COMPEL Glossary / real-time-processing
Real-Time Processing
Real-time processing involves generating AI predictions as events occur, typically delivering results within milliseconds to seconds.
What this means in practice
Real-time processing is required for applications where delays degrade value or create risk: fraud detection must evaluate transactions before they complete, conversational AI must respond quickly to maintain natural interaction, dynamic pricing must adjust as market conditions change, and autonomous systems must react to their environment continuously. Real-time AI processing requires always-on model serving infrastructure, low-latency data access, and robust failover mechanisms. In the COMPEL maturity model, real-time inference capability typically appears at Level 3 in the Data Infrastructure domain (Domain 10), representing the transition from batch-only to multi-pattern data processing architecture.
Why it matters
Real-time AI processing is required for applications where delays degrade value or create risk, including fraud detection, conversational AI, dynamic pricing, and autonomous systems. Organizations without real-time inference capability are limited to batch-only use cases, missing high-value opportunities that require immediate AI responses. Building this capability requires significant infrastructure investment that must be justified through use case analysis.
How COMPEL uses it
In the COMPEL maturity model, real-time inference capability appears at Level 3 in the Data Infrastructure domain (Domain 10), representing the transition from batch-only to multi-pattern processing. During the Model stage, real-time requirements drive infrastructure architecture decisions in the Technology pillar. The Produce stage deploys always-on model serving infrastructure, and the Evaluate stage monitors latency compliance against defined SLAs.
Related Terms
Other glossary terms mentioned in this entry's definition and context.