# Enterprise Generative AI Architecture

Wells Fargo · 2024 · Role: Principal Architect

> An enterprise-grade Generative AI reference architecture: a governed path from experiment to production, adopted as the reference model across business lines.

## Problem

Teams across the bank wanted to use large language models, but there was no approved, governed way to take a Generative AI use case from experiment to production.

## Approach

- Designed a modular, phased reference architecture covering data ingestion, processing, embedding, retrieval, generation and validation.
- Set an enterprise model strategy across public cloud and on-premises infrastructure, spanning frontier and open models.
- Published reusable archetypes for model deployment and inferencing, and for model development, training and validation.

## Governance

- Model guardrails and AI gateways against prompt injection and data leakage.
- Security testing of every Generative AI use case before it goes live.
- A governed experimentation environment with automated lineage, detective controls and secure data access.

## Outcome

- Approved through enterprise architecture governance and adopted as the reference model across business lines.
- The first use case, a retrieval-augmented assistant for internal policy guidance, went from pilot to production on this architecture.

## Lesson

Governance designed in from the first use case is what lets the second, third and twentieth move fast.

Technologies: Generative AI, Retrieval-augmented generation, Public cloud, Model guardrails, AI gateway

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Muthukrishnan (Muthu) Bommiah · https://muthub.org · Views are my own and do not represent my employer.