Industry AI

How Palantir AIP Enables the Unified Namespace

Amid geopolitical competition and the rise of Industry AI, the adoption of a Unified Namespace (UNS) is a strategic necessity. American industries — from aerospace to biotechnology — can secure a critical advantage with UNS, enabling manufacturers to leverage the full capabilities of AI and machine learning. This shift from reactive to proactive operations marks a significant leap towards more intelligent manufacturing.

Industry AI encompasses a broad range of technologies, with a UNS being a crucial piece focused on handling data. It requires three core elements:

• Data: A UNS aggregates data from all your sources (e.g., sensors, machines, and databases) to enable real-time access to information and serves as the foundation for smarter operations.
• Logic: Sophisticated algorithms and AI are needed to process and analyze this data, turning it into actionable intelligence for predictive maintenance and better quality control.
• Action: Closing the loop through automated responses, such as maintenance procedures or production adjustments, integrating decision-making directly into the workflow.

These elements must be integrated and governed in a coherent architecture that works across human teams, AI agents, devices, and environments.

The UNS Architecture

A Unified Namespace is a consolidated system for naming and organizing entities, concepts, and relationships across various data sources and systems. It provides a common language for referencing and integrating data, simplifying management, analysis, and sharing. Each entity or concept in a UNS is assigned a unique identifier and a standardized name, consistent across all systems. This removes ambiguity and ensures easy mapping and integration of data from different sources.

Example:
In many manufacturing plants, the UNS often follows the ISA-95 standard, offering a hierarchical structure:

Enterprise/Site/Area/Line/Cell

spBv1.0/NewCo/Site1/Assembly/Line1/xxx
spBv1.0/NewCo/Site1/Assembly/Line1/xxx
spBv1.0/NewCo/Site1/Quality/Inspection1/xxx
spBv1.0/NewCo/Site1/Inventory/Storage1/xxx

Creating a UNS in a manufacturing plant involves communication protocols, data integration, data storage and management, and analytics. Key components include:

• Real-Time Streaming Data: Using protocols such as OPC-UA, MQTT, and Kafka to capture live data from IoT devices, sensors, and Programmable Logic Controllers (PLCs). This requires structuring topics and payloads to fit with the desired model (e.g., ISA-95). For example, Sparkplug B provides a standardized topic namespace and payload format tailored for MQTT.
• Non-Streaming Data: For MES, ERP, and PLM systems that store data in RDBMS, the following strategies are typically employed:
• Scheduled Data Polling: Regularly querying databases to extract and push data into the UNS.
• Change Data Capture (CDC): Capturing database changes in real-time and streaming them to the UNS.
• Data Virtualization: Offering a unified view of data across multiple sources without physically moving it to the UNS.

Beyond a Unified Namespace

Palantir AIP enhances the Unified Namespace by adding a sophisticated layer of logic and action to the data-centric foundation, forming a decision-centric ontology. This enriched UNS framework not only organizes diverse data sets but also empowers decision-making through advanced analytics and AI. The result is a dynamic, responsive manufacturing environment with predictive maintenance and real-time operational adjustments becoming standard protocol.

1. Connect & Integrate Data

Palantir AIP offers AI-powered data pipelining to accelerate data integration and ensure information flow from the edge to the core:

• Streaming & Batch Connectivity: Supports protocols such as OPC-UA, MQTT, and Kafka, along with standard JDBC/SQL connectors for real-time and scheduled data integration.
• Virtualized Connectivity: Interfaces with data lakes, warehouses, and other storage solutions, making data accessible and actionable without physical consolidation.
• Bespoke Connections: Custom API and file export options cater to unique system requirements, enhancing UNS adaptability.
• Decision Write-back: Enables the write-back of insights and decisions to originating systems, closing the loop for continuous improvement.

Automated and code/low-code/no-code options support multi-modal data integration, harmonizing and contextualizing data in real-time.

2. Create Your AI-Powered Ontology

Ontology building within Palantir’s AIP UNS is crucial for creating an intelligent system that supports human and AI decision-making. It connects Data, Logic, and Action into a business-specific representation, allowing both humans and AI to interact with business operations without needing deep knowledge of data technologies or programming languages.

Follow these steps to build an effective ontology for your UNS.

