AI-Driven Production Visibility for Aluminum Manufacturing

AI and RFID for aluminum production combines Industrial AI, machine learning, connected sensors, and RFID identification to improve how aluminum plants track materials, work-in-process, production assets, and finished products. The approach connects physical material identities with process data from casting, homogenization, hot and cold rolling, extrusion, heat treatment, finishing, inspection, and shipping. RFID can identify ingots, billets, slabs, coils, bundles, containers, tooling, and production carriers, while AI analyzes movement patterns, production history, quality records, equipment conditions, and inventory behavior. This creates a more reliable operational record than manually entered location and status information. For aluminum producers, the value is not simply knowing where a material is. The objective is to associate each material with its production route, furnace or line, processing state, quality results, storage location, and downstream destination so production, quality, maintenance, warehouse, and shipping teams can make decisions using consistent operational data.

RFID-Based Material Visibility in Aluminum Production

The completed visual shows how RFID identification follows aluminum materials through casting, rolling, heat treatment, finishing, inspection, warehouse storage, and shipment while connecting physical identification events to edge processing, plant networking, AI-based production software, and operator workstations.

What AI and RFID Mean for Aluminum Production

AI-enabled RFID solutions address a specific operational problem in aluminum manufacturing: physical materials move continuously through processes, yards, staging areas, furnaces, rolling lines, finishing operations, warehouses, and shipping zones, while their digital records must remain synchronized with those movements.

RFID provides machine-readable material identification. AI provides the analytical layer that determines what those identification events mean when combined with production, quality, equipment, and enterprise data.

For example, an RFID event showing that a coil entered a finishing area becomes more useful when AI can associate that event with the coil’s alloy designation, heat or cast number, rolling history, target customer specification, inspection status, and scheduled order. The resulting information can support production sequencing, exception detection, inventory reconciliation, and shipment readiness.

Connecting Material Identity With Production Context

Aluminum production involves multiple transformations of the same material. A cast product may become a slab, billet, coil, extrusion stock, finished profile, sheet, plate, or fabricated component. Maintaining genealogy across these transformations is therefore more complex than tracking a conventional warehouse item.

An AI-enabled RFID system can associate identifiers and process events with:

• Heat, melt, cast, lot, and batch information
• Alloy grade and temper designation
• Slab, billet, coil, sheet, plate, extrusion, and finished-product identities
• Furnace loading and unloading events
• Homogenization and heat-treatment cycles
• Rolling mill entry and exit events
• Extrusion press assignments
• Finishing and cutting operations
• Inspection and laboratory test results
• Scrap, rework, quarantine, and release states
• Warehouse locations and staging areas
• Customer order and shipment references

The engineering objective is not to place an RFID tag on every object regardless of its physical characteristics. Metal creates challenging RF conditions, particularly when tags are mounted directly on conductive surfaces or when large coils, bundles, racks, and machinery create RF reflections. Tag selection, mounting position, reader placement, antenna orientation, read-zone design, shielding, and event filtering must therefore be engineered for the specific aluminum plant.

GAO supplies RFID hardware products and systems that can be incorporated into material identification, location monitoring, and production traceability applications. The appropriate RFID configuration depends on the material form, operating temperature, mounting surface, read distance, environmental exposure, and required identification rate.

High-Value AI Applications Across Aluminum Production

AI becomes particularly useful when RFID events are combined with production schedules, MES records, quality information, equipment telemetry, warehouse data, and operator actions.

AI Material Tracking and Aluminum Product Traceability

AI can continuously analyze RFID events to determine whether aluminum materials are following their expected production routes. A billet expected to move from casting storage to homogenization and then to extrusion can be compared against actual movement events.

The system can identify:

• Unexpected material movement
• Missing process transitions
• Duplicate or conflicting identification events
• Materials remaining too long in staging
• Materials entering an incorrect production zone
• Unreleased material approaching a downstream process
• Material associated with an incorrect production order
• Inventory records that differ from physical movement

This is particularly useful where forklifts, cranes, transfer vehicles, and operators move materials between process areas.

AI Aluminum Inventory Management

Aluminum inventories can represent substantial working capital because plants may hold raw materials, alloying inputs, semi-finished stock, work-in-process, finished coils, billets, sheets, plates, extrusions, packaging materials, and maintenance spares.

