Today we are announcing our AI agent for voice data.

Companies are supposed to be living organisms. Their primary job is to evolve. They need to learn from their environment and become more efficient over time.

But right now most corporate organisms evolve incredibly slowly.

One reason is that so much of a company's signal is trapped in unstructured conversation. Customer calls, sales pitches, support tickets. This data contains the lessons the company needs to learn, like why customers leave or where the product breaks. But because it's unstructured, it's invisible to the systems that run the business.

The organism is trying to learn, but it has amnesia.

More structured data means faster evolution. When conversation becomes structured, enterprises can act on it. Post-call work gets automated. Pricing adjustments get informed by what customers are actually saying about competitors, not by hunches. Marketing budgets get allocated based on which objections are most common in which lead sources and personas. Voice data has enormous signal about all of these things and far more. The problem is that signal has been locked away.

For the last few years the industry has tried to unlock it with what I call human middleware.

We hire humans to write prompts and define categories and manually stitch together the context. We treat humans as the router between the messy world of voice and the clean world of databases.

This approach doesn't scale. We are three years into the LLM era and this data is still basically unstructured everywhere. The bottleneck is obvious. Humans can only process so many calls, define so many categories, anticipate so many edge cases. The result is that most companies are still flying blind on their richest source of customer signal.

At Voiceops we built systems for our clients to structure aspects of their voice data. Things like whether reps are correctly doing discovery, or how customers feel about a new product. We gave them tools to write prompts, test against real calls, refine their definitions. Over years of experimentation we kept refactoring to reduce human time. 50 hours to structure a new dataset became 20. 20 became 5. Each iteration removed another piece of what humans had to do.

The natural end of that process was realizing we didn't need the human at all. The voice data was sufficient for the AI to deduce everything about the business and come back with the logic on its own.

And as LLMs have developed stronger reasoning capabilities, we've found that the AI actually does a better job than humans at this work. It can look at tens of thousands of calls and understand the environment in a way no human team could match. It figures out the right data schema, the right definitions, the right edge cases. It catches nuances that would take a human weeks to discover.

One example. We built a model to detect military affiliation for an education company. A human would have spent hours defining what counts as military affiliation, then days discovering edge cases through trial and error. The agent figured out the schema by observing what was actually discussed in calls. It defined the categories. It surfaced the ambiguous cases that needed clarification, things like whether a stepbrother's service counts as affiliation. It did this in hours, and the output was more rigorous than what we had seen from human-led efforts.

You don't need to tell it what a churn risk sounds like. You don't need to sit down and define the parameters of a good sales pitch. You simply let it loose on your history.

It scans tens of thousands of interactions and discovers the scaffolding of your business on its own. It deduces your products. It identifies your risks. It hypothesizes the questions you should be asking but aren't.

When you open the dashboard a few hours later, the experience feels magical. The AI appears to learn your business so well it feels like a deep tenured employee on day 1. A VP of Sales we work with had been meaning to ask his product team about when certain offerings from a catalog of hundreds were supposed to be pitched. He never got around to it. The agent inferred the answer from the calls and surfaced it before he asked.

Structuring the data is just act one. Act two is operationalizing it and creating a flywheel that allows businesses to adapt and evolve faster. Removing the bottleneck of human middleware is the first step to making that flywheel possible.

This is how companies become self-learning.

Humans are still the ones steering the ship. But they are no longer down in the engine room shoveling coal and trying to make sense of the noise. They are finally free to drive.