Practical notes on AI agents, custom software, and rescuing the things you already built — written for the owners and operators actually doing the work. No thought-leadership fluff.
It's a reasonable question with a genuinely variable answer — but "it depends" without actual numbers helps nobody. Here's the real breakdown, by project type, team model, and the factors that move the number in either direction.
Read the note →It's a reasonable question with a genuinely variable answer — but "it depends" without actual numbers helps nobody. Here's the real breakdown, by project type, team model, and the factors that move the number in either direction.
Most startups and product teams don't fail because they built the wrong thing. They fail because they built too much of it before finding out it was wrong. The MVP model exists to prevent that — but in practice, most teams still overbuild, overspend, and over-schedule before they have a single real user.
Your potential customers are changing where they search. Some still type queries into Google. A growing number ask ChatGPT, Perplexity, or Gemini instead — and act on the answer without clicking a single link. If your company only shows up in Google, you're invisible to that second group.
US companies need engineering capacity. Onshore teams are expensive and hard to staff. Pure offshore often means time zone gaps, communication overhead, and partners who treat your project as a resource allocation problem, not a business challenge.
A practical guide to designing how humans, AI agents, and systems work together. Map workflows, catalog agents, and build an operating model that scales.
AgentOps is the discipline of designing how AI agents work within your business operations. Learn what it is, why it matters, and how it differs from automation and DevOps.
