Answers to common questions about our AI services across security, testing, DevOps, and cloud.
Conversational AI can support customer service, workflow automation, internal assistance, appointment handling, information retrieval, and operational communication.
AI PoCs help organisations reduce implementation risks, evaluate operational impact, and understand infrastructure requirements before scaling investments.
ETL transforms data before loading into storage, while ELT loads raw data first and performs transformations within the target environment.
Data science can support forecasting, customer analytics, fraud detection, operational optimisation, predictive maintenance, and intelligent automation.
Modernization improves data accessibility, scalability, operational efficiency, analytics readiness, and long-term maintainability.
Businesses handling live operational data, IoT systems, customer analytics, monitoring systems, and real-time transactions benefit from streaming architectures.
Data governance improves data reliability, operational visibility, compliance readiness, and decision-making accuracy across organisations.
We build operational dashboards, executive reporting systems, financial analytics platforms, customer analytics dashboards, and real-time monitoring solutions.
Businesses handling high-volume operational data, analytics workloads, IoT systems, customer data, and real-time processing benefit from big data services.
We support AWS, Microsoft Azure, Google Cloud, hybrid cloud, and multi-cloud environments.
DevSecOps helps organisations identify vulnerabilities earlier, automate security validation, and improve operational security without slowing software delivery.
FinOps improves cloud cost efficiency, financial accountability, operational visibility, and long-term infrastructure scalability.
We support AWS, Microsoft Azure, Google Cloud, hybrid cloud, and multi-cloud environments.
We support AWS, Microsoft Azure, Google Cloud, hybrid cloud, and multi-cloud infrastructure environments.
We support AWS, Microsoft Azure, Google Cloud, hybrid cloud, and multi-cloud environments.
We support AWS, Microsoft Azure, Google Cloud, hybrid cloud, and multi-cloud environments.
A standard chatbot is a rigid, rule-based script. A custom AI agent, like the ones built at GlobalNodes, is autonomous—it can understand intent, use external tools (like querying your database or sending an email), and execute complex, multi-step workflows.
No. It complements existing analytics tools by connecting insights across departments and identifying the strategic decisions that emerge from those relationships.
Yes. Investment teams can adjust key assumptions including revenue growth, margins, financing costs and valuation multiples to understand how changes affect portfolio value in real time.
Risk Intelligence is designed as an intelligence layer that sits across the risk management process. It helps organisations identify signals, analyse patterns, understand exposure and prioritise actions.
Yes. The platform is designed to model exposure across different climate scenarios, helping organisations understand how changing conditions could affect sourcing regions and commodities over time.
The platform is designed to monitor organisations operating across multiple sustainability frameworks, including CSRD, ISSB, SEC Climate Disclosure, CDP and other emerging standards.
Supplier Intelligence is designed as an intelligence layer that sits across the supplier management process. It helps organisations understand supplier performance, identify emerging issues, assess dependencies and prioritise actions.
Product Intelligence is designed as an intelligence layer that sits across the product management process. It helps organisations understand product performance, identify patterns, analyse customer signals and prioritise actions.
It can bring together product, packaging, material, sales, waste, recycling, recovery, supplier, certificate and compliance information, depending on the systems and data available to the organisation.
The current demonstration includes tomato, basil and gerbera production lines. The approach can be adapted to other crops based on the environmental, crop health and operational data available.
The platform is designed to support natural capital analysis across ecosystems such as coral reefs, mangroves, fisheries, seagrass and coastal ecosystems. The approach can be adapted based on the environmental and economic data available.
No. It complements existing monitoring platforms by adding an intelligence layer that explains how narratives evolve, where perception is changing and what actions leadership should prioritise.
No. It complements surveys by continuously analysing workforce signals throughout the year, rather than relying solely on periodic feedback.
It can bring together community discussions, social interactions, event data, surveys, support conversations, product feedback and other member activity, depending on the available data sources.
It can bring together volunteer profiles, skills, interests, availability, registrations, attendance, participation, programme information and feedback, depending on the available data sources.
It can bring together application information, grant records, funding data, programme information, grantee reports, impact data and supporting documents, depending on the available data sources.
Legacy modernisation improves operational efficiency, system scalability, data accessibility, and integration across supply chain processes.
Legacy modernisation improves platform scalability, streaming performance, operational efficiency, and integration with modern technologies.
Legacy modernisation improves system scalability, operational efficiency, interoperability, and patient experience while supporting modern healthcare technologies.
Legacy modernisation improves scalability, operational efficiency, customer experiences, and integration with modern digital financial services.
Legacy modernisation improves scalability, collaboration, operational efficiency, and integration with modern legal technologies.
Legacy modernisation improves scalability, operational efficiency, system integration, and support for modern connected automotive technologies.
Legacy modernisation improves scalability, operational efficiency, system integration, and support for modern Industry 4.0 technologies.
Legacy modernisation improves scalability, operational efficiency, customer experiences, and integration with modern commerce technologies.
Yes. You are involved throughout the process with regular checkpoints.
No. Integration is designed to fit into your current systems.
No. It is a practical roadmap that can be directly executed by your team or a vendor.
No. Outputs are designed for both business and operations teams.
No. It aligns and enables your existing teams to execute better.
Yes. We guide decisions through MVP builds and deployments.
Typically 3–6 weeks depending on complexity and integrations.
Yes. MVP is built to plug into your current workflows (CRM, ERP, etc.).
Yes. We integrate automated testing workflows directly into CI/CD environments for continuous validation.
