Generative AI is past the novelty stage. The early demos, the playgrounds, the ​“Hey, look what ChatGPT just wrote!” moments — they got us here. But now, enterprise leaders are asking the harder question: how do we actually build with this?

If you’re trying to bring generative AI into a real, functioning business process — not a toy app or pilot experiment — you need more than a model. You need a stack.

Not just any stack, either. A well-thought-out set of tools and practices that fit your data, your team, and your goals. One that doesn’t break under compliance pressure or get tossed aside when IT gets nervous.

Let’s break down what that looks like.

Why You Need a Stack in the First Place

You don’t run a business on a language model alone. The real power of generative AI shows up when it’s embedded in your workflows, enriched with your data, and surrounded by the right infrastructure.

Here’s why the stack matters:

• Models need context. On their own, even the smartest models hallucinate or generalize. You want grounded answers based on your data.

• You need control. Monitoring, versioning, cost tracking — none of that comes ​“out of the box” with public APIs.

• Security and privacy aren’t optional. Especially when your prompts involve customer data, contracts, or internal communications.

The good news? You don’t need to build everything from scratch. The tooling has matured — now it’s about picking the right building blocks.

Core Layers of the Generative AI Stack

Think of the GenAI stack in layers, starting from the bottom and working up to the user.

1. Foundation Models (FM)

At the base, you’ve got the model itself — the engine doing the generative work.

Options include:

• Closed-source APIs: OpenAI, Anthropic, Google Gemini. These are reliable and easy to integrate.

• Open-source models: Mistral, LLaMA, Falcon. These offer more control but require infrastructure.

What matters most:

• Can it reason over the type of content you care about (code, contracts, product specs)?

• Is latency acceptable for your use case?

• Can you fine-tune or parameterize it for your industry?

You don’t always need the biggest model. Smaller, well-aimed ones often do the job better.

2. Data and Retrieval (RAG)

Most enterprises don’t want to train a model — they want to feed it the right data.

That’s where Retrieval-Augmented Generation (RAG) comes in. Instead of trying to stuff everything into a prompt, you connect your model to your internal content.

This layer typically includes:

• Data ingestion tools: to scrape, extract, or sync your documents (from SharePoint, CRMs, databases).

• Vector databases: like Pinecone, Weaviate, or Chroma, to store and search embeddings.

• Chunking and embedding logic: to break documents down into useful pieces and make them searchable by meaning, not just keywords.

With RAG, the model doesn’t need to know everything — it just needs to know how to find the right stuff before it answers.

3. Orchestration and Prompt Engineering

Now we’re getting into the glue layer — the logic that ties everything together.

Instead of a one-off prompt in a playground, you’re building reusable workflows. A sales tool that drafts a custom pitch. A legal bot that summarizes agreements. An analyst assistant that preps a slide deck.

Tools that live here:

• LangChain, LlamaIndex, or Semantic Kernel: libraries to manage prompt flows, context memory, and tool calling.

• Prompt templates: designed, tested, and versioned like software.

• Evaluation frameworks: to score your outputs automatically and keep quality from drifting.

This is also where you make things modular — so future updates don’t require tearing the whole thing down.

4. Interfaces and Applications

This is the part your users actually see. A chat app. A sidebar in your CRM. A Copilot in your ERP.

The UI should reflect the job to be done, not the magic underneath. And it should build trust by showing sources, offering controls, and making it easy to give feedback.

Popular approaches:

• Embedding GenAI into existing tools (e.g., Microsoft Copilot, Salesforce Einstein)

• Building internal tools with Streamlit, Retool, or low-code platforms

• Custom web apps with a clean interface and tight user permission controls

Don’t underestimate this part. A well-designed UI can make or break adoption.

Extras You Shouldn’t Skip

These don’t fall neatly into one layer, but they matter.

• Monitoring and logging: Track usage, latency, and model output quality.

• Guardrails: Apply filters for tone, PII, or regulatory red flags.

• Cost controls: Especially if you’re dealing with variable API pricing or large user bases.

• Security: Role-based access, data encryption, and audit trails need to be built in, not bolted on.

One Size Doesn’t Fit All

Every enterprise has different needs. A law firm might prioritize document summarization. A manufacturer might want AI to help with safety reporting. The stack should reflect your business — not someone else’s demo.

But the pattern is always the same: start with a clear problem, build a narrow workflow, and evolve from there.

You don’t need a monolithic platform. You need a flexible stack you can shape around your team, your data, and your goals.

Final Thought: It’s Not About the Model — It’s About the System

If there’s one thing early adopters are learning fast, it’s this: the model is just the beginning. It’s the surrounding stack — the data, the logic, the interfaces — that makes GenAI actually useful.

And here’s the upside: once you have that stack in place, spinning up new use cases becomes way easier. You can go from idea to impact in weeks, not quarters.

So, if you’re serious about using GenAI inside your business — not just watching from the sidelines — it’s time to think stack-first.

Not sexy, maybe. But it’s how real transformation starts.