Pecan AI Ltd. today announced the launch of its Predictive AI Agent, an agentic artificial intelligence system designed to help businesses forecast shifts in revenue, customer sentiment, demand and other metrics early enough to take action.
The new agent lets business teams plug raw, proprietary data into the company’s platform and ask questions directly. From there, it translates business intent into thoughtful foresight that teams can act on immediately.
“Predictions are probably one of the hallmarks of successful businesses,” Chief Executive Zohar Bronfman told SiliconANGLE in an interview. In addition to being an entrepreneur, he also holds dual Ph.D.s in cognitive neuroscience and technology philosophy, putting him at a crossroads between machines and humans.
Pecan’s mission is to make predictive modeling available to organizations that don’t have deep benches of machine learning engineers or data scientists. He explained that prediction is among the defining capabilities that makes or breaks business acumen in the modern era, but it has historically been limited to the subset that can build the infrastructure.
What’s launching today is Pecan’s “predictive agent,” an autonomous system that can interpret a company’s unique data structure, or “fingerprint,” by breaking down the predictive workflow into subtasks, building and validating a model, and delivering predictions.
“Each and every business has its own fingerprint, its own unique structure, and large language models can’t really make sense of it,” Bronfman said. “We employ an army of agents that basically go into your data and understand your data.”
He repeatedly emphasized that business data is unique and idiosyncratic. Every company has its own particularized “schema,” making it difficult for any given company to build its own predictive models on its own. That’s the gap that Pecan claims to fill with an agentic workflow that can adapt to a shifting business data reality.
Providing a concrete example, he contrasted “reactive” analysis by taking geographic and recent activity to decide which customers to retain using coupons. Current systems consider a number of signals — for example, looking at the data and discovering that people from Georgia and Tennessee have the highest likelihood to buy and not return, so a company could give them the coupons. The problem: Most are already gone and not coming back.
Instead, Bronfman said, agentic systems that can consider far more signals, up to 1,500 points, to forecast this churn before it happens, expanding the time window for intervention, providing companies the opportunity to change course before losing money.
Most of the market, he explained, predictive analysis is trapped in dashboards and reports. It doesn’t need to work like that.
The next wave of “AI agents,” Bronfman said, won’t just summarize business dashboards. The agents will do predictive work right out in the open like oracles. A user can say, “Here’s my data. I want to know my revenue next year,” and get predictions back.
“You don’t need to code,” he said. “You don’t have to do anything. The system does everything for you.”
Image: SiliconANGLE/Microsoft Designer
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