RFID provides physical identification events while AI analyzes consumption, production schedules, storage duration, replenishment patterns, and order requirements.

AI inventory models can support:

• Coil and billet availability forecasting
• Slow-moving material identification
• Storage-area utilization analysis
• Production-side material shortages
• Finished-goods allocation
• Safety-stock optimization
• Replenishment forecasting
• Inventory discrepancy detection
• Staging-area congestion prediction

The resulting inventory picture is more operationally useful than a static stock count because it incorporates material location, production state, demand, and expected movement.

AI Production Sequencing and Material Flow

Production scheduling in aluminum plants can be affected by alloy changes, customer specifications, furnace availability, rolling requirements, die or tooling constraints, heat-treatment capacity, inspection requirements, and downstream orders.

AI can evaluate RFID-derived material positions against production schedules to identify sequencing conflicts before they interrupt production.

For example, if a required billet has not reached the extrusion staging area while another alloy is already positioned for processing, AI software can identify the risk and notify production planners. Similar logic can be applied to rolling schedules, finishing lines, heat-treatment queues, and shipping staging.

AI Quality and Process Genealogy

Quality data becomes more valuable when it is connected to a persistent material identity.

RFID events can associate a coil, billet, slab, or finished bundle with:

• Chemical composition
• Mechanical test results
• Surface inspection results
• Dimensional measurements
• Furnace or heat-treatment records
• Rolling parameters
• Extrusion process data
• Inspection status
• Nonconformance records
• Rework history

AI can then identify relationships between process conditions and recurring quality deviations. The objective is not to replace metallurgical engineering or quality-control procedures. Instead, AI helps engineers search large volumes of production history and identify patterns that warrant technical investigation.

AI-Enabled Aluminum Material Genealogy

The completed diagram illustrates how a persistent aluminum material identity connects production, RFID movement, quality, inspection, and system records across melting and casting, slab or billet identification, homogenization, rolling or extrusion, heat treatment, finishing, warehouse storage, and shipment. An AI analytics layer identifies genealogy gaps, process anomalies, and quality trends and provides recommendations for human review.

Aluminum Production Workflows Supported by AI and RFID

A practical deployment begins with the physical movement of aluminum materials and then establishes a reliable digital event chain.

Data Acquisition at Production and Storage Points

RFID readers capture identification events when tagged materials or carriers enter defined zones. Depending on the process, fixed readers may be installed near production transfer points, warehouse entrances, staging lanes, loading areas, or controlled storage locations.

Handheld RFID readers can complement fixed infrastructure when operators need to locate individual materials or reconcile an area manually.

Additional data sources can include:

• Furnace temperature and cycle records
• Rolling mill process parameters
• Extrusion press conditions
• Heat-treatment data
• Crane and forklift telemetry
• Barcode and QR identification
• Machine status signals
• Weight and dimensional measurements
• Environmental sensors
• Quality inspection systems
• MES and ERP transactions

RFID should therefore be treated as one operational data source within the aluminum production information flow rather than as an isolated tracking system.

Edge Processing and Event Validation

Raw RFID reads should not automatically become business transactions. Aluminum plants can generate duplicate reads when a tagged material remains inside a reader’s field or when multiple tags are visible simultaneously.

An edge computer or industrial gateway can filter duplicate reads, apply reader-zone logic, timestamp events, validate tag identities, and associate reads with production locations.

Event processing may determine that:

• A tag has entered a furnace staging zone
• A coil has departed a rolling area
• A billet has arrived at extrusion staging
• A finished bundle has entered quality hold
• A released product has moved into shipping
• A material has crossed an unexpected zone boundary

This local processing reduces unnecessary data transmission and allows time-sensitive decisions to continue even when connectivity to a central server is temporarily unavailable.

AI Analysis and Operational Decisions

Validated events can be sent through middleware to production and enterprise systems. AI models then analyze current events alongside historical and planned information.

Typical outputs include:

• Material-location confidence
• Route deviation alerts
• Inventory discrepancy alerts
• Production delay predictions
• Queue and staging analysis
• Shipment readiness status
• Quality genealogy exceptions
• Material dwell-time anomalies
• Demand and replenishment forecasts

The final decision remains tied to an operational workflow. A production planner may resequence work, a warehouse supervisor may locate a missing coil, a quality engineer may place a batch under review, or a shipping team may verify that the correct finished bundle has reached the loading zone.

