Choosing the Right AI Stack for a Mid-Sized Business

There's no single "best" AI stack. There's only the stack that fits your team, your data, and the problem you're actually trying to solve. Here's how we think about it when scoping a new engagement.

Start with the problem, not the tool

Before evaluating Anthropic vs OpenAI vs open-source models, get specific about the workflow you're automating. Is it deterministic (same input → same output) or judgmental (needs reasoning)? Is data sensitive? What's the volume? Those answers narrow the field fast.

When low-code wins

Zapier and n8n shine when the work is integration-heavy: "new Stripe payment → create invoice in Xero → notify Slack." If your problem is mostly moving data between systems, low-code is faster, cheaper, and easier to maintain.

When custom wins

Custom builds make sense when (a) the data is sensitive, (b) the logic is complex enough that a low-code graph becomes spaghetti, or (c) the volume is high enough that per-task pricing dominates. Most of our 6-figure engagements look like this.

Picking a model

• 1Claude (Anthropic) strongest at long-context reasoning, careful summarization, and following nuanced instructions.
• 2GPT-4 / GPT-5 (OpenAI) broad ecosystem, great at function-calling and code.
• 3Gemini (Google) strong multimodal, integrates cleanly with Google Workspace.
• 4Open-source (Llama, Mistral) required when data residency or per-token cost rules out hosted APIs.

What we ship most often

For mid-sized businesses, the answer is usually a hybrid: n8n for the routing, a frontier model (Claude or GPT) for the reasoning, a small custom layer for the parts that have to be exactly right. That stack is fast to build, easy to extend, and doesn't lock you into one vendor.