Data

• Map Object Types: Identify core entities relevant to your operations (e.g., “Machine,” “Part,” “Product,” “Order,” “Customer,” “Maintenance Schedule”). Define each entity with clear, concise, and consistent attributes and characteristics.
• Map Relationships: Determine how these entities relate (.e.g., a “Product” consists of multiple “Parts;” a “Maintenance Schedule” applies to specific “Equipment”). Specify the relationship attributes, including type (hierarchical, associative), cardinality (one-to-one, one-to-many), and governing constraints or rules.

Logic

• Integrate Existing Logic Sources: Incorporate logic from systems such as PLCs, ERPs, and MESs, ensuring alignment with your UNS objects’ API for seamless cross-platform interactions.
• Build AI-Powered Functions: Build, test, and release feature-rich, AI-powered functions — for example, to predict the probability of a machine failure or identify root cause of an alarm.
• Capture Feedback: Use decision outcomes to refine logic. Implement feedback mechanisms and automate model updates.

Action

• Define Actions: Represent enterprise actions (e.g., “manufacture,” “ship,” or “inspect”) in a configurable manner.
• Orchestrate Decisions: Create interfaces to coordinate decisions across systems, such as triggering actions in ERPs, SCMs, edge systems, or SaaS tools based on UNS-derived insights.

3. Build AI-Powered Apps & Automations

Once the ontology is established, integrate and develop models to leverage structured data and logic for actionable insights. By focusing on ontology building and model integration, you can enhance your UNS within Palantir AIP for better decision-making and a more intelligent manufacturing operation.

Build AI Apps and Automations. Ship to Any Environment.

• Build AI Apps: Build AI apps with AI-powered data pipelines, AI-powered functions, and a complete developer toolchain that leverages your centrally-managed ontology.
• Enterprise Automation: Integrate classical AI/ML models — like anomaly detection models, optimizers, and forecasts — alongside Gen AI within applications to power automations and agents. AIP provides granular controls on which people, agents or automations can take operational action, with safeguards like action simulations, action reviews, and hand-off functions.
• Deploy and Manage Apps across Environments: AIP empowers both third-party and internal developers to ship, manage, and version AI apps in any environment — whether on-premise, at the near edge, or at the far edge.

Enable AI-powered Workflows on Day 1.

• PLC Exploration: Enable comprehensive analysis and visualization of PLC code to optimize industrial processes (e.g., root-cause analysis of alerts, available sensor data).
• Anomaly Detection: Identify unusual patterns in time-series data using LSTMs and other techniques to ensure operational efficiency.
• Predictive Maintenance: Use models to predict future trends and behaviors from historical data for forecasting demand, failure prediction, and proactively scheduling maintenance, thereby minimizing downtime.
• Technician Assistant: Provide real-time support and guidance to technicians using unstructured data (manuals, repair procedures), historical data, ML models, and k-LLMs to provide high-confidence recommendations.
• Maintenance Scheduling: Optimize the planning and timing of maintenance activities to ensure continuous production and resource efficiency.
• Remote Operations: Monitor and control of manufacturing processes from any location, ensuring flexibility and responsiveness as you move to higher levels of autonomy:
• Manual Operations: Human operators control all processes manually.
• Assisted Operations: Machines assist humans with basic functions, but humans still make final determinations.
• Semi-Automated Operations: Machines perform specific tasks autonomously, but humans monitor and intervene when necessary.
• Highly Automated Operations: Machines operate with minimal human intervention, performing complex tasks with advanced decision-making capabilities.
• Fully Autonomous Operations: Machines perform all tasks independently, with humans only overseeing the process and intervening in rare cases.

Industry AI in Action

Having explored AIP’s foundational principles and how they enable a transformative UNS for manufacturing operations, the following case study illustrates AIP’s capabilities in a real-world scenario and its impact on operational efficiency and decision-making.

Case Study: Optimizing Vehicle Manufacturing with Palantir AIP’s Unified Namespace

Let’s explore a manufacturing workflow within the defense industry. One of the pivotal processes when assembling armored vehicle chassis is the precise application of welds. These welds are essential for the structural integrity and durability of the vehicles, ensuring they can withstand extreme operational conditions and provide safety to military personnel. Meticulous attention to detail and advanced quality control measures are necessary to ensure that each weld meets stringent standards. Let’s delve into how Palantir AIP can enhance this process.