AI and RFID Data Flow for Aluminum Production

The completed visual shows how RFID-tagged aluminum materials and carriers generate identification events that move through fixed and handheld readers, industrial edge gateways, event processing, secure connectivity, middleware, and enterprise systems including MES, ERP, WMS, QMS, and CMMS. AI/ML analytics then transforms these validated events into alerts, recommendations, dashboards, and operational decisions across factory and cloud deployment paths.

RFID Hardware, AI Software, and Industrial Connectivity

A production-grade solution combines identification hardware with software that understands aluminum manufacturing workflows.

RFID Selection for Aluminum Materials

UHF RFID is generally useful where longer read ranges and automated zone detection are required, such as warehouse transitions, staging areas, shipping lanes, and material transfer points. However, metal-rich environments require careful tag and antenna selection.

HF RFID can be appropriate for shorter-range controlled identification, particularly where deliberate operator interaction is acceptable. LF RFID is generally associated with shorter-range identification and specialized industrial applications where its propagation characteristics and operating requirements fit the use case.

For aluminum production, selection should consider:

• Direct exposure to metal surfaces
• Tag mounting method
• Required read distance
• Material dimensions and geometry
• Temperature exposure
• Outdoor yard conditions
• Water, dust, oil, and contamination
• Reader-to-reader interference
• Crane and forklift movement
• Required read reliability
• Tag durability through processing and handling

A pilot should test the actual tag, mounting configuration, reader, antenna, material form, and plant environment before large-scale deployment.

AI, Edge Computing, and Industrial Software

AI models can operate on edge servers, factory servers, private data centers, or cloud infrastructure depending on latency, connectivity, cybersecurity, and data-governance requirements.

Common software components include:

• RFID event management software
• Edge processing services
• MES integration
• ERP integration
• WMS integration
• QMS and genealogy integration
• CMMS integration
• Production databases
• AI/ML analytics
• Operational dashboards
• Alert and workflow software

GAO’s experience supplying RFID, BLE, and IoT hardware products and systems supports deployments where physical identification needs to be connected with software and operational processes rather than deployed as a standalone reader network.

Cloud Version and Server Version

A Cloud Version can centralize RFID events, AI analytics, dashboards, and integration services across multiple aluminum plants. It is suitable when corporate teams need consolidated visibility across plants and when secure connectivity to cloud services is acceptable.

A Server Version can run on factory servers, customer-managed servers, private data centers, or edge computing infrastructure. This approach can be preferable when production networks require greater local control, when operational data should remain within privately managed infrastructure, or when plant-floor applications must continue operating during intermittent external connectivity.

A hybrid implementation can place time-sensitive RFID event processing and critical workflows near production while sending selected historical data to centralized analytics.

Engineering Considerations Before Plant Deployment

A successful implementation depends on the physical behavior of RFID in the plant as much as on software functionality. Metal surfaces can detune conventional tags, cranes can create changing RF environments, and dense storage can make individual material identification difficult.

Engineering teams should validate the following before commissioning:

• RFID tag performance on the exact aluminum material or carrier
• Reader and antenna positioning at transfer points
• Read-zone containment to prevent unintended reads
• Reader interference and antenna polarization
• Material stacking configuration
• Forklift and crane operating paths
• Temperature limits near furnaces and heat-treatment equipment
• Network availability across production and warehouse areas
• Edge-server capacity and redundancy
• MES, ERP, WMS, and QMS interface requirements
• RFID event naming and timestamp standards
• Cybersecurity controls for plant networks
• Failure handling when readers or network links become unavailable
• Manual recovery procedures for missed or ambiguous reads

The most reliable implementations define the operational event model first, then select hardware and software around those requirements. This prevents a common deployment failure in which a plant installs readers successfully but lacks the event logic needed to turn RFID reads into trustworthy production information.

AI-Enabled Traceability, Inventory, and Production Control

The strongest operational value appears when RFID-derived events are combined with the production records already maintained by an aluminum plant. A reader event by itself only establishes that an identifier was detected. AI can turn that event into an operational signal by comparing it with production orders, expected process routes, material genealogy, quality status, and inventory requirements.

Aluminum Coil and Billet Traceability

Traceability becomes particularly important when a production facility handles multiple alloy grades, tempers, customer specifications, and production orders simultaneously.

