Architecture

AI Pipeline

An AI pipeline is the end-to-end workflow of data ingestion, processing, model inference, and output delivery that transforms raw inputs into AI-powered business outcomes.

What Is an AI Pipeline?

An AI pipeline is the complete chain of processes that takes raw input (text, data, images) through preprocessing, model inference, post-processing, and output delivery to produce an AI-powered result. In production systems, pipelines include error handling, logging, monitoring, caching, and fallback logic.

How Are AI Pipelines Used in Business?

Every business AI application is a pipeline. A customer service AI pipeline: receives customer message → classifies intent → retrieves relevant knowledge (RAG) → generates response → applies guardrails → delivers response → logs interaction. AffixedAI's pre-built modules are composable pipeline components that snap together to create custom AI workflows.

AI pipelinedata pipelineML pipeline

Related Terms

AI Orchestration

AI orchestration is the coordination layer that manages multiple AI models, agents, and tools — routing tasks, managing state, handling failures, and ensuring coherent system-wide behavior.

RAG (Retrieval-Augmented Generation)

RAG is a technique that enhances AI responses by retrieving relevant information from external knowledge bases before generating an answer, combining the power of search with generative AI.

Guardrails

AI guardrails are safety mechanisms that constrain AI behavior within acceptable boundaries — preventing harmful outputs, enforcing policies, and maintaining quality standards in production systems.

AI Middleware

AI middleware is the software layer between AI models and business applications that handles routing, authentication, caching, logging, rate limiting, and model switching.

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