What is AI Fabric?

Most enterprises have AI initiatives scattered across teams - a chatbot here, a recommendation engine there, a fraud model somewhere else. Each builds its own data pipeline, feature store, and serving infrastructure. The result is duplicated effort, inconsistent data, and models that cannot share context.

An AI fabric unifies this by providing a single semantic layer that all AI applications draw from. The knowledge graph provides structured, governed context; the embedding layer provides semantic similarity; the model orchestration layer routes queries to the right combination of retrieval and generation.

The result is an organisation where AI applications are composable, explainable, and grounded in a shared understanding of the business - not isolated experiments running on isolated data copies.

An AI fabric typically builds on three layers:

1. Semantic data layer: A knowledge graph backed by a triplestore, enriched with ontologies and SHACL validation. This is the “ground truth” that all AI applications share.

2. Embedding and retrieval layer: Vector embeddings are generated from the knowledge graph entities and unstructured documents, stored in a vector index. This enables semantic search and hybrid retrieval (graph + vector) for RAG applications.

3. Orchestration layer: A model-agnostic orchestration layer (e.g., LangChain, Semantic Kernel, or custom) routes user queries through the right sequence of retrieval, reasoning, and generation steps. It can combine SPARQL graph traversal with vector search and LLM generation in a single pipeline.

The key differentiator from a plain data fabric is that the AI fabric treats AI models as first-class consumers and producers of knowledge - not just downstream analytics tools.

Enterprise knowledge assistant: A professional services firm builds an AI fabric that connects its project knowledge graph, document archive, and GPT-4 model. Consultants ask natural-language questions and get answers grounded in verified project data with source citations.

Smart manufacturing: A manufacturer’s AI fabric links equipment ontologies, sensor data streams, and predictive maintenance models. When a model detects an anomaly, it enriches the alert with contextual knowledge from the graph - related equipment, maintenance history, and recommended actions.

Financial intelligence: A bank’s AI fabric combines a FIBO-aligned knowledge graph with NLP models that extract entities from regulatory filings. New regulations are automatically linked to affected products, clients, and processes in the graph.