Decision Guide

Your AI has to work with the systems you already have. The real challenges of integrating AI with legacy software—and the patterns that avoid a risky rewrite.

Scope decides whether an AI project ships or sprawls. How to write a scope that defines the outcome, respects data reality, and says what you won't build.

Traditional SLAs don't fully fit AI. What service levels to expect—uptime, latency, accuracy thresholds, human backup—and what to demand from a vendor.

Build custom or buy off-the-shelf? A clear decision framework—when bespoke software is a real advantage, and when a ready product is simply the smarter buy.

Do you need AI consulting or AI development? When advice comes first, when building does, and why a partner who does both beats a strategy deck that never ships.

An agent demo is cheap; a production agent isn't. What drives AI agent development cost—integrations, guardrails, evaluation—and how to budget for the real thing.

A POC proves it can work; an MVP is the smallest thing real users can use. Which to build for your AI project—and why the distinction decides whether it ships.