Data Connectivity & Integration

Sensor Data & Streaming: Palantir’s AIP collects real-time data from sensors, using MQTT topics to monitor various parameters. It tracks the temperature of the welding arc (e.g., Facility1/Production/Line1/Weld/Temperature), measures the weld speed (e.g., Facility1/Production/Line1/Weld/Speed), and ensures the weld seam alignment (e.g., Facility1/Production/Line1/Weld/Alignment). This data stream enables continuous monitoring and optimization of the welding process.

MES & Database Synchronization: Palantir’s AIP employs JDBC connectors and Change Data Capture (CDC) techniques to synchronize data from the Manufacturing Execution System (MES) with databases. This allows for timely data integration, ensuring that all relevant information is up-to-date and readily accessible for decision-making and process improvement.

Unstructured Data Ingestion: The platform ingests and structures diverse data types, including PLC logic, engineering schematics, and welding procedure specifications. By utilizing Optical Character Recognition (OCR) and Large Language Models (LLMs), Palantir’s AIP UNS can extract valuable insights from these unstructured data sources, which can then be leveraged to enhance the vehicle assembly process.

Ontology Building

Defining Classes and Relationships: Palantir’s AIP establishes a semantic framework by creating classes such as Sensor Properties, Inspection Image, and Engineering Manual, while specifying their attributes and relationships. For example, it defines time-series properties for sensor data and establishes linkages between batches and specific production runs.

Contextualization: The platform enhances data interpretation by contextualizing the information within the ontology. Ingest pipelines support the creation and linking of objects, such as Batch and Operator, providing a clearer understanding of the production process. Welding procedure specifications are analyzed and made actionable through traditional techniques and LLM exploitation, enabling more informed decision-making. Manuals and documents are parsed using OCR or vision models, with chunking/vector embedding for efficient retrieval and augmented generation, making relevant information easier to access and utilize. Media sets and metadata for inspection images are generated to assist in quality control and process monitoring, ensuring the welding process remains consistent and effective.

Model Integration & Development

Advanced Analytics & AI: Palantir’s AIP integrates advanced analytics and AI, utilizing both off-the-shelf and custom-built analytics within automated workflows for real-time decision-making.

• Multi-modal ML Model: Analyzes sensor data and inspection images to detect anomalies, ensuring precise welding. The model takes inputs such as temperature, speed, alignment, and seam quality, producing outputs such as anomaly type and score.
• Trend Analysis: Monitors welding parameters over time, comparing current trends with historical data to identify deviations. This aids in the prediction and prevention of potential issues.
• Visual Inspection Analysis: Employs image processing and machine learning to identify defects in the welds. It compares findings against engineering guidelines to ensure quality standards are met.

Closing the Loop with Decision Support

Decision Aid Interface: The Decision Aid interface presents actionable insights and detected anomalies to operators, facilitating a symbiotic interaction between human operators and AI, thereby enhancing decision-making.

• Actionable Insights: The Decision Aid interface in Palantir’s AIP UNS presents actionable insights and detected anomalies to operators, facilitating a coordinated interaction between human operators and AI, which enhances decision-making.
• Image Inspection Alerts: Notifies operators about issues detected during visual inspection, such as “Image inspection detects weld porosity.”
• Anomaly Alerts: Highlights critical deviations in real-time, such as “Weld temperature and speed are outside acceptable range,” allowing for immediate corrective action.
• Trend Analysis Insights: Provides insights like “Increasing trend in seam misalignment. Temperature fluctuations detected,” predicting future maintenance needs.
• Recommendations from Analytics: Offers guidance based on analysis, such as “Recommend welding nozzle replacement during next available window.”
• Guidance & Action: Provides step-by-step instructions for immediate repairs or scheduling maintenance, optimized through dynamic scheduling algorithms to ensure efficient use of resources and time.

By equipping operators with timely and relevant insights, the AIP-enabled UNS enables swift and informed corrective measures. This safeguards the integrity and optimizes the efficiency of the welding process in armored vehicle assembly.

This practical example illustrates the application of AIP’s capabilities in a real-world scenario, demonstrating the platform’s ability to deliver a dynamic UNS impact on operational efficiency and decision-making processes.

For further information or to explore how Palantir AIP can be leveraged within your organization, please reach out to our team.

Palantir AIP: https://www.palantir.com/platforms/aip/

Explore applications & builder starter packs: https://aip.palantir.com/workflow/d8d7117b-57de-41be-aba3-2955aa525c24

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Industry AI was originally published in Palantir Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.