3 Principles for Production-Ready AI

How do you roll out AI across your enterprise — securely, at scale, and in alignment with your business strategy?

That was the question in the room at CAMP IT’s “Enterprise Architecture – Strategies That Enable Technology Trends” on March 5 in Chicago. Calvin Hendryx-Parker, CTO and Co-Founder of Six Feet Up, tackled this question head-on in his talk: Blending AI into Enterprise Architecture.

Why AI Fails

While some teams had launched pilots and a few had models in production, nearly all attendees shared the same blockers:

• Concerns around data privacy and compliance
• Gaps in governance and oversight
• Unreliable outputs and hallucinations
• Poorly structured or incomplete data

The reality? Prototypes are easy. Building enterprise-grade AI is the real test.

What Production-Ready AI Actually Takes

Moving from proof of concept to meaningful business value requires more than model experimentation — it demands intentional design and infrastructure. These 3 principles provide a solid starting point:

1. Trust Over Tools

• It’s not just ChatGPT vs. Claude. Don’t fall into the trap of choosing tools based on hype. Evaluate performance, bias, hallucination risk, and compliance. Tools like Hugging Face’s leaderboards are a good place to start.
  • ‍Takeaway: Treat models like any critical business system — and vet them accordingly.

2. Use RAG, Not Fine-Tuning

• Fine-tuning is slow, expensive, and hard to scale. Retrieval-augmented generation (RAG) offers faster, safer results by injecting your real data into model responses.‍
  • Takeaway: RAG makes your AI smarter with fewer hallucinations and more context-aware responses — all with less overhead.

3. Prep Your Data and Plan for Governance

• Even the best model fails with messy input. Organize, chunk, and embed your data using vector databases like PGVector or ChromaDB. But architecture alone isn’t enough — establish governance early with clear policies, usage tracking, and business stakeholder involvement.‍
  • Takeaway: Data quality and governance are your launchpad for safe, scalable AI.

Design AI Like You Design Software

AI isn’t a feature. It’s a foundational architecture decision.

"Choosing default tools gives you default results,” said Calvin. “If you don’t invest in architecture — choose the right models, prepare your data, and put the right systems in place — AI will fall short.”

To show what that investment looks like in practice, Calvin shared a slide mapping four common enterprise scenarios to recommended embedding models, with rationale for each.

Deploying AI is just like deploying software: you need pipelines, observability, and feedback loops. It’s not about one magic model. It’s about architecture.

If you’re moving from experimentation to enterprise impact, you don’t need to start from scratch. You just need a partner who understands the full AI lifecycle — from design to deployment.

Download Calvin’s slides and explore Six Feet Up's AI capabilities to dig deeper.