Connecting models to data and agents with Red Hat AI

Connect models to customer relationship management data and regional performance records. Surface territory recommendations based on historical conversion rates and current market intelligence.

Deploy an expert assistant that stays entirely within your private network. It cites its sources for every response, making audits simple and ensuring your data never leaves your oversight.

Link AI to sensor data in accordance with manufacturing specifications. Identify when a process drifts from the documented “standard” and let agents trigger corrections on the floor. 

Use models to analyze past purchase orders and vendor contracts. The system creates a flag when new quotes deviate from historical averages, empowering better negotiations. 

Query decades of internal lab notes and trial results to find relevant historical experiments and prevent teams from duplicating past work. 

Bridge live tickets with product manuals and past resolutions. Give support teams grounded, cited answers that cut repetitive troubleshooting time.

Docling integrates directly with Red Hat AI, allowing teams to process large document collections at scale while preserving lineage and traceability from source documents to AI outputs.  

SDG Hub is a framework for constructing synthetic data generation pipelines. It helps teams produce high-quality, domain-specific datasets that are validated, auditable, and safe for downstream use.  

Training Hub is an algorithm-focused interface for large language model (LLM) training, continual learning, and reinforcement learning techniques. It provides governance over datasets, training inputs, and model updates.

EvalHub offers a unified interface and API to assess, benchmark, and certify your AI models and applications for production. Results are published as immutable assets, giving you a verifiable, auditable record.

AutoRAG simplifies the construction and optimization of RAG pipelines with a data-driven approach that replaces manual trial-and-error tuning. 

AutoML capabilities automate key steps from training to production. It helps with model evaluation, compression, and hardware optimization.

Maintain clear lineage from source documents to processed output. Red Hat AI supports sovereign AI initiatives that require governance and audit requirements as well as explainability. 

The Model Context Protocol (MCP) Gateway lets agents reach your live enterprise data, APIs, and tools with identity-based access control and built-in guardrails. No custom code required.