Every vendor wants to sell you their "AI platform." Most businesses don't need a platform—they need the right tools, connected sensibly, with clear ownership. Here's how to think about building an AI stack that actually works.
Every AI stack has four layers. You need all four, but the right solution for each layer depends on your specific situation:
"Most AI failures happen at the integration and interface layers—not the intelligence layer. Getting AI to work is easy. Getting people to use AI outputs to make better decisions is hard."
// Don't over-invest in models while under-investing in adoption.
Let's break down what you need at each layer:
For most local businesses: You don't need a data warehouse. You need clean exports from your existing systems (POS, CRM, booking software) into a structured format.
Don't build custom models. Use existing services. OpenAI, Anthropic, or Google Cloud AI handle 90% of use cases better than anything you'd build yourself.
This is where most businesses need help. Connecting AI outputs to your existing tools—updating your CRM, sending the right message, triggering the right workflow.
The best interface is often no interface—AI works in the background, surfacing insights in tools people already use. When you need a UI, keep it dead simple.
Avoid these expensive mistakes:
Custom AI is rarely necessary. The best solution is usually stitching together existing services with light customization.
"Let's centralize all our data first" is a multi-year project that delays value. Start with specific use cases and specific data.
AI systems drift. Models degrade. Integrations break. Budget ongoing maintenance from day one, not as an afterthought.
Keep your data portable. Own your integrations. Be able to swap out any layer without rebuilding everything else.
Build your stack in this order:
Your stack should grow organically from real needs—not from a architecture diagram someone drew in a planning session.
Let's design your AI stack together.