AI & LLM Integration
#AI#LLM#AI&MLintegration
AI & LLM integration
AI Workload Assessment
Analysis of existing systems, data sources and operational workflows to identify where AI and LLM-based capabilities can be introduced within enterprise platforms without changing core system behavior. The focus is on data availability, processing boundaries and interaction patterns between AI components and existing services.
AI Technology and Service Selection
Evaluation of external AI and ML platforms, APIs and frameworks used in enterprise AI integration, based on functional scope, deployment constraints and compatibility with current infrastructure. Intelexity does not develop or train proprietary models and works exclusively with established third-party AI technologies.
Application and Data Pipeline Adaptation
Adaptation of application logic, service interfaces and data pipelines to enable AI-driven workflows. This includes request orchestration, preprocessing and postprocessing stages and alignment of AI outputs with existing business and system logic.
Runtime and Deployment Alignment
Preparation of execution environments for AI-enabled components, including workload placement, resource boundaries and interaction with surrounding services. AI-enabled solutions are aligned with on-premises or private cloud environments according to operational and architectural constraints.
Operational Control and Lifecycle Management
Implementation of mechanisms for observing AI behavior, managing access and maintaining AI-enabled functionality throughout its lifecycle. This ensures that AI components remain manageable and consistent with existing operational and governance processes.