An AI-enabled traceability system can maintain relationships between:

• Melt and cast identifiers
• Slabs and billets generated from a cast
• Coils produced by a rolling campaign
• Extrusion billets and resulting profiles
• Heat-treatment batches
• Finished bundles and shipping units
• Customer orders and delivery destinations

AI can identify genealogy gaps when a downstream record does not correspond with the expected upstream material relationship. This provides quality and production personnel with a faster way to investigate discrepancies without manually reconstructing material histories from disconnected spreadsheets, paper records, and system transactions.

Production WIP and Dwell-Time Analysis

Work-in-process can accumulate between casting, heat treatment, rolling, finishing, inspection, and warehouse operations. Excessive dwell time may indicate scheduling problems, equipment constraints, inspection bottlenecks, material shortages, or incorrect staging.

RFID movement events provide timestamps and location transitions. AI can calculate material dwell time by process and compare current behavior with historical operating patterns.

Examples include:

• Billets waiting beyond the expected extrusion window
• Coils remaining in a staging area after rolling
• Finished material waiting for inspection
• Released products occupying shipping lanes
• Quarantined material incorrectly entering production staging
• Material transfers that bypass expected process zones

These findings can help production planners distinguish a genuine process constraint from a temporary material-location problem.

AI-Assisted Inventory Reconciliation

Physical inventory reconciliation is challenging when coils, bundles, billets, containers, and production carriers are distributed across large yards and warehouses.

An AI system can compare expected ERP or WMS inventory with RFID-derived physical events. Rather than simply reporting that two quantities differ, it can identify likely causes such as:

• Material moved without a corresponding transaction
• Duplicate system transactions
• Incorrect storage-zone assignment
• RFID reads associated with the wrong process area
• Material awaiting quality release
• Unrecorded shipment staging
• Tags requiring inspection or replacement

This approach reduces the amount of manual searching required by warehouse and materials teams.

GAO can support these applications with RFID readers, tags, antennas, handheld equipment, and related IoT hardware products and systems selected around the plant’s operating conditions.

AI for Aluminum Quality, Maintenance, and Safety

Material tracking is only one component of an AI-enabled aluminum manufacturing system. The same operational data can support quality investigation, equipment reliability, and safer movement of materials.

Process and Quality Anomaly Detection

AI can correlate material identities with production parameters to identify unusual combinations that deserve engineering review.

For example, an analytics model may identify that a particular group of coils has a higher rate of surface defects after passing through a particular processing route. A quality engineer can then examine rolling parameters, equipment conditions, material chemistry, inspection results, and production history.

Possible AI methods include:

• Statistical anomaly detection
• Classification models
• Regression analysis
• Time-series analysis
• Clustering
• Predictive models
• Root-cause correlation analysis

AI should provide evidence and prioritization rather than automatically declaring a metallurgical root cause without engineering validation.

Equipment and Material Interaction

Production assets such as rolling mills, extrusion presses, furnaces, cranes, conveyors, cooling systems, and finishing equipment generate operational data that can be analyzed alongside material movement.

A coil that repeatedly experiences a production delay at a specific process can be correlated with equipment operating conditions. Similarly, maintenance teams can examine whether certain production campaigns correspond with abnormal equipment loading or repeated stoppages.

RFID contributes the material identity and location dimension, while machine sensors contribute equipment-condition data. AI connects the two perspectives.

Safer Material Movement

Aluminum plants contain heavy coils, slabs, billets, bundles, cranes, forklifts, and other mobile equipment. AI-assisted location information can support operational safety when integrated with appropriate plant safety systems.

Potential applications include:

• Detecting material movement into restricted zones
• Identifying unexpected forklift or carrier routes
• Monitoring high-traffic material staging areas
• Supporting crane-zone awareness
• Detecting prolonged material presence in designated exclusion areas
• Correlating personnel movement with material-handling operations where permitted

Safety functions should use appropriately engineered safety controls, certified equipment, and established plant procedures. RFID-derived information should not be treated as a substitute for dedicated machine safety systems where functional safety is required.

Integration With MES, ERP, WMS, QMS, and CMMS

AI and RFID deployments deliver limited value when identification data remains isolated from the software used by production and business teams. Integration should therefore be designed around business events rather than simply transferring raw RFID reads.