Yes. We can assess your current DevOps setup and modernise legacy deployment processes with scalable automation practices.
Yes. We test mobile applications across Android and iOS devices to ensure consistent performance and usability.
Yes. We help organisations strengthen security practices and prepare for compliance and governance requirements.
No. Automation testing works best alongside manual testing to ensure both technical validation and real-world usability testing.
No. Our optimisation approach focuses on improving efficiency while maintaining or enhancing application performance and reliability.
Our modernization approach is designed to minimise operational disruption through phased migration and structured transition planning.
Yes. Many technical debt issues can be addressed through phased refactoring, optimisation, and modernization strategies without complete redevelopment.
Yes. We assess AWS, Microsoft Azure, Google Cloud, and hybrid infrastructure environments.
Yes. We often modernise and restructure legacy systems through phased architectural improvements without full redevelopment.
Yes. We build AI solutions tailored to specific business workflows, operational requirements, and industry use cases.
AI agents can automate customer support, reporting, approvals, research, operational coordination, data retrieval, and internal business workflows.
Yes. We assess generative AI platforms, AI agents, LLM workflows, conversational AI systems, and retrieval pipelines.
Timelines vary depending on complexity, integrations, and workflows, but most POCs are designed for rapid execution and validation.
Yes. We integrate predictive models with enterprise applications, dashboards, APIs, and cloud platforms.
Yes. We integrate generative AI systems with CRMs, ERPs, databases, APIs, and cloud infrastructure environments.
Yes. We develop real-time computer vision systems capable of processing live video streams and operational data.
Yes. We integrate machine learning systems into APIs, cloud platforms, enterprise applications, and operational workflows.
Yes. We integrate conversational AI platforms with CRMs, APIs, enterprise applications, cloud systems, and operational workflows.
We build generative AI systems, AI agents, conversational AI platforms, predictive analytics models, and computer vision prototypes.
Yes. We develop streaming data architectures capable of processing and analysing live operational data.
Yes. We integrate data science models and analytics workflows with enterprise applications, APIs, cloud environments, and operational platforms.
Yes. We migrate legacy data systems to AWS, Microsoft Azure, Google Cloud, and hybrid environments.
Yes. We integrate streaming pipelines with APIs, enterprise platforms, databases, cloud services, and operational systems.
MDM is the process of creating consistent and centralised master records for critical business data such as customers, products, and suppliers.
Yes. We integrate visualization platforms with databases, APIs, cloud services, enterprise applications, and operational systems.
Yes. Big data systems provide the scalable infrastructure required for AI training, predictive analytics, and machine learning workflows.
Yes. We migrate legacy applications, databases, and infrastructure to modern cloud environments using structured transition strategies.
Yes. We integrate automated security scanning, code analysis, and compliance checks into CI/CD workflows.
We support AWS, Microsoft Azure, Google Cloud, hybrid cloud, and multi-cloud infrastructure environments.
Yes. We provide continuous monitoring, alerting, incident management, and operational support services.
Yes. We migrate and modernise legacy applications using structured transition and cloud-native modernization strategies.
Yes. We provide migration planning, readiness assessments, modernization guidance, and cloud adoption roadmaps.
Yes. We deploy cloud-native systems using containers, Kubernetes, microservices, and automation frameworks.
We do not "train" public models on your data, as that risks leakage. Instead, GlobalNodes uses Domain-Grounded RAG (Retrieval-Augmented Generation), an architecture that securely connects a private AI model to your internal documents in real-time.
The platform is designed to aggregate intelligence from finance, operations, people, sustainability, brand, customer, supply chain, risk and other enterprise systems into a unified executive view.
No. It complements existing reporting by continuously analysing live operational and financial data to identify opportunities, risks and recommended actions.
Yes. Anomaly detection is one of the AI use cases supported by the underlying intelligence approach. It can help identify unusual patterns across large datasets that may require further investigation.
Yes. The concept combines long-term scenario modelling with operational signals that can include crop stress, forest and land-use alerts, conflict data and other regional risk indicators.
No. It complements existing sustainability systems by aggregating data, surfacing risks and generating executive insights.
Yes. The platform can analyse supplier and operational signals to help identify changes in performance, unusual activity, dependencies and other areas that may require investigation.
Yes. Product and usage data can be analysed to identify changes in adoption, engagement and feature usage that may require further investigation.
Yes. It can help identify missing, incomplete or inconsistent information across the data supporting EPR obligations and reporting.
The current platform includes disease risk monitoring for powdery mildew, botrytis and fusarium. The Disease Alert Breakdown provides visibility by pathogen and growing house.
Yes. A core part of the platform is the ability to connect ecosystem health with indicators such as GDP contribution, fiscal revenue, employment and climate resilience.
The platform can analyse themes including sustainability, corporate reputation, leadership, innovation, employer brand, product quality, customer experience and other organisation-specific priorities.
The platform is designed to integrate with leading HR, collaboration and employee experience platforms including Workday, Culture Amp, Workhuman, ServiceNow and Lattice, along with other enterprise systems.
Yes. It can identify recurring topics, themes, questions, issues and other signals across large volumes of community conversations.
Yes. Volunteer Intelligence can help identify volunteers whose skills, interests and availability align with specific programme or activity requirements.
Yes. Foundation Intelligence can help organise and analyse application information, identify recurring themes and surface areas that require further review.
Cloud migration improves scalability, operational flexibility, reporting speed, and access to real-time logistics data.
Cloud migration improves scalability, supports global content delivery, enhances platform reliability, and reduces infrastructure limitations.
