Back to Blog
Enterprise AIArchitectureLLMDevOps

The 6 Pillars of an Enterprise AI Platform

Building a robust Enterprise AI Platform requires more than just models. Here are the 6 critical components for a scalable, secure, and effective AI infrastructure.

Components of an Enterprise AI Platform

As enterprises move from experimentation to production with Generative AI, the need for a robust, scalable platform becomes critical. A true Enterprise AI Platform is composed of six essential pillars that ensure security, observability, and performance.

1. AI Gateway

The entry point for all AI interactions.

  • LLM Gateway: Centralized access to various Large Language Models, handling API keys, rate limiting, and routing.
  • MCP Gateway: Manages Model Context Protocol connections, allowing AI agents to safely interact with internal tools and data sources.

2. Model Management

The governance layer for your AI assets.

  • Lifecycle Management: Versioning, deploying, and retiring models.
  • Guardrails: Real-time checks to prevent hallucinations, PII leakage, and inappropriate content.
  • Policies: Enforcing organizational standards for model usage and access control.

3. Agentic AI Frameworks & Foundry Blocks

The building blocks for autonomous behavior.

  • Reusable components ("blocks") to build complex agents.
  • Frameworks that enable planning, reasoning, and multi-agent collaboration.
  • Tool registries for agents to discover and use capabilities.

4. Self-RAG Solutions

A complete system for Retrieval-Augmented Generation.

  • Data Ingestion: Pipelines to clean, chunk, and embed proprietary data.
  • RAG Search: High-performance vector search combined with semantic reranking to provide the most relevant context to models.
  • Self-Correction: Mechanisms for the system to evaluate its own retrieved context and refine answers.

5. AI Ops Toolchains

Operational excellence for AI workloads.

  • FinOps: Tracking and optimizing token usage and GPU costs.
  • Observability: Tracing chains of thought, monitoring latency, and debugging complex agent interactions.
  • DevOps: CI/CD pipelines specifically designed for prompt engineering and model evaluation.

6. AI Ready Data

The foundation of intelligence.

  • Data Lakes: Centralized repositories for structured and unstructured data.
  • Content Readiness: Ensuring documents and knowledge bases are clean and accessible.
  • API Readiness: Exposing business logic and data via well-documented APIs that agents can easily consume.