MES Integration

MES software can receive validated material movement events and associate them with production orders, process steps, work centers, and production status.

Relevant transactions can include:

• Material arrival at a work center
• Production start and completion
• Material transfer between process areas
• WIP status changes
• Production-order association
• Scrap or rework events
• Quality-hold transitions

ERP and WMS Integration

ERP systems can provide order, procurement, inventory, and customer information. WMS software can manage warehouse locations, storage assignments, picking, staging, and shipment preparation.

AI can use information from these systems to determine whether physical material movement corresponds with expected business transactions.

QMS and CMMS Integration

QMS integration allows material identity to remain associated with inspection, nonconformance, corrective-action, and release information.

CMMS integration can provide maintenance records for equipment involved in material processing. This creates opportunities to investigate relationships between equipment conditions, production campaigns, material movement, and quality events.

Integration can use APIs, MQTT, HTTPS, OPC UA, database interfaces, middleware, or other plant-approved methods depending on existing systems and cybersecurity requirements.

AI and RFID Integration With Aluminum Manufacturing Systems

The completed diagram shows how RFID readers and edge gateways connect physical aluminum material identification with RFID event software, AI analytics, MES, ERP, WMS, QMS, CMMS, production historians, laboratory systems, shipping systems, and operator dashboards across casting, rolling, extrusion, heat treatment, finishing, warehouse, and shipping operations.

Cybersecurity and Reliability for Aluminum Production RFID

Connecting RFID infrastructure to production software creates an additional OT and IT security consideration. The system should be designed so that compromised identification devices cannot become an uncontrolled path into production networks.

Practical controls include:

• Segmentation between RFID devices, plant networks, and business systems
• Role-based access control
• Strong authentication for administrative functions
• Encrypted communication where supported
• Secure API authentication
• Firmware and software lifecycle management
• Network monitoring
• Centralized logging
• Device inventory and configuration management
• Backup and recovery procedures
• Controlled remote support access
• Audit trails for material-status changes

Industrial reliability also requires planning for component failure. A reader may lose connectivity, an antenna may be damaged, a tag may become unreadable, or a network segment may temporarily fail.

A robust solution should therefore define what happens when an identification event is missing. Edge software can temporarily retain events, operators can use handheld readers for recovery, and synchronization procedures can reconcile delayed events after connectivity is restored.

GAO Group, consisting of GAO, GAO Research Inc., and GAO Tek Inc., has served customers across the United States and Canada for three decades, including Fortune 500 companies, major R&D organizations, universities, and government agencies. GAO maintains stringent quality-assurance processes and provides technical support remotely or onsite, supporting deployments where reliability and engineering assistance are important considerations.

Deployment, Commissioning, and Optimization

A technically sound aluminum production deployment should proceed through controlled engineering stages rather than attempting plant-wide installation immediately.

Site Survey and Use-Case Definition

The first stage should document the physical and digital operating environment.

Engineering teams should identify:

• Material forms requiring identification
• Process transfer points
• Storage and staging locations
• Expected read ranges
• RF-obstructed or RF-sensitive areas
• Temperature and contamination conditions
• Crane and forklift paths
• Existing barcode and identification procedures
• MES, ERP, WMS, QMS, and CMMS interfaces
• Required production and inventory KPIs

The site survey should also document where RFID is unsuitable and where barcode, vision, sensor, or manual confirmation methods may provide better results.

RFID Pilot and RF Validation

A pilot should use representative aluminum products rather than laboratory test objects. Testing should include actual coils, billets, slabs, bundles, carriers, racks, and handling equipment.

Key measurements include:

• Read reliability
• Read-zone accuracy
• False-read frequency
• Tag durability
• Reader coverage
• Reader interference
• Event latency
• Network availability
• Identification accuracy during material movement

The pilot should test normal production conditions, including stacked materials, forklift traffic, crane movement, and peak operating periods.

AI Model Validation

AI models should be validated against known production outcomes. Historical records can be used to establish baseline behavior, while live testing determines whether the model generates useful alerts without excessive false positives.

Production, quality, warehouse, and maintenance specialists should participate in validation because an analytically correct signal may still be operationally irrelevant if it does not correspond to a decision the team can act upon.

Scale-Up and Continuous Improvement

After the pilot demonstrates reliable identification and useful decision support, the deployment can expand to additional lines, warehouses, yards, or plants.

