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The Seven-Factor Enterprise AI App
  • 0. Introduction
  • I. Modularity Over Monoliths
  • II. Information and Context Curation
  • III. Many LLMs, One Intelligence
  • IV. Dynamic Tools, Upgradeable Skills
  • V. Collaboration and Orchestration at Scale
  • VI. RAG Stack for Speed and Accuracy
  • VII. User Experience for Agents
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0. Introduction

Introduction to the Seven-Factor Enterprise AI methodology to build generative AI applications for large enterprises

NextI. Modularity Over Monoliths

Last updated 9 months ago

Since the advent of generative AI, there has been a surge of companies experimenting with and building applications with AI features, but the requirements for large enterprises are inherently different that entails stricter rules around accuracy, explainability, etc., with speed and scale.

The Seven-Factor Enterprise AI App, inspired by the document for cloud computing, is a methodology to build enterprise grade generative AI applications.

The Seven-Factor App is agnostic to the underlying choices of technologies and programming languages and is meant to be treated as a guideline for best practices but can ultimately be configured based on individual requirements for different enterprises.

Background

The document is based on the experiences of directly working with and helping several small and large companies building generative AI applications using as the underlying data source. These companies include but are not limited to multi-billion dollar tech and financial services companies. However, this is open for collaboration and contribution from other developers, engineers and architects who have built reality-scale enterprise-grade generative AI applications and have other notes to add. We will capture those notes and case studies in a separate chapter called case studies after step VII as they become available.

Twelve-Factor App
SingleStore
repo
High Level Reference Architecture of Enterprise AI using Seven-Factor Enterprise AI Apps