In 2023, processing a 30-second audio clip with AI quality good enough for production use cost several dollars. Today it costs less than a cent. That 99% reduction in AI infrastructure costs is the most important news for operational SMBs in Latin America — and almost nobody is talking about it.
The latest generation of multimodal models — those that process text, audio, and images together — reached in 2024 a level of precision sufficient to extract structured data from chaotic inputs: an audio clip with background noise, a photo of a delivery receipt in difficult lighting, an imprecise verbal description of a technical problem. Today those inputs become structured data with over 95% accuracy. And that changes everything for field operations.
The problem for operational SMBs was never a lack of data. It was the inability to structure it. Every maintenance technician, every delivery driver, every safety inspector generates valuable information every day. But that information arrives in formats impossible to systematize: voice notes in WhatsApp groups, photos without context, messages that mix multiple topics.
Traditional solutions tried to solve this from the other direction: forcing workers to generate structured data at the source. Digital forms, apps with fixed fields, data entry portals. The result was always the same: low adoption, incomplete data, return to WhatsApp. They were attacking the wrong symptom. The problem wasn't the worker. It was asking them to do data analyst work while doing their actual job.
The correct solution reverses the flow. Instead of structuring the input, you structure the output. The worker describes what happened in the most natural way possible. The AI extracts the relevant information, infers context, fills in missing fields, and generates the record. It's the same thing a human analyst does when processing field team notes — but in real time and without interpretation error.
At Queiros, we built that AI infrastructure layer on top of the channels teams already use. A technician who finishes a job and sends an audio to the WhatsApp group is, without knowing it, filling a database. An inspector who takes a photo of an installation's condition is generating a geolocated, timestamped record. There's no additional friction because there's no new interface to learn.
The question I ask operational SMB owners isn't whether they'll adopt AI. It's when. The models already exist, the cost is accessible, and the opportunity to capture operational knowledge before it's lost doesn't wait. Companies that structure their operational knowledge in the next two years will operate in a different category from the rest.
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