A scalable implementation should standardize:

• RFID event naming
• Asset and material identifiers
• Location identifiers
• Integration interfaces
• Security policies
• Device configuration
• AI model monitoring
• Exception-handling procedures
• KPI definitions

This allows new production areas to be added without redesigning the entire software environment.

KPIs for AI-Enabled RFID Aluminum Production

Performance should be measured through operational outcomes rather than the number of RFID readers installed.

Useful KPIs include:

• Material identification accuracy
• RFID read success rate
• Material-location accuracy
• Coil or billet search time
• Inventory reconciliation accuracy
• WIP dwell time
• Material transfer cycle time
• Production-order traceability completeness
• Genealogy completeness
• Quality-hold identification time
• Shipment verification accuracy
• Inventory discrepancy rate
• Production schedule adherence
• Material staging utilization
• Manual data-entry reduction
• Exception detection lead time
• AI alert precision
• False-alert rate
• System availability
• Mean time to recover from identification failures

The appropriate KPI set should be established before deployment so the plant can compare baseline performance with post-deployment results.

Business and Operational Value for Aluminum Manufacturers

The combined solution can improve aluminum manufacturing by connecting physical material movement with production decisions.

Key outcomes include:

• Faster material retrieval: RFID location events reduce manual searches for coils, billets, bundles, and WIP.
• Stronger genealogy: Persistent material identities connect processing and quality records across multiple production stages.
• Better inventory accuracy: AI compares physical movement patterns with ERP and WMS records.
• Earlier production exceptions: AI detects deviations from expected material routes and production sequences.
• Improved scheduling: Production planners gain more reliable visibility into material readiness and staging.
• Better quality investigation: Engineers can correlate material histories with process and inspection information.
• Reduced administrative work: Automated identification reduces repetitive scanning and manual location updates where RFID is technically suitable.
• More scalable traceability: Standardized identifiers and event models can extend from individual production areas to multiple aluminum facilities.

These outcomes depend on appropriate engineering, reliable identification, good master data, effective integration, and clearly defined operational workflows. RFID alone does not create traceability. AI alone does not create material visibility. The value comes from combining trustworthy physical identification with production context and actionable analytics.

Aluminum Production KPIs for AI and RFID Systems

The completed infographic connects AI and RFID capabilities with measurable performance indicators across five aluminum production domains: material traceability, WIP visibility, inventory accuracy, production scheduling, and quality genealogy. It presents KPI definitions, measurement context, enabling technologies, and a continuous improvement cycle without making unsupported performance claims.

Implementing AI and RFID for Aluminum Production

AI-enabled RFID can give aluminum manufacturers a more reliable connection between physical material movement and digital production information. The strongest applications focus on problems such as coil and billet traceability, WIP visibility, inventory reconciliation, material genealogy, production sequencing, quality investigation, and shipment verification.

Successful implementation requires more than selecting RFID hardware. Plants should validate RF performance on actual aluminum materials, establish reliable event models, integrate MES and ERP records, protect plant networks, validate AI outputs with production specialists, and define KPIs before scaling.

GAO provides RFID, BLE, and IoT hardware products and systems that can support these applications across material identification, asset visibility, sensing, and industrial data collection. Our engineering support can help organizations evaluate suitable hardware configurations and deployment approaches for their operating conditions.

Building the Future of Industrial AI and IoT with Aperture Venture Studio and GAO RFID Inc.

For more than three decades, GAO Group has invested heavily in R&D for industrial RFID, BLE, and IoT technologies. As AI became increasingly useful for industrial applications, we expanded our work across AI-enabled IoT, RFID, and BLE solutions and founded Aperture Venture Studio to advance practical AI and IoT solutions for applications such as aluminum production, material traceability, and industrial operations.

Aperture brings together technical experts, entrepreneurial and operational executives, investors, and leading companies. We also developed the Aperture Ventures Summit and TekSummit to exchange knowledge on advanced AI and IoT technologies.

GAO RFID Inc., headquartered in New York City and Toronto, Canada, is ranked among the world’s top 10 B2B and B2G BLE and RFID suppliers. GAO, GAO Research Inc., and GAO Tek Inc. form GAO Group, with operations in New York City and Toronto. The group serves organizations across the United States and Canada and welcomes collaboration with advisors, co-founders, employees, investors, and customers.