A Plain-English Guide to Applied AI

Before you can decide where AI belongs in your business, you need a working mental model of what it actually is. This page cuts through the vocabulary so the rest of the conversation makes sense.

Different problem shapes call for different model architectures. You will rarely design one, but knowing which family fits your data tells you whether a proposal is credible.

This is the layer most businesses actually touch — large language models and the patterns for pointing them at your own work.

The practitioner layer: the skills that separate people who get real leverage from AI from people who get a novelty demo.

Where your models run determines your cost, your latency, and — often decisively — your privacy posture.

Every disappointing AI project traces back to data. The model is rarely the problem; what you fed it usually is.

AI features break the rules conventional software design assumes. The same input can produce different output, and being wrong is a normal operating state rather than a bug.

The strategic question is not "how do we use AI" — it is which specific, measurable business problem justifies the effort.

Governance isn't paperwork you do after the fact — it's the set of decisions that determine whether your AI deployment survives its first serious mistake.

General capability becomes valuable only inside a specific function with a specific workflow. Here is where it lands.

Advice is cheap. RepBase and OrthoReps are software we designed, built, and shipped — which is where the operating knowledge on the rest of these pages comes from.