We build AI agents.

You probably could build something that handles 60% of what we'd build. The question isn't capability — it's time and the failure modes you don't know exist yet. Most owner-built agents work great until they hit production: an unusual customer message, an integration timeout, a model update that changes outputs. Then they break, and the owner who built it has to debug it instead of running their business. We've already hit those failure modes in our own products. You're not paying us to write code. You're paying for the time we save you and the mistakes we've already made on someone else's dollar.

Most agencies ship demos that break in production. We ship agents engineered like infrastructure software — with evaluation pipelines that test accuracy against real scenarios, audit trails for every decision, rollback when a change degrades performance, error recovery when integrations fail, and cost controls so AI spend doesn't surprise you. Most agencies skip these because they're invisible until something breaks. We don't ship without them because we've felt them matter in our own production systems.

Often. If your problem can be solved with a $50/month SaaS subscription, buy the SaaS. If it's a one-time task, don't build for it. If your team will resist using it, the agent fails before it ships. If the AI accuracy needed is 99.9% (legal, medical, regulatory), the cost of mistakes is too high right now. We tell clients no on roughly a third of the use cases they bring us. It builds more trust than saying yes to everything.

Three things. We monitor — we watch your agent's accuracy, cost, and uptime, and we tell you before something breaks. We tune — when your business changes (new clients, new processes, new edge cases), we update the agent so it stays useful. We evolve — when underlying models or tools improve, we upgrade the agent to use them. Most "ongoing support" is bug-fix-only. Ours is "the agent gets better over time, not stale."