQ complements AWS rather than replacing it.

AWS provides the cloud infrastructure, storage, analytics, machine learning, IoT, and generative AI services. DeepIQ provides the industrial connectivity and contextual intelligence layer that connects operational technology, engineering information, and enterprise data to those services.

Together, DeepIQ and AWS create a more complete foundation for industrial analytics and AI.

AWS provides powerful cloud and AI capabilities, but industrial data often remains fragmented across historians, SCADA systems, PLCs, engineering drawings, maintenance applications, and enterprise systems.

DeepIQ helps organizations:

• Securely access industrial data across OT and IT environments
• Resolve inconsistent asset identities across systems
• Connect engineering drawings to live operational data
• Create reusable, governed industrial data products
• Build contextual knowledge graphs
• Ground AI applications in trusted operational context

DeepIQ makes industrial data more understandable and usable across the AWS ecosystem.

Yes.

DeepIQ can be deployed within a customer's existing AWS architecture and use the organization's preferred storage, compute, security, and governance services.

Customers can continue using their established AWS accounts, data lakes, networking standards, access controls, and operating models without introducing a separate proprietary cloud environment.

No.

DeepIQ is designed around customer-owned data and can execute within the customer's infrastructure. Industrial data can remain in the customer's AWS environment, on-premises systems, edge infrastructure, or other approved platforms.

DeepIQ provides connectivity, orchestration, transformation, and context without requiring customers to surrender control of their data.

DeepIQ can support architectures that use services such as:

• Amazon Simple Storage Service
• AWS IoT Core
• Amazon Elastic Kubernetes Service
• Amazon Bedrock
• Amazon SageMaker
• Amazon Redshift
• Amazon Athena
• AWS Glue
• Amazon QuickSight
• AWS Identity and Access Management
• AWS security and monitoring services

The specific architecture depends on the customer's existing environment, use case, security requirements, and preferred AWS services.

Yes.

DeepIQ Reveal is designed for segmented, restricted, and air-gapped industrial networks. Its edge execution capabilities can connect to industrial systems locally, process requests securely, and support controlled data delivery to AWS when permitted.

The architecture can support store-and-forward operation, buffering, retries, and environments where continuous cloud connectivity is unavailable.

DeepIQ can be deployed without exposing inbound access to protected OT systems.

Reveal executes within the customer's environment and can use security patterns aligned with existing network segmentation, Purdue Model architecture, DMZ controls, and enterprise cybersecurity policies.

The final deployment design is established with the customer's OT, IT, cloud, and security teams.

DeepIQ can connect operational and enterprise sources such as:

• AVEVA PI System
• AspenTech IP.21
• Honeywell systems
• PLCs and SCADA platforms
• Relational databases
• File-based systems
• Engineering systems
• Enterprise applications

Supported protocols and interfaces include:

• OPC UA
• OPC HDA
• MQTT
• REST APIs
• WITSML
• ODBC
• JDBC

Through industrial connectivity technologies such as Matrikon OPC, DeepIQ can also access a broad range of additional control systems and industrial devices.

Yes.

DeepIQ supports batch, historical, near-real-time, and streaming industrial data workflows.

Organizations can use DeepIQ to ingest decades of historian data, process live operational signals, combine them with enterprise records, and deliver governed outputs to AWS applications, analytics, and AI services.

DeepIQ can extract historical and real-time data from industrial historians, transform and validate it, and deliver it into the customer's AWS architecture.

The process can preserve important operational information such as:

• Asset hierarchies
• Tag relationships
• Engineering metadata
• Calculation logic
• Time-series quality
• Data lineage and provenance

This helps organizations modernize historian data without reducing it to disconnected time-series values.