We built a Spanish-first voice agent for a Tampa roofing company. 41% of inbound calls switched to Spanish on offer. Here is the stack and the cost.
A Tampa roofing company we work with had a problem. Roughly 40 percent of their leads were Spanish-speaking, and the after-hours agent only worked in English. They were quietly losing every one of those calls. We rebuilt the agent Spanish-first. Here is the stack, the prompt design, and what it cost.
Why this is harder than it looks The most common failure mode with voice for spanish businesses is treating the problem as a model selection problem. The hard parts are the data pipeline feeding it, the eval that catches regressions, and the human ownership layer that keeps the system honest after the implementer leaves the building. We have shipped this category of system enough times to recognize a few patterns. The teams that win allocate roughly 20 percent of project time to the model and prompts, 40 percent to data and integrations, 25 percent to evals and observability, and 15 percent to change management. The teams that lose flip those numbers, spend 70 percent on prompts, and end up with a great demo that nobody trusts.
We built a Spanish-first voice agent for a Tampa roofing company. 41% of inbound calls switched to Spanish on offer. Here is the stack and the cost. It is filed under AI Voice because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.