A.I. Tech needed an enterprise-grade foundation for taking AI solutions such as forecasting and document intelligence from validation to production across complex customer environments that included existing ERP estates, multiple data sources, hybrid infrastructure, and strict security and governance requirements.
Reported impact
The IBM stack gave A.I. Tech a production-ready way to deploy enterprise AI in mission-critical environments, improving throughput and energy efficiency while lowering total cost and helping the company package document intelligence, forecasting, and custom AI solutions for hybrid deployment.
A.I. Tech expanded a validation exercise on IBM Power into a broader hybrid AI reference architecture spanning IBM infrastructure, orchestration, and deployment software. IBM Power serves as the high-performance inferencing foundation for business-critical workloads, while IBM watsonx.ai supports model development, integration, and training workflows that connect AI models with enterprise data and processes. IBM watsonx Orchestrate demonstrates how multi-step agentic workflows can coordinate services across systems, IBM Fusion and Red Hat technologies provide consistent deployment across hybrid and containerized environments, and IBM Expert Labs helped optimize and validate the stack for production readiness. Within that architecture, A.I. Tech can run Lexten for document intelligence and Estima for forecasting either on premises or in hybrid environments, showing customers a path to AI adoption that balances performance, governance, cost, and existing-system constraints instead of demanding a greenfield rebuild.
Enterprise documents, ERP data, and operational signals feed into watsonx.ai-supported training and integration workflows, production inferencing runs on IBM Power, and watsonx Orchestrate coordinates the downstream actions while Fusion and OpenShift keep the deployment portable across on-premises and hybrid environments.
“42% more inferencing throughput”
“51% lower three-year solution costs”
“AI training workloads could be supported through watsonx.ai, while AI inferencing workloads ran on IBM Power servers”
“IBM watsonx Orchestrate ... helped A.I. Tech demonstrate how multiple AI-powered services can work together efficiently within a single enterprise environment.”
“IBM watsonx.ai provided a data and AI platform layer supporting model development, integration and deployment workflows”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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